AutoMM Detection - Quick Start on a Tiny COCO Format Dataset¶
In this section, our goal is to fast finetune a pretrained model on a small dataset in COCO format, and evaluate on its test set. Both training and test sets are in COCO format. See Convert Data to COCO Format for how to convert other datasets to COCO format.
Setting up the imports¶
Make sure mmcv and mmdet are installed:
#!pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 # To use object detection, downgrade the torch version if it's >=2.2
!mim install "mmcv==2.1.0" # For Google Colab, use the line below instead to install mmcv
#!pip install "mmcv==2.1.0" -f https://download.openmmlab.com/mmcv/dist/cu121/torch2.1.0/index.html
!pip install "mmdet==3.2.0"
Show code cell output
Looking in links: https://download.openmmlab.com/mmcv/dist/cu124/torch2.5.0/index.html
Requirement already satisfied: mmcv==2.1.0 in /home/ci/opt/venv/lib/python3.11/site-packages (2.1.0)
Requirement already satisfied: addict in /home/ci/opt/venv/lib/python3.11/site-packages (from mmcv==2.1.0) (2.4.0)
Requirement already satisfied: mmengine>=0.3.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from mmcv==2.1.0) (0.10.5)
Requirement already satisfied: numpy in /home/ci/opt/venv/lib/python3.11/site-packages (from mmcv==2.1.0) (1.26.4)
Requirement already satisfied: packaging in /home/ci/opt/venv/lib/python3.11/site-packages (from mmcv==2.1.0) (24.2)
Requirement already satisfied: Pillow in /home/ci/opt/venv/lib/python3.11/site-packages (from mmcv==2.1.0) (11.1.0)
Requirement already satisfied: pyyaml in /home/ci/opt/venv/lib/python3.11/site-packages (from mmcv==2.1.0) (6.0.2)
Requirement already satisfied: yapf in /home/ci/opt/venv/lib/python3.11/site-packages (from mmcv==2.1.0) (0.43.0)
Requirement already satisfied: matplotlib in /home/ci/opt/venv/lib/python3.11/site-packages (from mmengine>=0.3.0->mmcv==2.1.0) (3.10.0)
Requirement already satisfied: rich in /home/ci/opt/venv/lib/python3.11/site-packages (from mmengine>=0.3.0->mmcv==2.1.0) (13.9.4)
Requirement already satisfied: termcolor in /home/ci/opt/venv/lib/python3.11/site-packages (from mmengine>=0.3.0->mmcv==2.1.0) (2.5.0)
Requirement already satisfied: opencv-python>=3 in /home/ci/opt/venv/lib/python3.11/site-packages (from mmengine>=0.3.0->mmcv==2.1.0) (4.11.0.86)
Requirement already satisfied: platformdirs>=3.5.1 in /home/ci/opt/venv/lib/python3.11/site-packages (from yapf->mmcv==2.1.0) (4.3.6)
Requirement already satisfied: contourpy>=1.0.1 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib->mmengine>=0.3.0->mmcv==2.1.0) (1.3.1)
Requirement already satisfied: cycler>=0.10 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib->mmengine>=0.3.0->mmcv==2.1.0) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib->mmengine>=0.3.0->mmcv==2.1.0) (4.55.3)
Requirement already satisfied: kiwisolver>=1.3.1 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib->mmengine>=0.3.0->mmcv==2.1.0) (1.4.8)
Requirement already satisfied: pyparsing>=2.3.1 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib->mmengine>=0.3.0->mmcv==2.1.0) (3.2.1)
Requirement already satisfied: python-dateutil>=2.7 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib->mmengine>=0.3.0->mmcv==2.1.0) (2.9.0.post0)
Requirement already satisfied: markdown-it-py>=2.2.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from rich->mmengine>=0.3.0->mmcv==2.1.0) (3.0.0)
Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from rich->mmengine>=0.3.0->mmcv==2.1.0) (2.19.1)
Requirement already satisfied: mdurl~=0.1 in /home/ci/opt/venv/lib/python3.11/site-packages (from markdown-it-py>=2.2.0->rich->mmengine>=0.3.0->mmcv==2.1.0) (0.1.2)
Requirement already satisfied: six>=1.5 in /home/ci/opt/venv/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib->mmengine>=0.3.0->mmcv==2.1.0) (1.17.0)
Requirement already satisfied: mmdet==3.2.0 in /home/ci/opt/venv/lib/python3.11/site-packages (3.2.0)
Requirement already satisfied: matplotlib in /home/ci/opt/venv/lib/python3.11/site-packages (from mmdet==3.2.0) (3.10.0)
Requirement already satisfied: numpy in /home/ci/opt/venv/lib/python3.11/site-packages (from mmdet==3.2.0) (1.26.4)
Requirement already satisfied: pycocotools in /home/ci/opt/venv/lib/python3.11/site-packages (from mmdet==3.2.0) (2.0.8)
Requirement already satisfied: scipy in /home/ci/opt/venv/lib/python3.11/site-packages (from mmdet==3.2.0) (1.15.1)
Requirement already satisfied: shapely in /home/ci/opt/venv/lib/python3.11/site-packages (from mmdet==3.2.0) (2.0.6)
Requirement already satisfied: six in /home/ci/opt/venv/lib/python3.11/site-packages (from mmdet==3.2.0) (1.17.0)
Requirement already satisfied: terminaltables in /home/ci/opt/venv/lib/python3.11/site-packages (from mmdet==3.2.0) (3.1.10)
Requirement already satisfied: tqdm in /home/ci/opt/venv/lib/python3.11/site-packages (from mmdet==3.2.0) (4.67.1)
Requirement already satisfied: contourpy>=1.0.1 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib->mmdet==3.2.0) (1.3.1)
Requirement already satisfied: cycler>=0.10 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib->mmdet==3.2.0) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib->mmdet==3.2.0) (4.55.3)
Requirement already satisfied: kiwisolver>=1.3.1 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib->mmdet==3.2.0) (1.4.8)
Requirement already satisfied: packaging>=20.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib->mmdet==3.2.0) (24.2)
Requirement already satisfied: pillow>=8 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib->mmdet==3.2.0) (11.1.0)
Requirement already satisfied: pyparsing>=2.3.1 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib->mmdet==3.2.0) (3.2.1)
Requirement already satisfied: python-dateutil>=2.7 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib->mmdet==3.2.0) (2.9.0.post0)
To start, let’s import MultiModalPredictor:
from autogluon.multimodal import MultiModalPredictor
/home/ci/opt/venv/lib/python3.11/site-packages/mmengine/optim/optimizer/zero_optimizer.py:11: DeprecationWarning: `TorchScript` support for functional optimizers is deprecated and will be removed in a future PyTorch release. Consider using the `torch.compile` optimizer instead.
from torch.distributed.optim import \
And also import some other packages that will be used in this tutorial:
import os
import time
from autogluon.core.utils.loaders import load_zip
Downloading Data¶
We have the sample dataset ready in the cloud. Let’s download it:
zip_file = "https://automl-mm-bench.s3.amazonaws.com/object_detection_dataset/tiny_motorbike_coco.zip"
download_dir = "./tiny_motorbike_coco"
load_zip.unzip(zip_file, unzip_dir=download_dir)
data_dir = os.path.join(download_dir, "tiny_motorbike")
train_path = os.path.join(data_dir, "Annotations", "trainval_cocoformat.json")
test_path = os.path.join(data_dir, "Annotations", "test_cocoformat.json")
Downloading ./tiny_motorbike_coco/file.zip from https://automl-mm-bench.s3.amazonaws.com/object_detection_dataset/tiny_motorbike_coco.zip...
0%| | 0.00/21.8M [00:00<?, ?iB/s]
48%|████▊ | 10.6M/21.8M [00:00<00:00, 106MiB/s]
97%|█████████▋| 21.1M/21.8M [00:00<00:00, 93.4MiB/s]
100%|██████████| 21.8M/21.8M [00:00<00:00, 95.2MiB/s]
While using COCO format dataset, the input is the json annotation file of the dataset split.
In this example, trainval_cocoformat.json is the annotation file of the train-and-validate split,
and test_cocoformat.json is the annotation file of the test split.
Creating the MultiModalPredictor¶
We select the "medium_quality" presets, which uses a YOLOX-large model pretrained on COCO dataset. This preset is fast to finetune or inference,
and easy to deploy. We also provide presets "high_quality" with a DINO-Resnet50 model and "best quality" with a DINO-SwinL model, with much higher performance but also slower and with higher GPU memory usage.
presets = "medium_quality"
We create the MultiModalPredictor with selected presets.
We need to specify the problem_type to "object_detection",
and also provide a sample_data_path for the predictor to infer the catgories of the dataset.
Here we provide the train_path, and it also works using any other split of this dataset.
And we also provide a path to save the predictor.
It will be saved to a automatically generated directory with timestamp under AutogluonModels if path is not specified.
# Init predictor
import uuid
model_path = f"./tmp/{uuid.uuid4().hex}-quick_start_tutorial_temp_save"
predictor = MultiModalPredictor(
problem_type="object_detection",
sample_data_path=train_path,
presets=presets,
path=model_path,
)
Finetuning the Model¶
Learning rate, number of epochs, and batch_size are included in the presets, and thus no need to specify. Note that we use a two-stage learning rate option during finetuning by default, and the model head will have 100x learning rate. Using a two-stage learning rate with high learning rate only on head layers makes the model converge faster during finetuning. It usually gives better performance as well, especially on small datasets with hundreds or thousands of images. We also compute the time of the fit process here for better understanding the speed. We run it on a g4.2xlarge EC2 machine on AWS, and part of the command outputs are shown below:
start = time.time()
predictor.fit(train_path) # Fit
train_end = time.time()
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
Downloading yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth from https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth...
Loads checkpoint by local backend from path: yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth
The model and loaded state dict do not match exactly
size mismatch for bbox_head.multi_level_conv_cls.0.weight: copying a param with shape torch.Size([80, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([10, 256, 1, 1]).
size mismatch for bbox_head.multi_level_conv_cls.0.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([10]).
size mismatch for bbox_head.multi_level_conv_cls.1.weight: copying a param with shape torch.Size([80, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([10, 256, 1, 1]).
size mismatch for bbox_head.multi_level_conv_cls.1.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([10]).
size mismatch for bbox_head.multi_level_conv_cls.2.weight: copying a param with shape torch.Size([80, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([10, 256, 1, 1]).
size mismatch for bbox_head.multi_level_conv_cls.2.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([10]).
=================== System Info ===================
AutoGluon Version: 1.2.1b20250118
Python Version: 3.11.9
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP Tue Sep 24 10:00:37 UTC 2024
CPU Count: 8
Pytorch Version: 2.5.1+cu124
CUDA Version: 12.4
Memory Avail: 28.41 GB / 30.95 GB (91.8%)
Disk Space Avail: WARNING, an exception (FileNotFoundError) occurred while attempting to get available disk space. Consider opening a GitHub Issue.
===================================================
Using default root folder: ./tiny_motorbike_coco/tiny_motorbike/Annotations/... Specify `model.mmdet_image.coco_root=...` in hyperparameters if you think it is wrong.
AutoMM starts to create your model. ✨✨✨
To track the learning progress, you can open a terminal and launch Tensorboard:
```shell
# Assume you have installed tensorboard
tensorboard --logdir /home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/tmp/fa4334e064874824b651483d2cb44438-quick_start_tutorial_temp_save
```
Seed set to 0
0%| | 0.00/217M [00:00<?, ?iB/s]
0%| | 95.2k/217M [00:00<04:41, 772kiB/s]
0%| | 270k/217M [00:00<03:19, 1.09MiB/s]
0%| | 693k/217M [00:00<01:36, 2.25MiB/s]
0%| | 939k/217M [00:00<01:33, 2.31MiB/s]
1%| | 1.18M/217M [00:00<01:32, 2.34MiB/s]
1%| | 1.42M/217M [00:00<01:48, 2.00MiB/s]
1%| | 1.67M/217M [00:00<01:48, 2.00MiB/s]
1%| | 1.90M/217M [00:00<01:45, 2.05MiB/s]
1%| | 2.11M/217M [00:01<02:08, 1.68MiB/s]
1%| | 2.29M/217M [00:01<02:51, 1.25MiB/s]
1%| | 2.44M/217M [00:01<03:28, 1.03MiB/s]
1%| | 2.56M/217M [00:01<04:10, 855kiB/s]
1%| | 2.66M/217M [00:02<04:30, 792kiB/s]
1%|▏ | 2.75M/217M [00:02<05:07, 697kiB/s]
1%|▏ | 2.83M/217M [00:02<05:16, 679kiB/s]
1%|▏ | 2.90M/217M [00:02<05:44, 623kiB/s]
1%|▏ | 2.97M/217M [00:02<05:47, 617kiB/s]
1%|▏ | 3.03M/217M [00:02<05:53, 606kiB/s]
1%|▏ | 3.10M/217M [00:02<05:51, 609kiB/s]
1%|▏ | 3.18M/217M [00:02<06:34, 543kiB/s]
2%|▏ | 3.28M/217M [00:03<05:53, 606kiB/s]
2%|▏ | 3.34M/217M [00:03<06:13, 572kiB/s]
2%|▏ | 3.41M/217M [00:03<06:18, 566kiB/s]
2%|▏ | 3.47M/217M [00:03<06:21, 561kiB/s]
2%|▏ | 3.54M/217M [00:03<06:15, 569kiB/s]
2%|▏ | 3.61M/217M [00:03<06:14, 570kiB/s]
2%|▏ | 3.67M/217M [00:03<06:17, 567kiB/s]
2%|▏ | 3.73M/217M [00:03<06:36, 539kiB/s]
2%|▏ | 3.79M/217M [00:04<06:49, 521kiB/s]
2%|▏ | 3.84M/217M [00:04<08:13, 432kiB/s]
2%|▏ | 3.90M/217M [00:04<09:00, 395kiB/s]
2%|▏ | 3.94M/217M [00:04<09:03, 393kiB/s]
2%|▏ | 3.98M/217M [00:04<12:26, 286kiB/s]
2%|▏ | 4.02M/217M [00:05<15:09, 234kiB/s]
2%|▏ | 4.04M/217M [00:05<15:33, 228kiB/s]
2%|▏ | 4.07M/217M [00:05<19:57, 178kiB/s]
2%|▏ | 4.09M/217M [00:05<19:58, 178kiB/s]
2%|▏ | 4.11M/217M [00:05<22:41, 157kiB/s]
2%|▏ | 4.15M/217M [00:05<21:59, 161kiB/s]
2%|▏ | 4.17M/217M [00:06<19:11, 185kiB/s]
2%|▏ | 4.20M/217M [00:06<21:40, 164kiB/s]
2%|▏ | 4.21M/217M [00:06<24:12, 147kiB/s]
2%|▏ | 4.23M/217M [00:06<24:43, 144kiB/s]
2%|▏ | 4.25M/217M [00:06<25:35, 139kiB/s]
2%|▏ | 4.26M/217M [00:06<26:47, 132kiB/s]
2%|▏ | 4.28M/217M [00:06<26:02, 136kiB/s]
2%|▏ | 4.29M/217M [00:07<26:41, 133kiB/s]
2%|▏ | 4.33M/217M [00:07<24:10, 147kiB/s]
2%|▏ | 4.36M/217M [00:07<23:18, 152kiB/s]
2%|▏ | 4.39M/217M [00:07<20:58, 169kiB/s]
2%|▏ | 4.42M/217M [00:07<19:17, 184kiB/s]
2%|▏ | 4.46M/217M [00:07<17:33, 202kiB/s]
2%|▏ | 4.49M/217M [00:07<15:57, 222kiB/s]
2%|▏ | 4.52M/217M [00:08<14:38, 242kiB/s]
2%|▏ | 4.57M/217M [00:08<12:56, 274kiB/s]
2%|▏ | 4.62M/217M [00:08<11:28, 309kiB/s]
2%|▏ | 4.67M/217M [00:08<10:15, 345kiB/s]
2%|▏ | 4.72M/217M [00:08<10:02, 353kiB/s]
2%|▏ | 4.76M/217M [00:08<11:07, 319kiB/s]
2%|▏ | 4.80M/217M [00:08<11:17, 313kiB/s]
2%|▏ | 4.83M/217M [00:09<12:12, 290kiB/s]
2%|▏ | 4.87M/217M [00:09<12:15, 289kiB/s]
2%|▏ | 4.90M/217M [00:09<12:31, 283kiB/s]
2%|▏ | 4.93M/217M [00:09<12:08, 292kiB/s]
2%|▏ | 4.97M/217M [00:09<11:48, 300kiB/s]
2%|▏ | 5.01M/217M [00:09<11:06, 318kiB/s]
2%|▏ | 5.06M/217M [00:09<10:36, 333kiB/s]
2%|▏ | 5.11M/217M [00:09<10:00, 353kiB/s]
2%|▏ | 5.16M/217M [00:10<10:27, 338kiB/s]
2%|▏ | 5.21M/217M [00:10<10:26, 339kiB/s]
2%|▏ | 5.25M/217M [00:10<11:02, 320kiB/s]
2%|▏ | 5.28M/217M [00:10<12:09, 291kiB/s]
2%|▏ | 5.31M/217M [00:10<13:15, 266kiB/s]
2%|▏ | 5.34M/217M [00:10<13:17, 266kiB/s]
2%|▏ | 5.37M/217M [00:10<12:52, 274kiB/s]
2%|▏ | 5.41M/217M [00:10<12:36, 280kiB/s]
3%|▎ | 5.46M/217M [00:11<11:49, 299kiB/s]
3%|▎ | 5.51M/217M [00:11<11:04, 319kiB/s]
3%|▎ | 5.56M/217M [00:11<10:28, 337kiB/s]
3%|▎ | 5.60M/217M [00:11<09:50, 358kiB/s]
3%|▎ | 5.65M/217M [00:11<10:36, 332kiB/s]
3%|▎ | 5.72M/217M [00:11<09:28, 372kiB/s]
3%|▎ | 5.77M/217M [00:11<11:02, 319kiB/s]
3%|▎ | 5.82M/217M [00:12<10:47, 326kiB/s]
3%|▎ | 5.85M/217M [00:12<13:33, 260kiB/s]
3%|▎ | 5.88M/217M [00:12<14:21, 245kiB/s]
3%|▎ | 5.91M/217M [00:12<14:09, 249kiB/s]
3%|▎ | 5.94M/217M [00:12<16:49, 209kiB/s]
3%|▎ | 5.96M/217M [00:12<17:38, 200kiB/s]
3%|▎ | 5.99M/217M [00:13<17:47, 198kiB/s]
3%|▎ | 6.01M/217M [00:13<19:10, 184kiB/s]
3%|▎ | 6.05M/217M [00:13<18:41, 188kiB/s]
3%|▎ | 6.08M/217M [00:13<17:55, 196kiB/s]
3%|▎ | 6.11M/217M [00:13<17:03, 206kiB/s]
3%|▎ | 6.15M/217M [00:13<16:17, 216kiB/s]
3%|▎ | 6.18M/217M [00:13<15:05, 233kiB/s]
3%|▎ | 6.21M/217M [00:14<14:03, 250kiB/s]
3%|▎ | 6.26M/217M [00:14<12:29, 282kiB/s]
3%|▎ | 6.31M/217M [00:14<11:44, 299kiB/s]
3%|▎ | 6.36M/217M [00:14<10:46, 326kiB/s]
3%|▎ | 6.41M/217M [00:14<10:09, 346kiB/s]
3%|▎ | 6.46M/217M [00:14<09:32, 368kiB/s]
3%|▎ | 6.51M/217M [00:14<09:08, 384kiB/s]
3%|▎ | 6.55M/217M [00:14<08:44, 402kiB/s]
3%|▎ | 6.60M/217M [00:15<08:18, 422kiB/s]
3%|▎ | 6.67M/217M [00:15<07:48, 450kiB/s]
3%|▎ | 6.73M/217M [00:15<07:26, 471kiB/s]
3%|▎ | 6.78M/217M [00:15<08:20, 421kiB/s]
3%|▎ | 6.87M/217M [00:15<07:22, 476kiB/s]
3%|▎ | 6.92M/217M [00:15<07:44, 453kiB/s]
3%|▎ | 6.96M/217M [00:15<07:40, 457kiB/s]
3%|▎ | 7.01M/217M [00:15<08:32, 411kiB/s]
3%|▎ | 7.06M/217M [00:16<08:15, 425kiB/s]
3%|▎ | 7.11M/217M [00:16<09:36, 364kiB/s]
3%|▎ | 7.16M/217M [00:16<09:52, 354kiB/s]
3%|▎ | 7.20M/217M [00:16<10:20, 339kiB/s]
3%|▎ | 7.23M/217M [00:16<10:32, 332kiB/s]
3%|▎ | 7.27M/217M [00:16<10:49, 323kiB/s]
3%|▎ | 7.31M/217M [00:16<11:11, 313kiB/s]
3%|▎ | 7.36M/217M [00:17<10:41, 327kiB/s]
3%|▎ | 7.41M/217M [00:17<10:19, 339kiB/s]
3%|▎ | 7.46M/217M [00:17<09:46, 358kiB/s]
3%|▎ | 7.50M/217M [00:17<09:24, 372kiB/s]
3%|▎ | 7.55M/217M [00:17<08:55, 392kiB/s]
3%|▎ | 7.60M/217M [00:17<08:31, 410kiB/s]
4%|▎ | 7.67M/217M [00:17<08:01, 435kiB/s]
4%|▎ | 7.73M/217M [00:17<07:32, 463kiB/s]
4%|▎ | 7.80M/217M [00:17<07:03, 495kiB/s]
4%|▎ | 7.87M/217M [00:18<06:48, 513kiB/s]
4%|▎ | 7.93M/217M [00:18<07:21, 474kiB/s]
4%|▎ | 8.01M/217M [00:18<07:35, 459kiB/s]
4%|▎ | 8.06M/217M [00:18<07:31, 463kiB/s]
4%|▎ | 8.11M/217M [00:18<07:31, 463kiB/s]
4%|▍ | 8.16M/217M [00:18<08:14, 423kiB/s]
4%|▍ | 8.20M/217M [00:18<09:09, 381kiB/s]
4%|▍ | 8.24M/217M [00:19<09:10, 380kiB/s]
4%|▍ | 8.28M/217M [00:19<11:10, 312kiB/s]
4%|▍ | 8.31M/217M [00:19<11:21, 307kiB/s]
4%|▍ | 8.34M/217M [00:19<11:27, 304kiB/s]
4%|▍ | 8.38M/217M [00:19<11:21, 307kiB/s]
4%|▍ | 8.42M/217M [00:19<12:29, 279kiB/s]
4%|▍ | 8.47M/217M [00:19<11:35, 300kiB/s]
4%|▍ | 8.50M/217M [00:20<12:23, 281kiB/s]
4%|▍ | 8.54M/217M [00:20<12:36, 276kiB/s]
4%|▍ | 8.57M/217M [00:20<12:27, 279kiB/s]
4%|▍ | 8.60M/217M [00:20<12:03, 289kiB/s]
4%|▍ | 8.64M/217M [00:20<11:52, 293kiB/s]
4%|▍ | 8.68M/217M [00:20<11:09, 311kiB/s]
4%|▍ | 8.73M/217M [00:20<10:20, 336kiB/s]
4%|▍ | 8.78M/217M [00:20<11:18, 307kiB/s]
4%|▍ | 8.85M/217M [00:21<10:14, 339kiB/s]
4%|▍ | 8.88M/217M [00:21<10:17, 337kiB/s]
4%|▍ | 8.92M/217M [00:21<10:14, 339kiB/s]
4%|▍ | 8.95M/217M [00:21<10:36, 327kiB/s]
4%|▍ | 8.99M/217M [00:21<11:50, 293kiB/s]
4%|▍ | 9.02M/217M [00:21<12:34, 276kiB/s]
4%|▍ | 9.04M/217M [00:21<13:11, 263kiB/s]
4%|▍ | 9.08M/217M [00:21<12:51, 270kiB/s]
4%|▍ | 9.11M/217M [00:22<12:24, 280kiB/s]
4%|▍ | 9.16M/217M [00:22<11:33, 300kiB/s]
4%|▍ | 9.21M/217M [00:22<10:48, 321kiB/s]
4%|▍ | 9.26M/217M [00:22<10:11, 340kiB/s]
4%|▍ | 9.31M/217M [00:22<09:38, 360kiB/s]
4%|▍ | 9.36M/217M [00:22<09:00, 385kiB/s]
4%|▍ | 9.41M/217M [00:22<08:44, 397kiB/s]
4%|▍ | 9.47M/217M [00:23<10:46, 321kiB/s]
4%|▍ | 9.54M/217M [00:23<08:53, 390kiB/s]
4%|▍ | 9.59M/217M [00:23<08:58, 386kiB/s]
4%|▍ | 9.63M/217M [00:23<09:33, 362kiB/s]
4%|▍ | 9.67M/217M [00:23<11:16, 307kiB/s]
4%|▍ | 9.70M/217M [00:23<11:31, 300kiB/s]
4%|▍ | 9.73M/217M [00:23<11:21, 304kiB/s]
5%|▍ | 9.78M/217M [00:23<11:08, 310kiB/s]
5%|▍ | 9.83M/217M [00:24<10:47, 321kiB/s]
5%|▍ | 9.88M/217M [00:24<10:12, 339kiB/s]
5%|▍ | 9.93M/217M [00:24<09:30, 363kiB/s]
5%|▍ | 9.98M/217M [00:24<09:01, 383kiB/s]
5%|▍ | 10.0M/217M [00:24<08:39, 399kiB/s]
5%|▍ | 10.1M/217M [00:24<08:10, 423kiB/s]
5%|▍ | 10.1M/217M [00:24<07:37, 453kiB/s]
5%|▍ | 10.2M/217M [00:24<07:17, 474kiB/s]
5%|▍ | 10.3M/217M [00:25<06:57, 496kiB/s]
5%|▍ | 10.3M/217M [00:25<06:33, 526kiB/s]
5%|▍ | 10.4M/217M [00:25<06:24, 538kiB/s]
5%|▍ | 10.5M/217M [00:25<06:09, 560kiB/s]
5%|▍ | 10.5M/217M [00:25<05:57, 579kiB/s]
5%|▍ | 10.6M/217M [00:25<05:45, 598kiB/s]
5%|▍ | 10.7M/217M [00:25<05:29, 628kiB/s]
5%|▍ | 10.7M/217M [00:25<06:21, 541kiB/s]
5%|▍ | 10.8M/217M [00:26<05:28, 629kiB/s]
5%|▌ | 10.9M/217M [00:26<05:45, 598kiB/s]
5%|▌ | 11.0M/217M [00:26<06:19, 543kiB/s]
5%|▌ | 11.0M/217M [00:26<06:41, 513kiB/s]
5%|▌ | 11.1M/217M [00:26<07:02, 488kiB/s]
5%|▌ | 11.1M/217M [00:26<07:47, 441kiB/s]
5%|▌ | 11.2M/217M [00:26<09:25, 364kiB/s]
5%|▌ | 11.2M/217M [00:27<11:44, 292kiB/s]
5%|▌ | 11.3M/217M [00:27<14:04, 244kiB/s]
5%|▌ | 11.3M/217M [00:27<16:26, 209kiB/s]
5%|▌ | 11.3M/217M [00:27<17:37, 195kiB/s]
5%|▌ | 11.3M/217M [00:27<17:08, 200kiB/s]
5%|▌ | 11.4M/217M [00:27<15:37, 220kiB/s]
5%|▌ | 11.4M/217M [00:28<14:44, 233kiB/s]
5%|▌ | 11.4M/217M [00:28<13:43, 250kiB/s]
5%|▌ | 11.5M/217M [00:28<12:20, 278kiB/s]
5%|▌ | 11.5M/217M [00:28<11:14, 305kiB/s]
5%|▌ | 11.6M/217M [00:28<10:25, 329kiB/s]
5%|▌ | 11.6M/217M [00:28<10:57, 313kiB/s]
5%|▌ | 11.7M/217M [00:28<11:21, 302kiB/s]
5%|▌ | 11.7M/217M [00:29<12:55, 265kiB/s]
5%|▌ | 11.7M/217M [00:29<13:29, 254kiB/s]
5%|▌ | 11.8M/217M [00:29<14:26, 237kiB/s]
5%|▌ | 11.8M/217M [00:29<14:58, 229kiB/s]
5%|▌ | 11.8M/217M [00:29<16:35, 206kiB/s]
5%|▌ | 11.8M/217M [00:29<16:40, 205kiB/s]
5%|▌ | 11.9M/217M [00:29<15:53, 215kiB/s]
5%|▌ | 11.9M/217M [00:30<15:06, 227kiB/s]
5%|▌ | 11.9M/217M [00:30<14:18, 239kiB/s]
6%|▌ | 12.0M/217M [00:30<13:28, 254kiB/s]
6%|▌ | 12.0M/217M [00:30<12:48, 267kiB/s]
6%|▌ | 12.1M/217M [00:30<11:48, 290kiB/s]
6%|▌ | 12.1M/217M [00:30<10:58, 312kiB/s]
6%|▌ | 12.2M/217M [00:30<11:00, 311kiB/s]
6%|▌ | 12.2M/217M [00:30<11:29, 298kiB/s]
6%|▌ | 12.2M/217M [00:31<11:08, 307kiB/s]
6%|▌ | 12.3M/217M [00:31<11:12, 305kiB/s]
6%|▌ | 12.3M/217M [00:31<12:04, 283kiB/s]
6%|▌ | 12.3M/217M [00:31<11:44, 291kiB/s]
6%|▌ | 12.4M/217M [00:31<12:22, 276kiB/s]
6%|▌ | 12.4M/217M [00:31<12:41, 269kiB/s]
6%|▌ | 12.4M/217M [00:31<12:24, 275kiB/s]
6%|▌ | 12.5M/217M [00:31<12:18, 277kiB/s]
6%|▌ | 12.5M/217M [00:32<11:35, 294kiB/s]
6%|▌ | 12.6M/217M [00:32<11:19, 301kiB/s]
6%|▌ | 12.6M/217M [00:32<12:24, 275kiB/s]
6%|▌ | 12.7M/217M [00:32<10:49, 315kiB/s]
6%|▌ | 12.7M/217M [00:32<11:09, 305kiB/s]
6%|▌ | 12.7M/217M [00:32<11:23, 299kiB/s]
6%|▌ | 12.8M/217M [00:32<11:39, 293kiB/s]
6%|▌ | 12.8M/217M [00:33<12:15, 278kiB/s]
6%|▌ | 12.8M/217M [00:33<12:39, 269kiB/s]
6%|▌ | 12.9M/217M [00:33<12:24, 275kiB/s]
6%|▌ | 12.9M/217M [00:33<12:17, 277kiB/s]
6%|▌ | 12.9M/217M [00:33<12:26, 274kiB/s]
6%|▌ | 13.0M/217M [00:33<11:19, 301kiB/s]
6%|▌ | 13.0M/217M [00:33<09:50, 346kiB/s]
6%|▌ | 13.1M/217M [00:33<11:17, 301kiB/s]
6%|▌ | 13.1M/217M [00:34<10:39, 319kiB/s]
6%|▌ | 13.2M/217M [00:34<10:13, 333kiB/s]
6%|▌ | 13.2M/217M [00:34<11:31, 295kiB/s]
6%|▌ | 13.3M/217M [00:34<10:21, 328kiB/s]
6%|▌ | 13.3M/217M [00:34<10:40, 319kiB/s]
6%|▌ | 13.3M/217M [00:34<11:04, 307kiB/s]
6%|▌ | 13.4M/217M [00:34<12:37, 269kiB/s]
6%|▌ | 13.4M/217M [00:35<12:59, 262kiB/s]
6%|▌ | 13.4M/217M [00:35<14:42, 231kiB/s]
6%|▌ | 13.5M/217M [00:35<14:37, 232kiB/s]
6%|▌ | 13.5M/217M [00:35<17:40, 192kiB/s]
6%|▌ | 13.5M/217M [00:35<16:44, 203kiB/s]
6%|▌ | 13.6M/217M [00:35<16:27, 206kiB/s]
6%|▋ | 13.6M/217M [00:36<17:56, 189kiB/s]
6%|▋ | 13.6M/217M [00:36<15:07, 225kiB/s]
6%|▋ | 13.7M/217M [00:36<15:18, 222kiB/s]
6%|▋ | 13.7M/217M [00:36<15:21, 221kiB/s]
6%|▋ | 13.7M/217M [00:36<16:11, 210kiB/s]
6%|▋ | 13.7M/217M [00:36<15:37, 217kiB/s]
6%|▋ | 13.8M/217M [00:36<15:56, 213kiB/s]
6%|▋ | 13.8M/217M [00:37<17:47, 191kiB/s]
6%|▋ | 13.8M/217M [00:37<16:56, 200kiB/s]
6%|▋ | 13.8M/217M [00:37<17:10, 197kiB/s]
6%|▋ | 13.9M/217M [00:37<20:31, 165kiB/s]
6%|▋ | 13.9M/217M [00:37<19:54, 170kiB/s]
6%|▋ | 13.9M/217M [00:37<19:39, 172kiB/s]
6%|▋ | 13.9M/217M [00:37<19:21, 175kiB/s]
6%|▋ | 14.0M/217M [00:38<19:11, 177kiB/s]
6%|▋ | 14.0M/217M [00:38<19:51, 171kiB/s]
6%|▋ | 14.0M/217M [00:38<18:15, 185kiB/s]
6%|▋ | 14.1M/217M [00:38<16:40, 203kiB/s]
6%|▋ | 14.1M/217M [00:38<14:55, 227kiB/s]
7%|▋ | 14.1M/217M [00:38<14:02, 241kiB/s]
7%|▋ | 14.2M/217M [00:38<12:29, 271kiB/s]
7%|▋ | 14.2M/217M [00:39<11:15, 300kiB/s]
7%|▋ | 14.3M/217M [00:39<10:37, 319kiB/s]
7%|▋ | 14.3M/217M [00:39<09:51, 343kiB/s]
7%|▋ | 14.4M/217M [00:39<10:44, 315kiB/s]
7%|▋ | 14.4M/217M [00:39<09:11, 368kiB/s]
7%|▋ | 14.5M/217M [00:39<09:22, 361kiB/s]
7%|▋ | 14.5M/217M [00:39<09:26, 358kiB/s]
7%|▋ | 14.6M/217M [00:40<10:18, 328kiB/s]
7%|▋ | 14.6M/217M [00:40<10:28, 322kiB/s]
7%|▋ | 14.6M/217M [00:40<11:08, 303kiB/s]
7%|▋ | 14.7M/217M [00:40<11:24, 296kiB/s]
7%|▋ | 14.7M/217M [00:40<10:59, 307kiB/s]
7%|▋ | 14.8M/217M [00:40<10:35, 319kiB/s]
7%|▋ | 14.8M/217M [00:40<10:09, 332kiB/s]
7%|▋ | 14.9M/217M [00:40<09:30, 355kiB/s]
7%|▋ | 14.9M/217M [00:41<09:11, 367kiB/s]
7%|▋ | 15.0M/217M [00:41<08:36, 391kiB/s]
7%|▋ | 15.0M/217M [00:41<08:18, 405kiB/s]
7%|▋ | 15.1M/217M [00:41<07:43, 436kiB/s]
7%|▋ | 15.2M/217M [00:41<07:24, 455kiB/s]
7%|▋ | 15.2M/217M [00:41<07:28, 450kiB/s]
7%|▋ | 15.3M/217M [00:41<07:46, 433kiB/s]
7%|▋ | 15.3M/217M [00:41<07:58, 422kiB/s]
7%|▋ | 15.4M/217M [00:42<07:49, 430kiB/s]
7%|▋ | 15.4M/217M [00:42<07:40, 439kiB/s]
7%|▋ | 15.5M/217M [00:42<07:41, 438kiB/s]
7%|▋ | 15.5M/217M [00:42<07:55, 424kiB/s]
7%|▋ | 15.5M/217M [00:42<08:34, 392kiB/s]
7%|▋ | 15.6M/217M [00:42<08:52, 379kiB/s]
7%|▋ | 15.6M/217M [00:42<08:52, 379kiB/s]
7%|▋ | 15.7M/217M [00:42<08:49, 381kiB/s]
7%|▋ | 15.7M/217M [00:43<09:12, 365kiB/s]
7%|▋ | 15.8M/217M [00:43<11:06, 303kiB/s]
7%|▋ | 15.8M/217M [00:43<11:07, 302kiB/s]
7%|▋ | 15.9M/217M [00:43<12:11, 275kiB/s]
7%|▋ | 15.9M/217M [00:43<12:28, 269kiB/s]
7%|▋ | 15.9M/217M [00:43<12:32, 268kiB/s]
7%|▋ | 16.0M/217M [00:43<12:07, 277kiB/s]
7%|▋ | 16.0M/217M [00:44<12:01, 279kiB/s]
7%|▋ | 16.0M/217M [00:44<11:18, 297kiB/s]
7%|▋ | 16.1M/217M [00:44<12:25, 270kiB/s]
7%|▋ | 16.1M/217M [00:44<10:45, 312kiB/s]
7%|▋ | 16.2M/217M [00:44<10:51, 309kiB/s]
7%|▋ | 16.2M/217M [00:44<11:11, 300kiB/s]
7%|▋ | 16.3M/217M [00:44<10:58, 305kiB/s]
8%|▊ | 16.3M/217M [00:45<10:18, 325kiB/s]
8%|▊ | 16.4M/217M [00:45<10:11, 329kiB/s]
8%|▊ | 16.4M/217M [00:45<09:51, 340kiB/s]
8%|▊ | 16.5M/217M [00:45<09:14, 362kiB/s]
8%|▊ | 16.5M/217M [00:45<08:49, 380kiB/s]
8%|▊ | 16.5M/217M [00:45<08:30, 393kiB/s]
8%|▊ | 16.6M/217M [00:45<08:07, 412kiB/s]
8%|▊ | 16.7M/217M [00:45<07:37, 439kiB/s]
8%|▊ | 16.7M/217M [00:46<08:12, 407kiB/s]
8%|▊ | 16.8M/217M [00:46<07:33, 442kiB/s]
8%|▊ | 16.8M/217M [00:46<07:51, 425kiB/s]
8%|▊ | 16.9M/217M [00:46<08:13, 406kiB/s]
8%|▊ | 16.9M/217M [00:46<08:48, 379kiB/s]
8%|▊ | 17.0M/217M [00:46<09:56, 336kiB/s]
8%|▊ | 17.0M/217M [00:46<12:08, 275kiB/s]
8%|▊ | 17.0M/217M [00:47<12:25, 269kiB/s]
8%|▊ | 17.1M/217M [00:47<12:14, 272kiB/s]
8%|▊ | 17.1M/217M [00:47<12:01, 277kiB/s]
8%|▊ | 17.1M/217M [00:47<11:39, 286kiB/s]
8%|▊ | 17.2M/217M [00:47<10:43, 311kiB/s]
8%|▊ | 17.2M/217M [00:47<10:10, 328kiB/s]
8%|▊ | 17.3M/217M [00:47<11:12, 297kiB/s]
8%|▊ | 17.3M/217M [00:47<09:39, 345kiB/s]
8%|▊ | 17.4M/217M [00:48<09:36, 347kiB/s]
8%|▊ | 17.4M/217M [00:48<09:38, 346kiB/s]
8%|▊ | 17.4M/217M [00:48<10:10, 327kiB/s]
8%|▊ | 17.5M/217M [00:48<09:55, 336kiB/s]
8%|▊ | 17.5M/217M [00:48<10:26, 319kiB/s]
8%|▊ | 17.6M/217M [00:48<10:55, 304kiB/s]
8%|▊ | 17.6M/217M [00:48<11:10, 298kiB/s]
8%|▊ | 17.6M/217M [00:48<11:17, 295kiB/s]
8%|▊ | 17.7M/217M [00:49<11:00, 302kiB/s]
8%|▊ | 17.7M/217M [00:49<10:45, 309kiB/s]
8%|▊ | 17.8M/217M [00:49<10:12, 326kiB/s]
8%|▊ | 17.8M/217M [00:49<09:38, 345kiB/s]
8%|▊ | 17.9M/217M [00:49<09:01, 368kiB/s]
8%|▊ | 17.9M/217M [00:49<08:45, 379kiB/s]
8%|▊ | 17.9M/217M [00:49<09:22, 354kiB/s]
8%|▊ | 18.0M/217M [00:49<08:37, 385kiB/s]
8%|▊ | 18.1M/217M [00:50<08:42, 381kiB/s]
8%|▊ | 18.1M/217M [00:50<08:50, 376kiB/s]
8%|▊ | 18.2M/217M [00:50<08:42, 381kiB/s]
8%|▊ | 18.2M/217M [00:50<08:26, 393kiB/s]
8%|▊ | 18.3M/217M [00:50<08:18, 399kiB/s]
8%|▊ | 18.3M/217M [00:50<08:03, 412kiB/s]
8%|▊ | 18.4M/217M [00:50<07:44, 428kiB/s]
8%|▊ | 18.4M/217M [00:50<07:34, 438kiB/s]
8%|▊ | 18.4M/217M [00:51<07:50, 422kiB/s]
9%|▊ | 18.5M/217M [00:51<08:13, 403kiB/s]
9%|▊ | 18.5M/217M [00:51<08:27, 391kiB/s]
9%|▊ | 18.6M/217M [00:51<08:17, 400kiB/s]
9%|▊ | 18.6M/217M [00:51<08:34, 386kiB/s]
9%|▊ | 18.7M/217M [00:51<08:41, 381kiB/s]
9%|▊ | 18.7M/217M [00:51<09:31, 347kiB/s]
9%|▊ | 18.8M/217M [00:51<09:27, 350kiB/s]
9%|▊ | 18.8M/217M [00:52<09:31, 347kiB/s]
9%|▊ | 18.8M/217M [00:52<09:46, 338kiB/s]
9%|▊ | 18.9M/217M [00:52<10:09, 326kiB/s]
9%|▊ | 18.9M/217M [00:52<13:36, 243kiB/s]
9%|▊ | 18.9M/217M [00:52<18:12, 182kiB/s]
9%|▊ | 19.0M/217M [00:52<20:26, 162kiB/s]
9%|▊ | 19.0M/217M [00:53<21:13, 156kiB/s]
9%|▊ | 19.0M/217M [00:53<21:26, 154kiB/s]
9%|▉ | 19.0M/217M [00:53<21:01, 157kiB/s]
9%|▉ | 19.1M/217M [00:53<20:26, 162kiB/s]
9%|▉ | 19.1M/217M [00:53<20:10, 164kiB/s]
9%|▉ | 19.1M/217M [00:53<22:22, 148kiB/s]
9%|▉ | 19.1M/217M [00:53<18:15, 181kiB/s]
9%|▉ | 19.2M/217M [00:54<18:29, 179kiB/s]
9%|▉ | 19.2M/217M [00:54<17:49, 185kiB/s]
9%|▉ | 19.2M/217M [00:54<16:42, 198kiB/s]
9%|▉ | 19.3M/217M [00:54<16:59, 194kiB/s]
9%|▉ | 19.3M/217M [00:54<15:50, 208kiB/s]
9%|▉ | 19.3M/217M [00:54<15:55, 207kiB/s]
9%|▉ | 19.4M/217M [00:55<17:50, 185kiB/s]
9%|▉ | 19.4M/217M [00:55<16:31, 200kiB/s]
9%|▉ | 19.4M/217M [00:55<17:12, 192kiB/s]
9%|▉ | 19.4M/217M [00:55<17:02, 194kiB/s]
9%|▉ | 19.5M/217M [00:55<18:03, 183kiB/s]
9%|▉ | 19.5M/217M [00:55<17:03, 193kiB/s]
9%|▉ | 19.5M/217M [00:56<17:20, 190kiB/s]
9%|▉ | 19.6M/217M [00:56<17:55, 184kiB/s]
9%|▉ | 19.6M/217M [00:56<18:46, 176kiB/s]
9%|▉ | 19.6M/217M [00:56<19:28, 169kiB/s]
9%|▉ | 19.6M/217M [00:56<18:41, 176kiB/s]
9%|▉ | 19.7M/217M [00:56<17:07, 192kiB/s]
9%|▉ | 19.7M/217M [00:56<15:49, 208kiB/s]
9%|▉ | 19.7M/217M [00:57<17:14, 191kiB/s]
9%|▉ | 19.8M/217M [00:57<14:05, 234kiB/s]
9%|▉ | 19.8M/217M [00:57<15:49, 208kiB/s]
9%|▉ | 19.8M/217M [00:57<16:54, 195kiB/s]
9%|▉ | 19.9M/217M [00:57<16:55, 194kiB/s]
9%|▉ | 19.9M/217M [00:57<16:34, 198kiB/s]
9%|▉ | 19.9M/217M [00:58<15:28, 213kiB/s]
9%|▉ | 20.0M/217M [00:58<14:42, 223kiB/s]
9%|▉ | 20.0M/217M [00:58<13:35, 242kiB/s]
9%|▉ | 20.0M/217M [00:58<12:44, 258kiB/s]
9%|▉ | 20.1M/217M [00:58<11:24, 288kiB/s]
9%|▉ | 20.1M/217M [00:58<10:28, 314kiB/s]
9%|▉ | 20.2M/217M [00:58<09:45, 337kiB/s]
9%|▉ | 20.2M/217M [00:58<10:04, 326kiB/s]
9%|▉ | 20.3M/217M [00:59<09:11, 357kiB/s]
9%|▉ | 20.3M/217M [00:59<10:30, 312kiB/s]
9%|▉ | 20.4M/217M [00:59<10:26, 314kiB/s]
9%|▉ | 20.4M/217M [00:59<11:22, 289kiB/s]
9%|▉ | 20.4M/217M [00:59<11:38, 282kiB/s]
9%|▉ | 20.5M/217M [00:59<11:36, 283kiB/s]
9%|▉ | 20.5M/217M [00:59<11:00, 298kiB/s]
9%|▉ | 20.5M/217M [01:00<10:49, 303kiB/s]
9%|▉ | 20.6M/217M [01:00<10:13, 320kiB/s]
10%|▉ | 20.6M/217M [01:00<10:48, 303kiB/s]
10%|▉ | 20.7M/217M [01:00<09:58, 328kiB/s]
10%|▉ | 20.7M/217M [01:00<10:05, 324kiB/s]
10%|▉ | 20.8M/217M [01:00<10:25, 314kiB/s]
10%|▉ | 20.8M/217M [01:00<10:25, 314kiB/s]
10%|▉ | 20.8M/217M [01:00<11:24, 287kiB/s]
10%|▉ | 20.9M/217M [01:01<10:32, 310kiB/s]
10%|▉ | 20.9M/217M [01:01<10:46, 304kiB/s]
10%|▉ | 21.0M/217M [01:01<11:09, 293kiB/s]
10%|▉ | 21.0M/217M [01:01<10:54, 300kiB/s]
10%|▉ | 21.0M/217M [01:01<11:00, 297kiB/s]
10%|▉ | 21.1M/217M [01:01<10:14, 319kiB/s]
10%|▉ | 21.1M/217M [01:01<11:12, 292kiB/s]
10%|▉ | 21.2M/217M [01:02<10:01, 326kiB/s]
10%|▉ | 21.2M/217M [01:02<10:16, 318kiB/s]
10%|▉ | 21.2M/217M [01:02<11:01, 296kiB/s]
10%|▉ | 21.3M/217M [01:02<12:30, 261kiB/s]
10%|▉ | 21.3M/217M [01:02<13:51, 236kiB/s]
10%|▉ | 21.3M/217M [01:02<14:33, 224kiB/s]
10%|▉ | 21.4M/217M [01:02<15:10, 215kiB/s]
10%|▉ | 21.4M/217M [01:03<16:20, 200kiB/s]
10%|▉ | 21.4M/217M [01:03<18:19, 178kiB/s]
10%|▉ | 21.5M/217M [01:03<19:00, 172kiB/s]
10%|▉ | 21.5M/217M [01:03<19:40, 166kiB/s]
10%|▉ | 21.5M/217M [01:03<20:29, 159kiB/s]
10%|▉ | 21.5M/217M [01:03<20:50, 157kiB/s]
10%|▉ | 21.5M/217M [01:03<21:03, 155kiB/s]
10%|▉ | 21.5M/217M [01:04<20:16, 161kiB/s]
10%|▉ | 21.6M/217M [01:04<18:02, 181kiB/s]
10%|▉ | 21.6M/217M [01:04<17:46, 184kiB/s]
10%|▉ | 21.6M/217M [01:04<16:24, 199kiB/s]
10%|▉ | 21.7M/217M [01:04<15:51, 206kiB/s]
10%|▉ | 21.7M/217M [01:04<15:47, 207kiB/s]
10%|█ | 21.7M/217M [01:04<14:50, 220kiB/s]
10%|█ | 21.8M/217M [01:05<14:18, 228kiB/s]
10%|█ | 21.8M/217M [01:05<14:23, 226kiB/s]
10%|█ | 21.8M/217M [01:05<13:56, 234kiB/s]
10%|█ | 21.9M/217M [01:05<13:37, 239kiB/s]
10%|█ | 21.9M/217M [01:05<15:50, 206kiB/s]
10%|█ | 21.9M/217M [01:05<19:19, 169kiB/s]
10%|█ | 21.9M/217M [01:06<24:41, 132kiB/s]
10%|█ | 22.0M/217M [01:06<27:28, 118kiB/s]
10%|█ | 22.0M/217M [01:06<29:04, 112kiB/s]
10%|█ | 22.0M/217M [01:06<29:22, 111kiB/s]
10%|█ | 22.0M/217M [01:06<28:16, 115kiB/s]
10%|█ | 22.0M/217M [01:06<26:59, 121kiB/s]
10%|█ | 22.0M/217M [01:07<26:46, 122kiB/s]
10%|█ | 22.1M/217M [01:07<26:09, 124kiB/s]
10%|█ | 22.1M/217M [01:07<25:00, 130kiB/s]
10%|█ | 22.1M/217M [01:07<23:52, 136kiB/s]
10%|█ | 22.1M/217M [01:07<21:25, 152kiB/s]
10%|█ | 22.2M/217M [01:07<18:34, 175kiB/s]
10%|█ | 22.2M/217M [01:07<16:54, 192kiB/s]
10%|█ | 22.2M/217M [01:07<14:48, 220kiB/s]
10%|█ | 22.3M/217M [01:08<13:37, 239kiB/s]
10%|█ | 22.3M/217M [01:08<12:45, 255kiB/s]
10%|█ | 22.3M/217M [01:08<11:34, 281kiB/s]
10%|█ | 22.4M/217M [01:08<10:29, 310kiB/s]
10%|█ | 22.4M/217M [01:08<10:00, 325kiB/s]
10%|█ | 22.5M/217M [01:08<09:26, 344kiB/s]
10%|█ | 22.5M/217M [01:08<08:54, 364kiB/s]
10%|█ | 22.6M/217M [01:08<08:22, 387kiB/s]
10%|█ | 22.6M/217M [01:09<07:47, 417kiB/s]
10%|█ | 22.7M/217M [01:09<07:21, 441kiB/s]
10%|█ | 22.8M/217M [01:09<06:52, 472kiB/s]
11%|█ | 22.8M/217M [01:09<06:33, 494kiB/s]
11%|█ | 22.9M/217M [01:09<06:37, 489kiB/s]
11%|█ | 23.0M/217M [01:09<06:55, 468kiB/s]
11%|█ | 23.0M/217M [01:09<07:35, 427kiB/s]
11%|█ | 23.1M/217M [01:09<07:43, 419kiB/s]
11%|█ | 23.1M/217M [01:10<07:55, 409kiB/s]
11%|█ | 23.1M/217M [01:10<10:01, 323kiB/s]
11%|█ | 23.2M/217M [01:10<12:45, 254kiB/s]
11%|█ | 23.2M/217M [01:10<14:11, 228kiB/s]
11%|█ | 23.2M/217M [01:10<14:32, 222kiB/s]
11%|█ | 23.3M/217M [01:10<14:26, 224kiB/s]
11%|█ | 23.3M/217M [01:11<13:52, 233kiB/s]
11%|█ | 23.3M/217M [01:11<12:54, 250kiB/s]
11%|█ | 23.4M/217M [01:11<12:11, 265kiB/s]
11%|█ | 23.4M/217M [01:11<11:30, 281kiB/s]
11%|█ | 23.4M/217M [01:11<10:38, 304kiB/s]
11%|█ | 23.5M/217M [01:11<09:52, 327kiB/s]
11%|█ | 23.5M/217M [01:11<10:23, 311kiB/s]
11%|█ | 23.6M/217M [01:11<09:29, 340kiB/s]
11%|█ | 23.6M/217M [01:12<08:54, 362kiB/s]
11%|█ | 23.7M/217M [01:12<08:51, 364kiB/s]
11%|█ | 23.7M/217M [01:12<09:52, 327kiB/s]
11%|█ | 23.8M/217M [01:12<09:41, 333kiB/s]
11%|█ | 23.8M/217M [01:12<09:27, 341kiB/s]
11%|█ | 23.9M/217M [01:12<09:02, 357kiB/s]
11%|█ | 23.9M/217M [01:12<08:36, 374kiB/s]
11%|█ | 24.0M/217M [01:12<08:11, 393kiB/s]
11%|█ | 24.0M/217M [01:13<07:53, 408kiB/s]
11%|█ | 24.1M/217M [01:13<07:27, 432kiB/s]
11%|█ | 24.1M/217M [01:13<07:03, 456kiB/s]
11%|█ | 24.2M/217M [01:13<06:40, 483kiB/s]
11%|█ | 24.3M/217M [01:13<06:24, 502kiB/s]
11%|█ | 24.3M/217M [01:13<06:08, 524kiB/s]
11%|█ | 24.4M/217M [01:13<05:51, 548kiB/s]
11%|█▏ | 24.5M/217M [01:13<05:48, 554kiB/s]
11%|█▏ | 24.5M/217M [01:14<06:06, 526kiB/s]
11%|█▏ | 24.6M/217M [01:14<06:15, 514kiB/s]
11%|█▏ | 24.6M/217M [01:14<06:41, 480kiB/s]
11%|█▏ | 24.7M/217M [01:14<07:27, 431kiB/s]
11%|█▏ | 24.7M/217M [01:14<06:59, 459kiB/s]
11%|█▏ | 24.8M/217M [01:14<08:25, 381kiB/s]
11%|█▏ | 24.8M/217M [01:14<08:00, 401kiB/s]
11%|█▏ | 24.9M/217M [01:15<08:42, 368kiB/s]
11%|█▏ | 24.9M/217M [01:15<08:39, 371kiB/s]
11%|█▏ | 25.0M/217M [01:15<08:35, 373kiB/s]
12%|█▏ | 25.0M/217M [01:15<09:16, 346kiB/s]
12%|█▏ | 25.1M/217M [01:15<09:15, 346kiB/s]
12%|█▏ | 25.1M/217M [01:15<08:54, 360kiB/s]
12%|█▏ | 25.2M/217M [01:15<08:27, 378kiB/s]
12%|█▏ | 25.2M/217M [01:15<08:00, 400kiB/s]
12%|█▏ | 25.2M/217M [01:15<07:46, 412kiB/s]
12%|█▏ | 25.3M/217M [01:16<07:24, 432kiB/s]
12%|█▏ | 25.4M/217M [01:16<06:55, 462kiB/s]
12%|█▏ | 25.4M/217M [01:16<06:30, 491kiB/s]
12%|█▏ | 25.5M/217M [01:16<06:21, 503kiB/s]
12%|█▏ | 25.6M/217M [01:16<06:05, 525kiB/s]
12%|█▏ | 25.6M/217M [01:16<05:47, 551kiB/s]
12%|█▏ | 25.7M/217M [01:16<05:34, 573kiB/s]
12%|█▏ | 25.7M/217M [01:16<05:57, 536kiB/s]
12%|█▏ | 25.8M/217M [01:16<06:04, 526kiB/s]
12%|█▏ | 25.9M/217M [01:17<06:12, 515kiB/s]
12%|█▏ | 25.9M/217M [01:17<06:23, 499kiB/s]
12%|█▏ | 26.0M/217M [01:17<06:17, 507kiB/s]
12%|█▏ | 26.1M/217M [01:17<06:04, 524kiB/s]
12%|█▏ | 26.1M/217M [01:17<06:37, 481kiB/s]
12%|█▏ | 26.2M/217M [01:17<06:27, 494kiB/s]
12%|█▏ | 26.2M/217M [01:17<06:36, 481kiB/s]
12%|█▏ | 26.3M/217M [01:17<07:36, 419kiB/s]
12%|█▏ | 26.3M/217M [01:18<08:16, 385kiB/s]
12%|█▏ | 26.4M/217M [01:18<08:16, 384kiB/s]
12%|█▏ | 26.4M/217M [01:18<08:56, 356kiB/s]
12%|█▏ | 26.4M/217M [01:18<08:55, 356kiB/s]
12%|█▏ | 26.5M/217M [01:18<08:50, 360kiB/s]
12%|█▏ | 26.5M/217M [01:18<09:38, 330kiB/s]
12%|█▏ | 26.6M/217M [01:18<08:54, 357kiB/s]
12%|█▏ | 26.6M/217M [01:18<09:02, 352kiB/s]
12%|█▏ | 26.7M/217M [01:19<08:36, 369kiB/s]
12%|█▏ | 26.7M/217M [01:19<09:31, 334kiB/s]
12%|█▏ | 26.7M/217M [01:19<10:28, 303kiB/s]
12%|█▏ | 26.8M/217M [01:19<11:03, 287kiB/s]
12%|█▏ | 26.8M/217M [01:19<11:54, 267kiB/s]
12%|█▏ | 26.8M/217M [01:19<11:55, 266kiB/s]
12%|█▏ | 26.8M/217M [01:19<14:14, 223kiB/s]
12%|█▏ | 26.9M/217M [01:20<15:31, 204kiB/s]
12%|█▏ | 26.9M/217M [01:20<14:43, 215kiB/s]
12%|█▏ | 26.9M/217M [01:20<15:18, 207kiB/s]
12%|█▏ | 27.0M/217M [01:20<15:01, 211kiB/s]
12%|█▏ | 27.0M/217M [01:20<14:38, 216kiB/s]
12%|█▏ | 27.0M/217M [01:20<14:29, 219kiB/s]
12%|█▏ | 27.1M/217M [01:20<13:46, 230kiB/s]
12%|█▏ | 27.1M/217M [01:21<13:14, 239kiB/s]
12%|█▏ | 27.1M/217M [01:21<12:16, 258kiB/s]
13%|█▎ | 27.2M/217M [01:21<11:32, 274kiB/s]
13%|█▎ | 27.2M/217M [01:21<12:03, 263kiB/s]
13%|█▎ | 27.2M/217M [01:21<11:29, 276kiB/s]
13%|█▎ | 27.3M/217M [01:21<11:39, 272kiB/s]
13%|█▎ | 27.3M/217M [01:21<12:25, 255kiB/s]
13%|█▎ | 27.3M/217M [01:21<13:42, 231kiB/s]
13%|█▎ | 27.4M/217M [01:22<14:07, 224kiB/s]
13%|█▎ | 27.4M/217M [01:22<13:58, 226kiB/s]
13%|█▎ | 27.4M/217M [01:22<13:22, 237kiB/s]
13%|█▎ | 27.5M/217M [01:22<12:58, 244kiB/s]
13%|█▎ | 27.5M/217M [01:22<12:07, 261kiB/s]
13%|█▎ | 27.5M/217M [01:22<11:45, 269kiB/s]
13%|█▎ | 27.6M/217M [01:22<10:37, 298kiB/s]
13%|█▎ | 27.6M/217M [01:22<11:05, 285kiB/s]
13%|█▎ | 27.7M/217M [01:23<10:56, 289kiB/s]
13%|█▎ | 27.7M/217M [01:23<10:44, 294kiB/s]
13%|█▎ | 27.7M/217M [01:23<10:38, 297kiB/s]
13%|█▎ | 27.8M/217M [01:23<10:41, 296kiB/s]
13%|█▎ | 27.8M/217M [01:23<12:19, 256kiB/s]
13%|█▎ | 27.8M/217M [01:23<13:45, 229kiB/s]
13%|█▎ | 27.9M/217M [01:24<14:40, 215kiB/s]
13%|█▎ | 27.9M/217M [01:24<16:35, 190kiB/s]
13%|█▎ | 27.9M/217M [01:24<18:12, 173kiB/s]
13%|█▎ | 27.9M/217M [01:24<18:52, 167kiB/s]
13%|█▎ | 27.9M/217M [01:24<18:51, 167kiB/s]
13%|█▎ | 28.0M/217M [01:24<19:01, 166kiB/s]
13%|█▎ | 28.0M/217M [01:24<18:54, 167kiB/s]
13%|█▎ | 28.0M/217M [01:25<17:33, 180kiB/s]
13%|█▎ | 28.1M/217M [01:25<15:40, 201kiB/s]
13%|█▎ | 28.1M/217M [01:25<14:26, 218kiB/s]
13%|█▎ | 28.1M/217M [01:25<15:27, 204kiB/s]
13%|█▎ | 28.1M/217M [01:25<13:35, 232kiB/s]
13%|█▎ | 28.2M/217M [01:25<15:22, 205kiB/s]
13%|█▎ | 28.2M/217M [01:25<14:45, 214kiB/s]
13%|█▎ | 28.2M/217M [01:26<15:18, 206kiB/s]
13%|█▎ | 28.3M/217M [01:26<15:06, 208kiB/s]
13%|█▎ | 28.3M/217M [01:26<16:56, 186kiB/s]
13%|█▎ | 28.3M/217M [01:26<16:08, 195kiB/s]
13%|█▎ | 28.4M/217M [01:26<16:44, 188kiB/s]
13%|█▎ | 28.4M/217M [01:26<16:25, 192kiB/s]
13%|█▎ | 28.4M/217M [01:27<16:00, 197kiB/s]
13%|█▎ | 28.5M/217M [01:27<14:50, 212kiB/s]
13%|█▎ | 28.5M/217M [01:27<13:46, 228kiB/s]
13%|█▎ | 28.5M/217M [01:27<15:01, 209kiB/s]
13%|█▎ | 28.6M/217M [01:27<14:51, 212kiB/s]
13%|█▎ | 28.6M/217M [01:27<13:51, 227kiB/s]
13%|█▎ | 28.6M/217M [01:27<15:30, 203kiB/s]
13%|█▎ | 28.6M/217M [01:28<18:43, 168kiB/s]
13%|█▎ | 28.7M/217M [01:28<19:16, 163kiB/s]
13%|█▎ | 28.7M/217M [01:28<19:22, 162kiB/s]
13%|█▎ | 28.7M/217M [01:28<19:08, 164kiB/s]
13%|█▎ | 28.7M/217M [01:28<18:59, 166kiB/s]
13%|█▎ | 28.8M/217M [01:28<17:28, 180kiB/s]
13%|█▎ | 28.8M/217M [01:28<15:43, 200kiB/s]
13%|█▎ | 28.8M/217M [01:29<14:31, 216kiB/s]
13%|█▎ | 28.9M/217M [01:29<13:21, 235kiB/s]
13%|█▎ | 28.9M/217M [01:29<12:12, 257kiB/s]
13%|█▎ | 28.9M/217M [01:29<11:25, 275kiB/s]
13%|█▎ | 29.0M/217M [01:29<12:15, 256kiB/s]
13%|█▎ | 29.0M/217M [01:29<10:22, 302kiB/s]
13%|█▎ | 29.0M/217M [01:29<10:33, 297kiB/s]
13%|█▎ | 29.1M/217M [01:29<10:41, 293kiB/s]
13%|█▎ | 29.1M/217M [01:30<10:34, 297kiB/s]
13%|█▎ | 29.1M/217M [01:30<10:43, 292kiB/s]
13%|█▎ | 29.2M/217M [01:30<11:58, 262kiB/s]
13%|█▎ | 29.2M/217M [01:30<12:24, 253kiB/s]
13%|█▎ | 29.2M/217M [01:30<12:33, 250kiB/s]
13%|█▎ | 29.3M/217M [01:30<12:19, 254kiB/s]
13%|█▎ | 29.3M/217M [01:30<12:35, 249kiB/s]
14%|█▎ | 29.3M/217M [01:30<13:06, 239kiB/s]
14%|█▎ | 29.4M/217M [01:31<13:09, 238kiB/s]
14%|█▎ | 29.4M/217M [01:31<13:12, 237kiB/s]
14%|█▎ | 29.4M/217M [01:31<12:34, 249kiB/s]
14%|█▎ | 29.5M/217M [01:31<12:04, 259kiB/s]
14%|█▎ | 29.5M/217M [01:31<11:22, 275kiB/s]
14%|█▎ | 29.5M/217M [01:31<10:53, 287kiB/s]
14%|█▎ | 29.6M/217M [01:31<09:58, 314kiB/s]
14%|█▎ | 29.6M/217M [01:31<09:16, 337kiB/s]
14%|█▎ | 29.7M/217M [01:32<08:48, 355kiB/s]
14%|█▎ | 29.7M/217M [01:32<08:18, 376kiB/s]
14%|█▎ | 29.8M/217M [01:32<07:49, 399kiB/s]
14%|█▎ | 29.8M/217M [01:32<07:26, 420kiB/s]
14%|█▍ | 29.9M/217M [01:32<06:57, 449kiB/s]
14%|█▍ | 30.0M/217M [01:32<06:42, 466kiB/s]
14%|█▍ | 30.0M/217M [01:32<06:48, 458kiB/s]
14%|█▍ | 30.1M/217M [01:32<06:56, 449kiB/s]
14%|█▍ | 30.1M/217M [01:33<07:13, 432kiB/s]
14%|█▍ | 30.2M/217M [01:33<07:11, 433kiB/s]
14%|█▍ | 30.2M/217M [01:33<08:29, 367kiB/s]
14%|█▍ | 30.3M/217M [01:33<07:20, 425kiB/s]
14%|█▍ | 30.3M/217M [01:33<07:52, 395kiB/s]
14%|█▍ | 30.4M/217M [01:33<08:04, 386kiB/s]
14%|█▍ | 30.4M/217M [01:33<08:44, 356kiB/s]
14%|█▍ | 30.5M/217M [01:34<08:53, 350kiB/s]
14%|█▍ | 30.5M/217M [01:34<09:05, 342kiB/s]
14%|█▍ | 30.5M/217M [01:34<09:11, 338kiB/s]
14%|█▍ | 30.6M/217M [01:34<09:11, 338kiB/s]
14%|█▍ | 30.6M/217M [01:34<09:17, 335kiB/s]
14%|█▍ | 30.7M/217M [01:34<09:56, 313kiB/s]
14%|█▍ | 30.7M/217M [01:34<10:52, 286kiB/s]
14%|█▍ | 30.8M/217M [01:34<09:34, 325kiB/s]
14%|█▍ | 30.8M/217M [01:35<09:47, 318kiB/s]
14%|█▍ | 30.8M/217M [01:35<09:59, 311kiB/s]
14%|█▍ | 30.9M/217M [01:35<10:02, 310kiB/s]
14%|█▍ | 30.9M/217M [01:35<09:54, 314kiB/s]
14%|█▍ | 30.9M/217M [01:35<10:35, 293kiB/s]
14%|█▍ | 31.0M/217M [01:35<10:12, 304kiB/s]
14%|█▍ | 31.0M/217M [01:35<10:24, 299kiB/s]
14%|█▍ | 31.0M/217M [01:35<11:59, 259kiB/s]
14%|█▍ | 31.1M/217M [01:36<11:21, 273kiB/s]
14%|█▍ | 31.1M/217M [01:36<12:01, 258kiB/s]
14%|█▍ | 31.1M/217M [01:36<12:21, 251kiB/s]
14%|█▍ | 31.2M/217M [01:36<12:11, 254kiB/s]
14%|█▍ | 31.2M/217M [01:36<11:44, 264kiB/s]
14%|█▍ | 31.2M/217M [01:36<11:20, 274kiB/s]
14%|█▍ | 31.3M/217M [01:36<10:48, 287kiB/s]
14%|█▍ | 31.3M/217M [01:36<10:06, 306kiB/s]
14%|█▍ | 31.4M/217M [01:37<09:26, 328kiB/s]
14%|█▍ | 31.4M/217M [01:37<10:06, 306kiB/s]
14%|█▍ | 31.5M/217M [01:37<09:34, 323kiB/s]
14%|█▍ | 31.5M/217M [01:37<09:47, 316kiB/s]
15%|█▍ | 31.5M/217M [01:37<10:47, 287kiB/s]
15%|█▍ | 31.6M/217M [01:37<10:16, 301kiB/s]
15%|█▍ | 31.6M/217M [01:37<11:06, 279kiB/s]
15%|█▍ | 31.6M/217M [01:37<11:00, 281kiB/s]
15%|█▍ | 31.7M/217M [01:38<11:58, 258kiB/s]
15%|█▍ | 31.7M/217M [01:38<12:02, 257kiB/s]
15%|█▍ | 31.7M/217M [01:38<12:28, 248kiB/s]
15%|█▍ | 31.8M/217M [01:38<12:47, 242kiB/s]
15%|█▍ | 31.8M/217M [01:38<12:30, 247kiB/s]
15%|█▍ | 31.8M/217M [01:38<11:54, 260kiB/s]
15%|█▍ | 31.9M/217M [01:38<11:25, 271kiB/s]
15%|█▍ | 31.9M/217M [01:39<11:48, 262kiB/s]
15%|█▍ | 31.9M/217M [01:39<12:00, 257kiB/s]
15%|█▍ | 32.0M/217M [01:39<12:17, 251kiB/s]
15%|█▍ | 32.0M/217M [01:39<12:17, 251kiB/s]
15%|█▍ | 32.0M/217M [01:39<11:52, 260kiB/s]
15%|█▍ | 32.1M/217M [01:39<11:19, 273kiB/s]
15%|█▍ | 32.1M/217M [01:39<11:44, 263kiB/s]
15%|█▍ | 32.1M/217M [01:39<12:26, 248kiB/s]
15%|█▍ | 32.2M/217M [01:40<14:05, 219kiB/s]
15%|█▍ | 32.2M/217M [01:40<14:11, 218kiB/s]
15%|█▍ | 32.2M/217M [01:40<14:47, 208kiB/s]
15%|█▍ | 32.3M/217M [01:40<14:46, 209kiB/s]
15%|█▍ | 32.3M/217M [01:40<14:21, 215kiB/s]
15%|█▍ | 32.3M/217M [01:40<13:33, 227kiB/s]
15%|█▍ | 32.4M/217M [01:40<13:01, 237kiB/s]
15%|█▍ | 32.4M/217M [01:41<12:30, 246kiB/s]
15%|█▍ | 32.4M/217M [01:41<11:18, 273kiB/s]
15%|█▍ | 32.5M/217M [01:41<11:02, 279kiB/s]
15%|█▍ | 32.5M/217M [01:41<11:43, 263kiB/s]
15%|█▍ | 32.5M/217M [01:41<11:46, 262kiB/s]
15%|█▍ | 32.6M/217M [01:41<14:57, 206kiB/s]
15%|█▍ | 32.6M/217M [01:41<15:41, 196kiB/s]
15%|█▌ | 32.6M/217M [01:42<15:46, 195kiB/s]
15%|█▌ | 32.6M/217M [01:42<19:51, 155kiB/s]
15%|█▌ | 32.7M/217M [01:42<19:04, 161kiB/s]
15%|█▌ | 32.7M/217M [01:42<19:12, 160kiB/s]
15%|█▌ | 32.7M/217M [01:42<17:00, 181kiB/s]
15%|█▌ | 32.8M/217M [01:42<16:50, 183kiB/s]
15%|█▌ | 32.8M/217M [01:43<16:16, 189kiB/s]
15%|█▌ | 32.8M/217M [01:43<15:19, 201kiB/s]
15%|█▌ | 32.9M/217M [01:43<14:19, 214kiB/s]
15%|█▌ | 32.9M/217M [01:43<13:22, 230kiB/s]
15%|█▌ | 32.9M/217M [01:43<12:28, 246kiB/s]
15%|█▌ | 32.9M/217M [01:43<11:40, 263kiB/s]
15%|█▌ | 33.0M/217M [01:43<10:33, 291kiB/s]
15%|█▌ | 33.0M/217M [01:44<11:00, 279kiB/s]
15%|█▌ | 33.1M/217M [01:44<09:55, 310kiB/s]
15%|█▌ | 33.1M/217M [01:44<10:06, 303kiB/s]
15%|█▌ | 33.2M/217M [01:44<09:55, 309kiB/s]
15%|█▌ | 33.2M/217M [01:44<09:59, 307kiB/s]
15%|█▌ | 33.2M/217M [01:44<10:25, 294kiB/s]
15%|█▌ | 33.3M/217M [01:44<12:24, 247kiB/s]
15%|█▌ | 33.3M/217M [01:45<13:21, 230kiB/s]
15%|█▌ | 33.3M/217M [01:45<13:48, 222kiB/s]
15%|█▌ | 33.4M/217M [01:45<13:58, 219kiB/s]
15%|█▌ | 33.4M/217M [01:45<13:33, 226kiB/s]
15%|█▌ | 33.4M/217M [01:45<12:43, 241kiB/s]
15%|█▌ | 33.5M/217M [01:45<12:01, 255kiB/s]
15%|█▌ | 33.5M/217M [01:45<11:27, 267kiB/s]
15%|█▌ | 33.6M/217M [01:45<10:33, 290kiB/s]
15%|█▌ | 33.6M/217M [01:46<09:52, 310kiB/s]
15%|█▌ | 33.6M/217M [01:46<10:20, 296kiB/s]
15%|█▌ | 33.7M/217M [01:46<10:55, 280kiB/s]
16%|█▌ | 33.7M/217M [01:46<12:36, 243kiB/s]
16%|█▌ | 33.7M/217M [01:46<12:53, 237kiB/s]
16%|█▌ | 33.8M/217M [01:46<17:30, 175kiB/s]
16%|█▌ | 33.8M/217M [01:47<17:20, 176kiB/s]
16%|█▌ | 33.8M/217M [01:47<17:37, 174kiB/s]
16%|█▌ | 33.8M/217M [01:47<21:40, 141kiB/s]
16%|█▌ | 33.8M/217M [01:47<23:28, 130kiB/s]
16%|█▌ | 33.8M/217M [01:47<25:20, 121kiB/s]
16%|█▌ | 33.9M/217M [01:47<26:38, 115kiB/s]
16%|█▌ | 33.9M/217M [01:47<27:38, 111kiB/s]
16%|█▌ | 33.9M/217M [01:48<25:55, 118kiB/s]
16%|█▌ | 33.9M/217M [01:48<20:06, 152kiB/s]
16%|█▌ | 33.9M/217M [01:48<20:39, 148kiB/s]
16%|█▌ | 34.0M/217M [01:48<17:54, 171kiB/s]
16%|█▌ | 34.0M/217M [01:48<15:44, 194kiB/s]
16%|█▌ | 34.0M/217M [01:48<14:23, 212kiB/s]
16%|█▌ | 34.1M/217M [01:48<12:23, 247kiB/s]
16%|█▌ | 34.1M/217M [01:49<12:59, 235kiB/s]
16%|█▌ | 34.1M/217M [01:49<11:57, 255kiB/s]
16%|█▌ | 34.2M/217M [01:49<11:50, 258kiB/s]
16%|█▌ | 34.2M/217M [01:49<11:44, 260kiB/s]
16%|█▌ | 34.2M/217M [01:49<11:08, 274kiB/s]
16%|█▌ | 34.3M/217M [01:49<11:07, 274kiB/s]
16%|█▌ | 34.3M/217M [01:49<11:45, 259kiB/s]
16%|█▌ | 34.4M/217M [01:49<10:39, 286kiB/s]
16%|█▌ | 34.4M/217M [01:50<10:58, 278kiB/s]
16%|█▌ | 34.4M/217M [01:50<12:25, 245kiB/s]
16%|█▌ | 34.5M/217M [01:50<12:24, 245kiB/s]
16%|█▌ | 34.5M/217M [01:50<13:26, 227kiB/s]
16%|█▌ | 34.5M/217M [01:50<14:00, 217kiB/s]
16%|█▌ | 34.6M/217M [01:50<14:14, 214kiB/s]
16%|█▌ | 34.6M/217M [01:51<19:32, 156kiB/s]
16%|█▌ | 34.6M/217M [01:51<19:22, 157kiB/s]
16%|█▌ | 34.6M/217M [01:51<18:58, 160kiB/s]
16%|█▌ | 34.7M/217M [01:51<16:51, 181kiB/s]
16%|█▌ | 34.7M/217M [01:51<15:42, 194kiB/s]
16%|█▌ | 34.7M/217M [01:51<14:16, 213kiB/s]
16%|█▌ | 34.8M/217M [01:51<13:00, 234kiB/s]
16%|█▌ | 34.8M/217M [01:52<13:20, 228kiB/s]
16%|█▌ | 34.8M/217M [01:52<14:41, 207kiB/s]
16%|█▌ | 34.9M/217M [01:52<13:49, 220kiB/s]
16%|█▌ | 34.9M/217M [01:52<14:37, 208kiB/s]
16%|█▌ | 34.9M/217M [01:52<15:04, 202kiB/s]
16%|█▌ | 35.0M/217M [01:52<15:53, 191kiB/s]
16%|█▌ | 35.0M/217M [01:53<15:44, 193kiB/s]
16%|█▌ | 35.0M/217M [01:53<15:44, 193kiB/s]
16%|█▌ | 35.0M/217M [01:53<15:28, 196kiB/s]
16%|█▌ | 35.1M/217M [01:53<15:10, 200kiB/s]
16%|█▌ | 35.1M/217M [01:53<15:40, 194kiB/s]
16%|█▌ | 35.1M/217M [01:53<15:40, 194kiB/s]
16%|█▌ | 35.2M/217M [01:54<15:27, 196kiB/s]
16%|█▌ | 35.2M/217M [01:54<16:27, 184kiB/s]
16%|█▌ | 35.2M/217M [01:54<16:13, 187kiB/s]
16%|█▌ | 35.3M/217M [01:54<16:09, 188kiB/s]
16%|█▌ | 35.3M/217M [01:54<15:39, 194kiB/s]
16%|█▋ | 35.3M/217M [01:54<14:45, 206kiB/s]
16%|█▋ | 35.4M/217M [01:54<13:43, 221kiB/s]
16%|█▋ | 35.4M/217M [01:55<12:35, 241kiB/s]
16%|█▋ | 35.4M/217M [01:55<11:52, 255kiB/s]
16%|█▋ | 35.5M/217M [01:55<11:11, 271kiB/s]
16%|█▋ | 35.5M/217M [01:55<10:08, 299kiB/s]
16%|█▋ | 35.6M/217M [01:55<09:22, 323kiB/s]
16%|█▋ | 35.6M/217M [01:55<10:17, 294kiB/s]
16%|█▋ | 35.7M/217M [01:55<09:02, 335kiB/s]
16%|█▋ | 35.7M/217M [01:56<09:09, 330kiB/s]
16%|█▋ | 35.8M/217M [01:56<09:07, 332kiB/s]
16%|█▋ | 35.8M/217M [01:56<08:57, 338kiB/s]
16%|█▋ | 35.8M/217M [01:56<09:26, 320kiB/s]
17%|█▋ | 35.9M/217M [01:56<09:29, 319kiB/s]
17%|█▋ | 35.9M/217M [01:56<09:42, 312kiB/s]
17%|█▋ | 35.9M/217M [01:56<10:09, 297kiB/s]
17%|█▋ | 36.0M/217M [01:56<10:18, 293kiB/s]
17%|█▋ | 36.0M/217M [01:56<11:02, 274kiB/s]
17%|█▋ | 36.0M/217M [01:57<11:11, 270kiB/s]
17%|█▋ | 36.1M/217M [01:57<11:34, 261kiB/s]
17%|█▋ | 36.1M/217M [01:57<11:38, 259kiB/s]
17%|█▋ | 36.1M/217M [01:57<11:35, 261kiB/s]
17%|█▋ | 36.2M/217M [01:57<11:18, 267kiB/s]
17%|█▋ | 36.2M/217M [01:57<10:57, 275kiB/s]
17%|█▋ | 36.2M/217M [01:57<10:07, 298kiB/s]
17%|█▋ | 36.3M/217M [01:57<09:32, 316kiB/s]
17%|█▋ | 36.3M/217M [01:58<09:53, 305kiB/s]
17%|█▋ | 36.4M/217M [01:58<09:39, 312kiB/s]
17%|█▋ | 36.4M/217M [01:58<10:06, 298kiB/s]
17%|█▋ | 36.4M/217M [01:58<10:05, 299kiB/s]
17%|█▋ | 36.5M/217M [01:58<10:03, 300kiB/s]
17%|█▋ | 36.5M/217M [01:58<09:39, 312kiB/s]
17%|█▋ | 36.6M/217M [01:58<10:52, 277kiB/s]
17%|█▋ | 36.6M/217M [01:59<09:30, 317kiB/s]
17%|█▋ | 36.6M/217M [01:59<10:05, 299kiB/s]
17%|█▋ | 36.7M/217M [01:59<10:15, 293kiB/s]
17%|█▋ | 36.7M/217M [01:59<11:13, 268kiB/s]
17%|█▋ | 36.7M/217M [01:59<11:28, 262kiB/s]
17%|█▋ | 36.8M/217M [01:59<11:25, 263kiB/s]
17%|█▋ | 36.8M/217M [01:59<14:25, 209kiB/s]
17%|█▋ | 36.8M/217M [01:59<14:12, 212kiB/s]
17%|█▋ | 36.8M/217M [02:00<15:35, 193kiB/s]
17%|█▋ | 36.9M/217M [02:00<17:53, 168kiB/s]
17%|█▋ | 36.9M/217M [02:00<15:09, 198kiB/s]
17%|█▋ | 36.9M/217M [02:00<14:59, 201kiB/s]
17%|█▋ | 36.9M/217M [02:00<15:58, 188kiB/s]
17%|█▋ | 37.0M/217M [02:00<15:33, 193kiB/s]
17%|█▋ | 37.0M/217M [02:01<14:41, 205kiB/s]
17%|█▋ | 37.0M/217M [02:01<13:38, 220kiB/s]
17%|█▋ | 37.1M/217M [02:01<12:43, 236kiB/s]
17%|█▋ | 37.1M/217M [02:01<11:44, 256kiB/s]
17%|█▋ | 37.1M/217M [02:01<11:10, 269kiB/s]
17%|█▋ | 37.2M/217M [02:01<12:06, 248kiB/s]
17%|█▋ | 37.2M/217M [02:01<10:19, 291kiB/s]
17%|█▋ | 37.3M/217M [02:01<10:21, 290kiB/s]
17%|█▋ | 37.3M/217M [02:01<10:12, 294kiB/s]
17%|█▋ | 37.3M/217M [02:02<10:45, 279kiB/s]
17%|█▋ | 37.4M/217M [02:02<11:37, 258kiB/s]
17%|█▋ | 37.4M/217M [02:02<11:59, 250kiB/s]
17%|█▋ | 37.4M/217M [02:02<12:03, 249kiB/s]
17%|█▋ | 37.5M/217M [02:02<11:36, 258kiB/s]
17%|█▋ | 37.5M/217M [02:02<11:12, 267kiB/s]
17%|█▋ | 37.5M/217M [02:02<10:44, 279kiB/s]
17%|█▋ | 37.6M/217M [02:03<11:45, 255kiB/s]
17%|█▋ | 37.6M/217M [02:03<11:20, 264kiB/s]
17%|█▋ | 37.6M/217M [02:03<12:55, 232kiB/s]
17%|█▋ | 37.7M/217M [02:03<14:02, 213kiB/s]
17%|█▋ | 37.7M/217M [02:03<14:08, 212kiB/s]
17%|█▋ | 37.7M/217M [02:03<13:46, 217kiB/s]
17%|█▋ | 37.8M/217M [02:04<14:49, 202kiB/s]
17%|█▋ | 37.8M/217M [02:04<13:52, 216kiB/s]
17%|█▋ | 37.8M/217M [02:04<14:49, 202kiB/s]
17%|█▋ | 37.9M/217M [02:04<15:01, 199kiB/s]
17%|█▋ | 37.9M/217M [02:04<15:11, 197kiB/s]
17%|█▋ | 37.9M/217M [02:04<15:56, 187kiB/s]
17%|█▋ | 37.9M/217M [02:05<16:50, 177kiB/s]
17%|█▋ | 38.0M/217M [02:05<15:32, 192kiB/s]
17%|█▋ | 38.0M/217M [02:05<15:38, 191kiB/s]
18%|█▊ | 38.0M/217M [02:05<15:38, 191kiB/s]
18%|█▊ | 38.1M/217M [02:05<14:25, 207kiB/s]
18%|█▊ | 38.1M/217M [02:05<13:53, 215kiB/s]
18%|█▊ | 38.1M/217M [02:05<12:42, 235kiB/s]
18%|█▊ | 38.2M/217M [02:05<11:54, 251kiB/s]
18%|█▊ | 38.2M/217M [02:06<12:50, 233kiB/s]
18%|█▊ | 38.3M/217M [02:06<15:38, 191kiB/s]
18%|█▊ | 38.3M/217M [02:06<13:27, 222kiB/s]
18%|█▊ | 38.3M/217M [02:06<13:28, 221kiB/s]
18%|█▊ | 38.4M/217M [02:06<14:58, 199kiB/s]
18%|█▊ | 38.4M/217M [02:07<14:43, 202kiB/s]
18%|█▊ | 38.4M/217M [02:07<15:09, 197kiB/s]
18%|█▊ | 38.5M/217M [02:07<14:13, 209kiB/s]
18%|█▊ | 38.5M/217M [02:07<14:16, 209kiB/s]
18%|█▊ | 38.5M/217M [02:07<13:50, 215kiB/s]
18%|█▊ | 38.6M/217M [02:07<13:13, 225kiB/s]
18%|█▊ | 38.6M/217M [02:07<12:50, 232kiB/s]
18%|█▊ | 38.6M/217M [02:08<13:55, 214kiB/s]
18%|█▊ | 38.7M/217M [02:08<12:30, 238kiB/s]
18%|█▊ | 38.7M/217M [02:08<12:14, 243kiB/s]
18%|█▊ | 38.7M/217M [02:08<12:07, 246kiB/s]
18%|█▊ | 38.8M/217M [02:08<11:39, 255kiB/s]
18%|█▊ | 38.8M/217M [02:08<12:07, 245kiB/s]
18%|█▊ | 38.8M/217M [02:08<12:42, 234kiB/s]
18%|█▊ | 38.9M/217M [02:09<12:43, 234kiB/s]
18%|█▊ | 38.9M/217M [02:09<12:17, 242kiB/s]
18%|█▊ | 38.9M/217M [02:09<12:00, 248kiB/s]
18%|█▊ | 39.0M/217M [02:09<11:19, 262kiB/s]
18%|█▊ | 39.0M/217M [02:09<10:42, 277kiB/s]
18%|█▊ | 39.0M/217M [02:09<12:08, 245kiB/s]
18%|█▊ | 39.1M/217M [02:09<10:01, 296kiB/s]
18%|█▊ | 39.1M/217M [02:09<10:07, 293kiB/s]
18%|█▊ | 39.1M/217M [02:10<09:58, 298kiB/s]
18%|█▊ | 39.2M/217M [02:10<09:54, 299kiB/s]
18%|█▊ | 39.2M/217M [02:10<09:40, 307kiB/s]
18%|█▊ | 39.3M/217M [02:10<09:06, 326kiB/s]
18%|█▊ | 39.3M/217M [02:10<08:39, 343kiB/s]
18%|█▊ | 39.3M/217M [02:10<09:11, 322kiB/s]
18%|█▊ | 39.4M/217M [02:10<09:39, 307kiB/s]
18%|█▊ | 39.4M/217M [02:10<09:56, 298kiB/s]
18%|█▊ | 39.4M/217M [02:11<09:53, 299kiB/s]
18%|█▊ | 39.5M/217M [02:11<09:52, 300kiB/s]
18%|█▊ | 39.5M/217M [02:11<09:29, 312kiB/s]
18%|█▊ | 39.6M/217M [02:11<09:38, 307kiB/s]
18%|█▊ | 39.6M/217M [02:11<09:54, 299kiB/s]
18%|█▊ | 39.6M/217M [02:11<09:54, 299kiB/s]
18%|█▊ | 39.6M/217M [02:11<11:38, 254kiB/s]
18%|█▊ | 39.7M/217M [02:12<16:32, 179kiB/s]
18%|█▊ | 39.7M/217M [02:12<16:35, 178kiB/s]
18%|█▊ | 39.7M/217M [02:12<16:19, 181kiB/s]
18%|█▊ | 39.7M/217M [02:12<18:38, 159kiB/s]
18%|█▊ | 39.8M/217M [02:12<17:35, 168kiB/s]
18%|█▊ | 39.8M/217M [02:12<15:51, 187kiB/s]
18%|█▊ | 39.8M/217M [02:12<14:15, 207kiB/s]
18%|█▊ | 39.9M/217M [02:12<12:57, 228kiB/s]
18%|█▊ | 39.9M/217M [02:13<11:55, 248kiB/s]
18%|█▊ | 39.9M/217M [02:13<12:21, 239kiB/s]
18%|█▊ | 40.0M/217M [02:13<11:21, 260kiB/s]
18%|█▊ | 40.0M/217M [02:13<12:42, 233kiB/s]
18%|█▊ | 40.0M/217M [02:13<12:27, 237kiB/s]
18%|█▊ | 40.1M/217M [02:13<14:17, 207kiB/s]
18%|█▊ | 40.1M/217M [02:14<17:05, 173kiB/s]
18%|█▊ | 40.1M/217M [02:14<16:56, 174kiB/s]
18%|█▊ | 40.1M/217M [02:14<18:38, 158kiB/s]
18%|█▊ | 40.1M/217M [02:14<19:54, 148kiB/s]
18%|█▊ | 40.2M/217M [02:14<23:19, 127kiB/s]
18%|█▊ | 40.2M/217M [02:14<25:32, 116kiB/s]
18%|█▊ | 40.2M/217M [02:15<26:07, 113kiB/s]
19%|█▊ | 40.2M/217M [02:15<24:27, 121kiB/s]
19%|█▊ | 40.2M/217M [02:15<23:09, 127kiB/s]
19%|█▊ | 40.3M/217M [02:15<19:49, 149kiB/s]
19%|█▊ | 40.3M/217M [02:15<17:54, 165kiB/s]
19%|█▊ | 40.3M/217M [02:15<18:41, 158kiB/s]
19%|█▊ | 40.3M/217M [02:15<18:54, 156kiB/s]
19%|█▊ | 40.4M/217M [02:16<18:57, 155kiB/s]
19%|█▊ | 40.4M/217M [02:16<18:56, 156kiB/s]
19%|█▊ | 40.4M/217M [02:16<17:43, 166kiB/s]
19%|█▊ | 40.4M/217M [02:16<18:00, 164kiB/s]
19%|█▊ | 40.5M/217M [02:16<18:28, 160kiB/s]
19%|█▊ | 40.5M/217M [02:16<18:52, 156kiB/s]
19%|█▊ | 40.5M/217M [02:16<18:14, 162kiB/s]
19%|█▊ | 40.5M/217M [02:17<16:59, 173kiB/s]
19%|█▊ | 40.6M/217M [02:17<15:37, 189kiB/s]
19%|█▊ | 40.6M/217M [02:17<14:13, 207kiB/s]
19%|█▊ | 40.6M/217M [02:17<13:19, 221kiB/s]
19%|█▊ | 40.7M/217M [02:17<12:17, 240kiB/s]
19%|█▊ | 40.7M/217M [02:17<11:56, 247kiB/s]
19%|█▊ | 40.7M/217M [02:17<12:28, 236kiB/s]
19%|█▉ | 40.8M/217M [02:18<12:44, 231kiB/s]
19%|█▉ | 40.8M/217M [02:18<12:15, 240kiB/s]
19%|█▉ | 40.8M/217M [02:18<11:43, 251kiB/s]
19%|█▉ | 40.9M/217M [02:18<11:23, 258kiB/s]
19%|█▉ | 40.9M/217M [02:18<10:29, 280kiB/s]
19%|█▉ | 41.0M/217M [02:18<09:52, 298kiB/s]
19%|█▉ | 41.0M/217M [02:18<09:20, 315kiB/s]
19%|█▉ | 41.1M/217M [02:18<08:38, 340kiB/s]
19%|█▉ | 41.1M/217M [02:19<08:08, 361kiB/s]
19%|█▉ | 41.2M/217M [02:19<08:01, 366kiB/s]
19%|█▉ | 41.2M/217M [02:19<08:11, 358kiB/s]
19%|█▉ | 41.2M/217M [02:19<08:32, 344kiB/s]
19%|█▉ | 41.3M/217M [02:19<09:16, 316kiB/s]
19%|█▉ | 41.3M/217M [02:19<09:51, 298kiB/s]
19%|█▉ | 41.3M/217M [02:19<10:29, 279kiB/s]
19%|█▉ | 41.4M/217M [02:19<11:03, 265kiB/s]
19%|█▉ | 41.4M/217M [02:20<09:28, 309kiB/s]
19%|█▉ | 41.4M/217M [02:20<09:21, 313kiB/s]
19%|█▉ | 41.5M/217M [02:20<10:03, 291kiB/s]
19%|█▉ | 41.5M/217M [02:20<09:46, 300kiB/s]
19%|█▉ | 41.6M/217M [02:20<09:26, 310kiB/s]
19%|█▉ | 41.6M/217M [02:20<09:54, 296kiB/s]
19%|█▉ | 41.6M/217M [02:20<10:04, 291kiB/s]
19%|█▉ | 41.6M/217M [02:20<09:49, 298kiB/s]
19%|█▉ | 41.7M/217M [02:20<10:24, 281kiB/s]
19%|█▉ | 41.7M/217M [02:21<10:19, 283kiB/s]
19%|█▉ | 41.7M/217M [02:21<12:09, 240kiB/s]
19%|█▉ | 41.8M/217M [02:21<12:39, 231kiB/s]
19%|█▉ | 41.8M/217M [02:21<13:47, 212kiB/s]
19%|█▉ | 41.8M/217M [02:21<14:01, 208kiB/s]
19%|█▉ | 41.9M/217M [02:21<13:24, 218kiB/s]
19%|█▉ | 41.9M/217M [02:22<12:47, 228kiB/s]
19%|█▉ | 41.9M/217M [02:22<12:17, 238kiB/s]
19%|█▉ | 42.0M/217M [02:22<11:31, 253kiB/s]
19%|█▉ | 42.0M/217M [02:22<10:50, 270kiB/s]
19%|█▉ | 42.0M/217M [02:22<10:17, 284kiB/s]
19%|█▉ | 42.1M/217M [02:22<09:24, 310kiB/s]
19%|█▉ | 42.1M/217M [02:22<10:09, 287kiB/s]
19%|█▉ | 42.2M/217M [02:22<08:47, 332kiB/s]
19%|█▉ | 42.2M/217M [02:23<09:26, 309kiB/s]
19%|█▉ | 42.3M/217M [02:23<10:23, 281kiB/s]
19%|█▉ | 42.3M/217M [02:23<10:56, 267kiB/s]
19%|█▉ | 42.3M/217M [02:23<11:16, 259kiB/s]
19%|█▉ | 42.4M/217M [02:23<10:58, 266kiB/s]
20%|█▉ | 42.4M/217M [02:23<10:48, 270kiB/s]
20%|█▉ | 42.5M/217M [02:23<10:06, 288kiB/s]
20%|█▉ | 42.5M/217M [02:24<10:26, 279kiB/s]
20%|█▉ | 42.5M/217M [02:24<10:29, 278kiB/s]
20%|█▉ | 42.6M/217M [02:24<10:38, 274kiB/s]
20%|█▉ | 42.6M/217M [02:24<10:51, 268kiB/s]
20%|█▉ | 42.6M/217M [02:24<10:19, 282kiB/s]
20%|█▉ | 42.6M/217M [02:24<10:29, 278kiB/s]
20%|█▉ | 42.7M/217M [02:24<09:50, 296kiB/s]
20%|█▉ | 42.7M/217M [02:24<09:38, 302kiB/s]
20%|█▉ | 42.8M/217M [02:25<08:52, 328kiB/s]
20%|█▉ | 42.8M/217M [02:25<08:21, 348kiB/s]
20%|█▉ | 42.9M/217M [02:25<07:50, 371kiB/s]
20%|█▉ | 42.9M/217M [02:25<07:20, 396kiB/s]
20%|█▉ | 43.0M/217M [02:25<06:59, 416kiB/s]
20%|█▉ | 43.0M/217M [02:25<06:45, 430kiB/s]
20%|█▉ | 43.1M/217M [02:25<06:25, 452kiB/s]
20%|█▉ | 43.2M/217M [02:25<07:21, 395kiB/s]
20%|█▉ | 43.2M/217M [02:26<07:09, 405kiB/s]
20%|█▉ | 43.2M/217M [02:26<07:26, 390kiB/s]
20%|█▉ | 43.3M/217M [02:26<08:52, 327kiB/s]
20%|█▉ | 43.3M/217M [02:26<08:51, 327kiB/s]
20%|█▉ | 43.4M/217M [02:26<08:45, 331kiB/s]
20%|█▉ | 43.4M/217M [02:26<10:13, 283kiB/s]
20%|█▉ | 43.4M/217M [02:26<12:41, 228kiB/s]
20%|█▉ | 43.5M/217M [02:27<13:51, 209kiB/s]
20%|██ | 43.5M/217M [02:27<14:04, 206kiB/s]
20%|██ | 43.5M/217M [02:27<13:26, 215kiB/s]
20%|██ | 43.5M/217M [02:27<12:42, 228kiB/s]
20%|██ | 43.6M/217M [02:27<11:55, 243kiB/s]
20%|██ | 43.6M/217M [02:27<11:01, 263kiB/s]
20%|██ | 43.7M/217M [02:27<10:09, 285kiB/s]
20%|██ | 43.7M/217M [02:28<09:25, 307kiB/s]
20%|██ | 43.8M/217M [02:28<08:42, 332kiB/s]
20%|██ | 43.8M/217M [02:28<09:14, 313kiB/s]
20%|██ | 43.8M/217M [02:28<10:26, 277kiB/s]
20%|██ | 43.9M/217M [02:28<09:47, 295kiB/s]
20%|██ | 43.9M/217M [02:28<10:19, 280kiB/s]
20%|██ | 44.0M/217M [02:28<10:43, 270kiB/s]
20%|██ | 44.0M/217M [02:29<10:38, 271kiB/s]
20%|██ | 44.0M/217M [02:29<10:20, 279kiB/s]
20%|██ | 44.1M/217M [02:29<10:59, 263kiB/s]
20%|██ | 44.1M/217M [02:29<11:16, 256kiB/s]
20%|██ | 44.1M/217M [02:29<13:31, 213kiB/s]
20%|██ | 44.2M/217M [02:29<12:52, 224kiB/s]
20%|██ | 44.2M/217M [02:29<13:20, 216kiB/s]
20%|██ | 44.2M/217M [02:30<14:21, 201kiB/s]
20%|██ | 44.3M/217M [02:30<14:42, 196kiB/s]
20%|██ | 44.3M/217M [02:30<14:46, 195kiB/s]
20%|██ | 44.3M/217M [02:30<15:21, 188kiB/s]
20%|██ | 44.3M/217M [02:30<14:31, 198kiB/s]
20%|██ | 44.4M/217M [02:30<13:51, 208kiB/s]
20%|██ | 44.4M/217M [02:30<12:27, 231kiB/s]
20%|██ | 44.4M/217M [02:31<11:44, 245kiB/s]
20%|██ | 44.5M/217M [02:31<11:03, 261kiB/s]
20%|██ | 44.5M/217M [02:31<10:02, 287kiB/s]
21%|██ | 44.5M/217M [02:31<09:49, 293kiB/s]
21%|██ | 44.6M/217M [02:31<09:01, 319kiB/s]
21%|██ | 44.6M/217M [02:31<09:44, 295kiB/s]
21%|██ | 44.7M/217M [02:31<08:51, 325kiB/s]
21%|██ | 44.7M/217M [02:31<08:56, 322kiB/s]
21%|██ | 44.7M/217M [02:32<09:06, 316kiB/s]
21%|██ | 44.8M/217M [02:32<11:17, 255kiB/s]
21%|██ | 44.8M/217M [02:32<12:15, 235kiB/s]
21%|██ | 44.8M/217M [02:32<13:30, 213kiB/s]
21%|██ | 44.9M/217M [02:32<14:58, 192kiB/s]
21%|██ | 44.9M/217M [02:32<19:05, 150kiB/s]
21%|██ | 44.9M/217M [02:33<18:13, 158kiB/s]
21%|██ | 44.9M/217M [02:33<16:56, 170kiB/s]
21%|██ | 45.0M/217M [02:33<15:23, 187kiB/s]
21%|██ | 45.0M/217M [02:33<14:13, 202kiB/s]
21%|██ | 45.0M/217M [02:33<14:01, 205kiB/s]
21%|██ | 45.1M/217M [02:33<12:52, 223kiB/s]
21%|██ | 45.1M/217M [02:33<12:16, 234kiB/s]
21%|██ | 45.1M/217M [02:34<12:21, 232kiB/s]
21%|██ | 45.2M/217M [02:34<12:54, 222kiB/s]
21%|██ | 45.2M/217M [02:34<13:02, 220kiB/s]
21%|██ | 45.2M/217M [02:34<13:24, 214kiB/s]
21%|██ | 45.3M/217M [02:34<13:36, 211kiB/s]
21%|██ | 45.3M/217M [02:34<14:26, 199kiB/s]
21%|██ | 45.3M/217M [02:34<16:34, 173kiB/s]
21%|██ | 45.3M/217M [02:35<15:46, 182kiB/s]
21%|██ | 45.4M/217M [02:35<16:00, 179kiB/s]
21%|██ | 45.4M/217M [02:35<15:55, 180kiB/s]
21%|██ | 45.4M/217M [02:35<16:47, 171kiB/s]
21%|██ | 45.4M/217M [02:35<17:52, 160kiB/s]
21%|██ | 45.5M/217M [02:35<16:37, 172kiB/s]
21%|██ | 45.5M/217M [02:36<16:27, 174kiB/s]
21%|██ | 45.5M/217M [02:36<15:23, 186kiB/s]
21%|██ | 45.5M/217M [02:36<14:32, 197kiB/s]
21%|██ | 45.6M/217M [02:36<13:22, 214kiB/s]
21%|██ | 45.6M/217M [02:36<12:13, 234kiB/s]
21%|██ | 45.6M/217M [02:36<11:35, 247kiB/s]
21%|██ | 45.7M/217M [02:36<10:51, 263kiB/s]
21%|██ | 45.7M/217M [02:36<09:44, 293kiB/s]
21%|██ | 45.8M/217M [02:37<09:02, 316kiB/s]
21%|██ | 45.8M/217M [02:37<08:28, 337kiB/s]
21%|██ | 45.9M/217M [02:37<07:52, 362kiB/s]
21%|██ | 45.9M/217M [02:37<07:50, 364kiB/s]
21%|██ | 46.0M/217M [02:37<07:54, 361kiB/s]
21%|██ | 46.0M/217M [02:37<08:59, 317kiB/s]
21%|██ | 46.0M/217M [02:37<09:27, 302kiB/s]
21%|██ | 46.1M/217M [02:37<10:01, 285kiB/s]
21%|██ | 46.1M/217M [02:38<11:26, 249kiB/s]
21%|██ | 46.1M/217M [02:38<11:09, 256kiB/s]
21%|██ | 46.2M/217M [02:38<12:02, 237kiB/s]
21%|██▏ | 46.2M/217M [02:38<12:18, 232kiB/s]
21%|██▏ | 46.2M/217M [02:38<11:52, 240kiB/s]
21%|██▏ | 46.3M/217M [02:38<11:41, 244kiB/s]
21%|██▏ | 46.3M/217M [02:38<11:02, 258kiB/s]
21%|██▏ | 46.3M/217M [02:39<10:45, 265kiB/s]
21%|██▏ | 46.4M/217M [02:39<09:54, 287kiB/s]
21%|██▏ | 46.4M/217M [02:39<09:11, 310kiB/s]
21%|██▏ | 46.5M/217M [02:39<08:42, 327kiB/s]
21%|██▏ | 46.5M/217M [02:39<09:13, 309kiB/s]
21%|██▏ | 46.6M/217M [02:39<08:33, 333kiB/s]
21%|██▏ | 46.6M/217M [02:39<08:32, 333kiB/s]
21%|██▏ | 46.6M/217M [02:39<09:27, 301kiB/s]
21%|██▏ | 46.7M/217M [02:40<09:43, 292kiB/s]
21%|██▏ | 46.7M/217M [02:40<10:16, 277kiB/s]
22%|██▏ | 46.7M/217M [02:40<10:38, 267kiB/s]
22%|██▏ | 46.8M/217M [02:40<10:53, 261kiB/s]
22%|██▏ | 46.8M/217M [02:40<11:39, 244kiB/s]
22%|██▏ | 46.8M/217M [02:40<11:31, 247kiB/s]
22%|██▏ | 46.9M/217M [02:40<11:33, 246kiB/s]
22%|██▏ | 46.9M/217M [02:41<11:19, 251kiB/s]
22%|██▏ | 46.9M/217M [02:41<12:31, 227kiB/s]
22%|██▏ | 47.0M/217M [02:41<11:00, 258kiB/s]
22%|██▏ | 47.0M/217M [02:41<11:24, 249kiB/s]
22%|██▏ | 47.0M/217M [02:41<11:12, 253kiB/s]
22%|██▏ | 47.1M/217M [02:41<11:19, 251kiB/s]
22%|██▏ | 47.1M/217M [02:41<10:45, 264kiB/s]
22%|██▏ | 47.1M/217M [02:41<10:17, 276kiB/s]
22%|██▏ | 47.2M/217M [02:42<10:23, 273kiB/s]
22%|██▏ | 47.2M/217M [02:42<12:29, 227kiB/s]
22%|██▏ | 47.3M/217M [02:42<11:29, 247kiB/s]
22%|██▏ | 47.3M/217M [02:42<12:12, 232kiB/s]
22%|██▏ | 47.3M/217M [02:42<12:16, 231kiB/s]
22%|██▏ | 47.4M/217M [02:42<11:54, 238kiB/s]
22%|██▏ | 47.4M/217M [02:43<11:20, 250kiB/s]
22%|██▏ | 47.4M/217M [02:43<11:02, 256kiB/s]
22%|██▏ | 47.5M/217M [02:43<10:09, 279kiB/s]
22%|██▏ | 47.5M/217M [02:43<09:47, 289kiB/s]
22%|██▏ | 47.5M/217M [02:43<10:17, 275kiB/s]
22%|██▏ | 47.6M/217M [02:43<09:19, 304kiB/s]
22%|██▏ | 47.6M/217M [02:43<09:27, 299kiB/s]
22%|██▏ | 47.6M/217M [02:43<09:28, 298kiB/s]
22%|██▏ | 47.7M/217M [02:43<09:20, 303kiB/s]
22%|██▏ | 47.7M/217M [02:44<08:55, 317kiB/s]
22%|██▏ | 47.8M/217M [02:44<08:38, 327kiB/s]
22%|██▏ | 47.8M/217M [02:44<08:14, 343kiB/s]
22%|██▏ | 47.9M/217M [02:44<07:53, 358kiB/s]
22%|██▏ | 47.9M/217M [02:44<08:32, 330kiB/s]
22%|██▏ | 48.0M/217M [02:44<07:51, 359kiB/s]
22%|██▏ | 48.0M/217M [02:44<07:53, 358kiB/s]
22%|██▏ | 48.1M/217M [02:45<08:06, 348kiB/s]
22%|██▏ | 48.1M/217M [02:45<08:14, 342kiB/s]
22%|██▏ | 48.1M/217M [02:45<08:57, 315kiB/s]
22%|██▏ | 48.2M/217M [02:45<09:22, 301kiB/s]
22%|██▏ | 48.2M/217M [02:45<09:29, 297kiB/s]
22%|██▏ | 48.2M/217M [02:45<09:35, 294kiB/s]
22%|██▏ | 48.3M/217M [02:45<09:21, 301kiB/s]
22%|██▏ | 48.3M/217M [02:45<10:18, 273kiB/s]
22%|██▏ | 48.3M/217M [02:45<09:08, 308kiB/s]
22%|██▏ | 48.4M/217M [02:46<09:13, 305kiB/s]
22%|██▏ | 48.4M/217M [02:46<09:24, 299kiB/s]
22%|██▏ | 48.4M/217M [02:46<09:54, 284kiB/s]
22%|██▏ | 48.5M/217M [02:46<11:46, 239kiB/s]
22%|██▏ | 48.5M/217M [02:46<13:00, 216kiB/s]
22%|██▏ | 48.5M/217M [02:46<13:33, 208kiB/s]
22%|██▏ | 48.5M/217M [02:46<14:45, 191kiB/s]
22%|██▏ | 48.6M/217M [02:47<19:02, 148kiB/s]
22%|██▏ | 48.6M/217M [02:47<19:32, 144kiB/s]
22%|██▏ | 48.6M/217M [02:47<19:22, 145kiB/s]
22%|██▏ | 48.6M/217M [02:47<17:23, 162kiB/s]
22%|██▏ | 48.6M/217M [02:47<18:17, 154kiB/s]
22%|██▏ | 48.7M/217M [02:47<16:06, 174kiB/s]
22%|██▏ | 48.7M/217M [02:48<16:03, 175kiB/s]
22%|██▏ | 48.7M/217M [02:48<16:21, 172kiB/s]
22%|██▏ | 48.7M/217M [02:48<16:44, 168kiB/s]
22%|██▏ | 48.8M/217M [02:48<16:51, 167kiB/s]
22%|██▏ | 48.8M/217M [02:48<17:39, 159kiB/s]
22%|██▏ | 48.8M/217M [02:48<16:39, 169kiB/s]
22%|██▏ | 48.9M/217M [02:48<14:55, 188kiB/s]
23%|██▎ | 48.9M/217M [02:49<13:37, 206kiB/s]
23%|██▎ | 48.9M/217M [02:49<12:40, 221kiB/s]
23%|██▎ | 49.0M/217M [02:49<11:43, 239kiB/s]
23%|██▎ | 49.0M/217M [02:49<11:27, 245kiB/s]
23%|██▎ | 49.1M/217M [02:49<11:51, 236kiB/s]
23%|██▎ | 49.1M/217M [02:49<11:44, 239kiB/s]
23%|██▎ | 49.1M/217M [02:50<12:36, 222kiB/s]
23%|██▎ | 49.2M/217M [02:50<13:05, 214kiB/s]
23%|██▎ | 49.2M/217M [02:50<12:50, 218kiB/s]
23%|██▎ | 49.2M/217M [02:50<12:28, 224kiB/s]
23%|██▎ | 49.3M/217M [02:50<11:40, 240kiB/s]
23%|██▎ | 49.3M/217M [02:50<12:15, 228kiB/s]
23%|██▎ | 49.3M/217M [02:50<11:24, 245kiB/s]
23%|██▎ | 49.3M/217M [02:50<11:32, 242kiB/s]
23%|██▎ | 49.4M/217M [02:51<11:31, 243kiB/s]
23%|██▎ | 49.4M/217M [02:51<12:26, 225kiB/s]
23%|██▎ | 49.4M/217M [02:51<12:51, 218kiB/s]
23%|██▎ | 49.5M/217M [02:51<12:44, 220kiB/s]
23%|██▎ | 49.5M/217M [02:51<12:26, 225kiB/s]
23%|██▎ | 49.5M/217M [02:51<11:53, 235kiB/s]
23%|██▎ | 49.6M/217M [02:51<11:17, 248kiB/s]
23%|██▎ | 49.6M/217M [02:52<10:35, 264kiB/s]
23%|██▎ | 49.6M/217M [02:52<10:10, 275kiB/s]
23%|██▎ | 49.7M/217M [02:52<09:13, 303kiB/s]
23%|██▎ | 49.7M/217M [02:52<08:28, 329kiB/s]
23%|██▎ | 49.8M/217M [02:52<08:03, 347kiB/s]
23%|██▎ | 49.8M/217M [02:52<07:37, 366kiB/s]
23%|██▎ | 49.9M/217M [02:52<07:10, 389kiB/s]
23%|██▎ | 49.9M/217M [02:52<06:48, 410kiB/s]
23%|██▎ | 50.0M/217M [02:53<06:33, 425kiB/s]
23%|██▎ | 50.1M/217M [02:53<06:12, 449kiB/s]
23%|██▎ | 50.1M/217M [02:53<05:55, 471kiB/s]
23%|██▎ | 50.2M/217M [02:53<05:39, 492kiB/s]
23%|██▎ | 50.3M/217M [02:53<05:27, 511kiB/s]
23%|██▎ | 50.3M/217M [02:53<05:06, 545kiB/s]
23%|██▎ | 50.4M/217M [02:53<04:58, 559kiB/s]
23%|██▎ | 50.4M/217M [02:53<04:48, 578kiB/s]
23%|██▎ | 50.5M/217M [02:53<05:11, 536kiB/s]
23%|██▎ | 50.6M/217M [02:54<04:43, 587kiB/s]
23%|██▎ | 50.6M/217M [02:54<04:46, 583kiB/s]
23%|██▎ | 50.7M/217M [02:54<05:05, 546kiB/s]
23%|██▎ | 50.8M/217M [02:54<05:08, 540kiB/s]
23%|██▎ | 50.8M/217M [02:54<05:08, 540kiB/s]
23%|██▎ | 50.9M/217M [02:54<05:47, 479kiB/s]
23%|██▎ | 50.9M/217M [02:54<06:12, 446kiB/s]
23%|██▎ | 51.0M/217M [02:54<06:31, 425kiB/s]
23%|██▎ | 51.0M/217M [02:55<07:43, 359kiB/s]
24%|██▎ | 51.1M/217M [02:55<08:24, 330kiB/s]
24%|██▎ | 51.1M/217M [02:55<08:13, 337kiB/s]
24%|██▎ | 51.2M/217M [02:55<08:12, 338kiB/s]
24%|██▎ | 51.2M/217M [02:55<08:35, 322kiB/s]
24%|██▎ | 51.2M/217M [02:55<09:04, 305kiB/s]
24%|██▎ | 51.3M/217M [02:55<09:02, 306kiB/s]
24%|██▎ | 51.3M/217M [02:56<09:08, 302kiB/s]
24%|██▎ | 51.3M/217M [02:56<09:02, 306kiB/s]
24%|██▎ | 51.4M/217M [02:56<09:25, 294kiB/s]
24%|██▎ | 51.4M/217M [02:56<10:38, 260kiB/s]
24%|██▎ | 51.4M/217M [02:56<11:04, 250kiB/s]
24%|██▎ | 51.5M/217M [02:56<11:57, 231kiB/s]
24%|██▎ | 51.5M/217M [02:56<12:02, 229kiB/s]
24%|██▎ | 51.5M/217M [02:57<12:22, 223kiB/s]
24%|██▎ | 51.5M/217M [02:57<13:11, 210kiB/s]
24%|██▎ | 51.6M/217M [02:57<13:51, 199kiB/s]
24%|██▍ | 51.6M/217M [02:57<13:31, 204kiB/s]
24%|██▍ | 51.6M/217M [02:57<13:53, 199kiB/s]
24%|██▍ | 51.7M/217M [02:57<14:11, 194kiB/s]
24%|██▍ | 51.7M/217M [02:58<14:59, 184kiB/s]
24%|██▍ | 51.7M/217M [02:58<15:32, 178kiB/s]
24%|██▍ | 51.7M/217M [02:58<15:45, 175kiB/s]
24%|██▍ | 51.8M/217M [02:58<15:50, 174kiB/s]
24%|██▍ | 51.8M/217M [02:58<15:51, 174kiB/s]
24%|██▍ | 51.8M/217M [02:58<19:31, 141kiB/s]
24%|██▍ | 51.8M/217M [02:58<15:44, 175kiB/s]
24%|██▍ | 51.9M/217M [02:58<15:52, 174kiB/s]
24%|██▍ | 51.9M/217M [02:59<15:40, 176kiB/s]
24%|██▍ | 51.9M/217M [02:59<15:00, 184kiB/s]
24%|██▍ | 52.0M/217M [02:59<13:29, 204kiB/s]
24%|██▍ | 52.0M/217M [02:59<12:40, 217kiB/s]
24%|██▍ | 52.0M/217M [02:59<11:44, 235kiB/s]
24%|██▍ | 52.1M/217M [02:59<10:46, 255kiB/s]
24%|██▍ | 52.1M/217M [02:59<09:40, 285kiB/s]
24%|██▍ | 52.2M/217M [03:00<08:56, 308kiB/s]
24%|██▍ | 52.2M/217M [03:00<08:26, 326kiB/s]
24%|██▍ | 52.2M/217M [03:00<08:52, 310kiB/s]
24%|██▍ | 52.3M/217M [03:00<08:05, 340kiB/s]
24%|██▍ | 52.3M/217M [03:00<09:39, 285kiB/s]
24%|██▍ | 52.4M/217M [03:00<09:11, 299kiB/s]
24%|██▍ | 52.4M/217M [03:00<11:42, 235kiB/s]
24%|██▍ | 52.4M/217M [03:01<11:20, 242kiB/s]
24%|██▍ | 52.5M/217M [03:01<11:13, 245kiB/s]
24%|██▍ | 52.5M/217M [03:01<12:46, 215kiB/s]
24%|██▍ | 52.5M/217M [03:01<12:40, 217kiB/s]
24%|██▍ | 52.5M/217M [03:01<14:27, 190kiB/s]
24%|██▍ | 52.6M/217M [03:01<14:29, 189kiB/s]
24%|██▍ | 52.6M/217M [03:01<15:41, 175kiB/s]
24%|██▍ | 52.6M/217M [03:02<14:35, 188kiB/s]
24%|██▍ | 52.6M/217M [03:02<13:05, 209kiB/s]
24%|██▍ | 52.7M/217M [03:02<12:20, 222kiB/s]
24%|██▍ | 52.7M/217M [03:02<12:59, 211kiB/s]
24%|██▍ | 52.7M/217M [03:02<12:00, 228kiB/s]
24%|██▍ | 52.8M/217M [03:02<11:31, 238kiB/s]
24%|██▍ | 52.8M/217M [03:02<11:11, 245kiB/s]
24%|██▍ | 52.8M/217M [03:03<13:03, 210kiB/s]
24%|██▍ | 52.9M/217M [03:03<13:05, 209kiB/s]
24%|██▍ | 52.9M/217M [03:03<13:50, 198kiB/s]
24%|██▍ | 52.9M/217M [03:03<14:13, 193kiB/s]
24%|██▍ | 52.9M/217M [03:03<14:14, 192kiB/s]
24%|██▍ | 53.0M/217M [03:03<14:13, 192kiB/s]
24%|██▍ | 53.0M/217M [03:03<16:22, 167kiB/s]
24%|██▍ | 53.0M/217M [03:04<14:32, 188kiB/s]
24%|██▍ | 53.1M/217M [03:04<13:13, 207kiB/s]
24%|██▍ | 53.1M/217M [03:04<11:53, 230kiB/s]
24%|██▍ | 53.1M/217M [03:04<11:04, 247kiB/s]
24%|██▍ | 53.2M/217M [03:04<10:15, 267kiB/s]
24%|██▍ | 53.2M/217M [03:04<10:55, 250kiB/s]
24%|██▍ | 53.2M/217M [03:04<10:06, 271kiB/s]
25%|██▍ | 53.3M/217M [03:04<09:56, 275kiB/s]
25%|██▍ | 53.3M/217M [03:05<09:59, 274kiB/s]
25%|██▍ | 53.3M/217M [03:05<10:52, 251kiB/s]
25%|██▍ | 53.4M/217M [03:05<10:36, 258kiB/s]
25%|██▍ | 53.4M/217M [03:05<10:56, 250kiB/s]
25%|██▍ | 53.4M/217M [03:05<11:08, 245kiB/s]
25%|██▍ | 53.5M/217M [03:05<11:05, 246kiB/s]
25%|██▍ | 53.5M/217M [03:05<10:30, 260kiB/s]
25%|██▍ | 53.5M/217M [03:05<10:19, 264kiB/s]
25%|██▍ | 53.6M/217M [03:06<09:47, 279kiB/s]
25%|██▍ | 53.6M/217M [03:06<08:56, 305kiB/s]
25%|██▍ | 53.7M/217M [03:06<09:38, 283kiB/s]
25%|██▍ | 53.7M/217M [03:06<08:31, 320kiB/s]
25%|██▍ | 53.7M/217M [03:06<08:42, 313kiB/s]
25%|██▍ | 53.8M/217M [03:06<08:46, 310kiB/s]
25%|██▍ | 53.8M/217M [03:06<10:05, 270kiB/s]
25%|██▍ | 53.9M/217M [03:07<10:00, 272kiB/s]
25%|██▍ | 53.9M/217M [03:07<10:34, 257kiB/s]
25%|██▍ | 53.9M/217M [03:07<10:43, 254kiB/s]
25%|██▍ | 54.0M/217M [03:07<11:06, 245kiB/s]
25%|██▍ | 54.0M/217M [03:07<11:04, 246kiB/s]
25%|██▍ | 54.0M/217M [03:07<12:30, 217kiB/s]
25%|██▍ | 54.0M/217M [03:07<12:30, 217kiB/s]
25%|██▍ | 54.1M/217M [03:08<12:03, 226kiB/s]
25%|██▍ | 54.1M/217M [03:08<12:27, 218kiB/s]
25%|██▍ | 54.1M/217M [03:08<13:22, 203kiB/s]
25%|██▍ | 54.2M/217M [03:08<13:23, 203kiB/s]
25%|██▍ | 54.2M/217M [03:08<17:54, 152kiB/s]
25%|██▍ | 54.2M/217M [03:08<18:25, 147kiB/s]
25%|██▍ | 54.2M/217M [03:09<18:54, 144kiB/s]
25%|██▍ | 54.2M/217M [03:09<18:38, 146kiB/s]
25%|██▍ | 54.2M/217M [03:09<18:26, 147kiB/s]
25%|██▍ | 54.3M/217M [03:09<18:27, 147kiB/s]
25%|██▍ | 54.3M/217M [03:09<16:36, 163kiB/s]
25%|██▌ | 54.3M/217M [03:09<15:21, 177kiB/s]
25%|██▌ | 54.4M/217M [03:09<13:34, 200kiB/s]
25%|██▌ | 54.4M/217M [03:09<12:00, 226kiB/s]
25%|██▌ | 54.4M/217M [03:10<12:20, 220kiB/s]
25%|██▌ | 54.4M/217M [03:10<12:34, 216kiB/s]
25%|██▌ | 54.5M/217M [03:10<12:06, 224kiB/s]
25%|██▌ | 54.5M/217M [03:10<11:33, 235kiB/s]
25%|██▌ | 54.5M/217M [03:10<11:24, 238kiB/s]
25%|██▌ | 54.6M/217M [03:10<10:46, 252kiB/s]
25%|██▌ | 54.6M/217M [03:10<10:56, 248kiB/s]
25%|██▌ | 54.6M/217M [03:10<11:37, 233kiB/s]
25%|██▌ | 54.7M/217M [03:11<12:18, 220kiB/s]
25%|██▌ | 54.7M/217M [03:11<12:51, 211kiB/s]
25%|██▌ | 54.7M/217M [03:11<14:14, 190kiB/s]
25%|██▌ | 54.8M/217M [03:11<13:52, 195kiB/s]
25%|██▌ | 54.8M/217M [03:11<13:07, 206kiB/s]
25%|██▌ | 54.8M/217M [03:11<12:14, 221kiB/s]
25%|██▌ | 54.9M/217M [03:11<11:24, 237kiB/s]
25%|██▌ | 54.9M/217M [03:12<10:55, 248kiB/s]
25%|██▌ | 54.9M/217M [03:12<09:48, 276kiB/s]
25%|██▌ | 55.0M/217M [03:12<09:01, 300kiB/s]
25%|██▌ | 55.0M/217M [03:12<08:29, 319kiB/s]
25%|██▌ | 55.1M/217M [03:12<08:20, 324kiB/s]
25%|██▌ | 55.1M/217M [03:12<08:57, 302kiB/s]
25%|██▌ | 55.1M/217M [03:12<09:01, 299kiB/s]
25%|██▌ | 55.2M/217M [03:13<09:08, 295kiB/s]
25%|██▌ | 55.2M/217M [03:13<09:26, 286kiB/s]
25%|██▌ | 55.2M/217M [03:13<10:14, 264kiB/s]
25%|██▌ | 55.3M/217M [03:13<10:30, 257kiB/s]
25%|██▌ | 55.3M/217M [03:13<10:45, 251kiB/s]
25%|██▌ | 55.3M/217M [03:13<12:07, 223kiB/s]
25%|██▌ | 55.4M/217M [03:13<12:58, 208kiB/s]
25%|██▌ | 55.4M/217M [03:14<15:02, 179kiB/s]
26%|██▌ | 55.4M/217M [03:14<13:33, 199kiB/s]
26%|██▌ | 55.4M/217M [03:14<15:56, 169kiB/s]
26%|██▌ | 55.5M/217M [03:14<15:58, 169kiB/s]
26%|██▌ | 55.5M/217M [03:14<16:57, 159kiB/s]
26%|██▌ | 55.5M/217M [03:14<16:58, 159kiB/s]
26%|██▌ | 55.5M/217M [03:14<17:09, 157kiB/s]
26%|██▌ | 55.6M/217M [03:15<16:10, 167kiB/s]
26%|██▌ | 55.6M/217M [03:15<15:12, 177kiB/s]
26%|██▌ | 55.6M/217M [03:15<13:38, 197kiB/s]
26%|██▌ | 55.7M/217M [03:15<12:26, 217kiB/s]
26%|██▌ | 55.7M/217M [03:15<11:32, 233kiB/s]
26%|██▌ | 55.7M/217M [03:15<10:35, 254kiB/s]
26%|██▌ | 55.8M/217M [03:15<09:53, 272kiB/s]
26%|██▌ | 55.8M/217M [03:15<10:43, 251kiB/s]
26%|██▌ | 55.8M/217M [03:16<09:46, 275kiB/s]
26%|██▌ | 55.9M/217M [03:16<09:32, 282kiB/s]
26%|██▌ | 55.9M/217M [03:16<09:31, 282kiB/s]
26%|██▌ | 55.9M/217M [03:16<09:23, 286kiB/s]
26%|██▌ | 56.0M/217M [03:16<08:47, 306kiB/s]
26%|██▌ | 56.0M/217M [03:16<08:28, 317kiB/s]
26%|██▌ | 56.1M/217M [03:16<08:02, 334kiB/s]
26%|██▌ | 56.1M/217M [03:17<07:38, 352kiB/s]
26%|██▌ | 56.2M/217M [03:17<07:15, 370kiB/s]
26%|██▌ | 56.2M/217M [03:17<06:54, 389kiB/s]
26%|██▌ | 56.3M/217M [03:17<06:37, 405kiB/s]
26%|██▌ | 56.3M/217M [03:17<06:45, 397kiB/s]
26%|██▌ | 56.4M/217M [03:17<06:56, 386kiB/s]
26%|██▌ | 56.4M/217M [03:17<07:16, 368kiB/s]
26%|██▌ | 56.5M/217M [03:17<07:31, 356kiB/s]
26%|██▌ | 56.5M/217M [03:18<08:24, 319kiB/s]
26%|██▌ | 56.5M/217M [03:18<09:34, 280kiB/s]
26%|██▌ | 56.6M/217M [03:18<10:52, 246kiB/s]
26%|██▌ | 56.6M/217M [03:18<11:13, 239kiB/s]
26%|██▌ | 56.6M/217M [03:18<11:00, 243kiB/s]
26%|██▌ | 56.7M/217M [03:18<10:38, 252kiB/s]
26%|██▌ | 56.7M/217M [03:18<10:30, 255kiB/s]
26%|██▌ | 56.7M/217M [03:19<09:34, 280kiB/s]
26%|██▌ | 56.8M/217M [03:19<08:47, 304kiB/s]
26%|██▌ | 56.8M/217M [03:19<08:18, 322kiB/s]
26%|██▌ | 56.9M/217M [03:19<07:48, 343kiB/s]
26%|██▌ | 56.9M/217M [03:19<07:22, 362kiB/s]
26%|██▌ | 57.0M/217M [03:19<07:00, 381kiB/s]
26%|██▌ | 57.0M/217M [03:19<06:41, 399kiB/s]
26%|██▋ | 57.1M/217M [03:19<06:20, 421kiB/s]
26%|██▋ | 57.1M/217M [03:19<05:56, 449kiB/s]
26%|██▋ | 57.2M/217M [03:20<06:24, 416kiB/s]
26%|██▋ | 57.2M/217M [03:20<06:35, 405kiB/s]
26%|██▋ | 57.3M/217M [03:20<06:39, 400kiB/s]
26%|██▋ | 57.3M/217M [03:20<07:02, 379kiB/s]
26%|██▋ | 57.4M/217M [03:20<07:46, 343kiB/s]
26%|██▋ | 57.4M/217M [03:20<09:37, 277kiB/s]
26%|██▋ | 57.4M/217M [03:20<10:04, 264kiB/s]
26%|██▋ | 57.5M/217M [03:21<10:16, 259kiB/s]
26%|██▋ | 57.5M/217M [03:21<10:00, 266kiB/s]
26%|██▋ | 57.5M/217M [03:21<09:49, 271kiB/s]
26%|██▋ | 57.6M/217M [03:21<09:25, 282kiB/s]
27%|██▋ | 57.6M/217M [03:21<08:46, 303kiB/s]
27%|██▋ | 57.7M/217M [03:21<09:26, 282kiB/s]
27%|██▋ | 57.7M/217M [03:21<08:24, 316kiB/s]
27%|██▋ | 57.8M/217M [03:22<08:30, 313kiB/s]
27%|██▋ | 57.8M/217M [03:22<08:38, 307kiB/s]
27%|██▋ | 57.8M/217M [03:22<08:35, 309kiB/s]
27%|██▋ | 57.9M/217M [03:22<08:30, 313kiB/s]
27%|██▋ | 57.9M/217M [03:22<08:30, 312kiB/s]
27%|██▋ | 57.9M/217M [03:22<08:12, 324kiB/s]
27%|██▋ | 58.0M/217M [03:22<08:10, 325kiB/s]
27%|██▋ | 58.0M/217M [03:22<09:10, 289kiB/s]
27%|██▋ | 58.0M/217M [03:23<12:15, 217kiB/s]
27%|██▋ | 58.1M/217M [03:23<12:12, 217kiB/s]
27%|██▋ | 58.1M/217M [03:23<12:09, 218kiB/s]
27%|██▋ | 58.1M/217M [03:23<12:08, 219kiB/s]
27%|██▋ | 58.2M/217M [03:23<14:26, 184kiB/s]
27%|██▋ | 58.2M/217M [03:23<14:40, 181kiB/s]
27%|██▋ | 58.2M/217M [03:23<13:16, 200kiB/s]
27%|██▋ | 58.2M/217M [03:24<13:33, 195kiB/s]
27%|██▋ | 58.3M/217M [03:24<12:52, 206kiB/s]
27%|██▋ | 58.3M/217M [03:24<13:40, 194kiB/s]
27%|██▋ | 58.3M/217M [03:24<14:10, 187kiB/s]
27%|██▋ | 58.3M/217M [03:24<14:21, 184kiB/s]
27%|██▋ | 58.4M/217M [03:24<14:28, 183kiB/s]
27%|██▋ | 58.4M/217M [03:25<13:43, 193kiB/s]
27%|██▋ | 58.4M/217M [03:25<12:39, 209kiB/s]
27%|██▋ | 58.5M/217M [03:25<11:26, 231kiB/s]
27%|██▋ | 58.5M/217M [03:25<10:54, 243kiB/s]
27%|██▋ | 58.5M/217M [03:25<10:13, 259kiB/s]
27%|██▋ | 58.6M/217M [03:25<09:13, 287kiB/s]
27%|██▋ | 58.6M/217M [03:25<08:29, 311kiB/s]
27%|██▋ | 58.7M/217M [03:25<07:52, 336kiB/s]
27%|██▋ | 58.7M/217M [03:26<08:02, 329kiB/s]
27%|██▋ | 58.8M/217M [03:26<07:31, 351kiB/s]
27%|██▋ | 58.8M/217M [03:26<07:39, 345kiB/s]
27%|██▋ | 58.9M/217M [03:26<08:36, 307kiB/s]
27%|██▋ | 58.9M/217M [03:26<08:01, 329kiB/s]
27%|██▋ | 59.0M/217M [03:26<09:37, 274kiB/s]
27%|██▋ | 59.0M/217M [03:26<09:47, 269kiB/s]
27%|██▋ | 59.0M/217M [03:27<12:28, 211kiB/s]
27%|██▋ | 59.0M/217M [03:27<11:38, 227kiB/s]
27%|██▋ | 59.1M/217M [03:27<11:24, 231kiB/s]
27%|██▋ | 59.1M/217M [03:27<13:01, 202kiB/s]
27%|██▋ | 59.1M/217M [03:27<13:09, 200kiB/s]
27%|██▋ | 59.2M/217M [03:27<12:32, 210kiB/s]
27%|██▋ | 59.2M/217M [03:28<12:37, 209kiB/s]
27%|██▋ | 59.2M/217M [03:28<12:52, 205kiB/s]
27%|██▋ | 59.3M/217M [03:28<12:32, 210kiB/s]
27%|██▋ | 59.3M/217M [03:28<12:10, 216kiB/s]
27%|██▋ | 59.3M/217M [03:28<11:30, 229kiB/s]
27%|██▋ | 59.4M/217M [03:28<10:51, 242kiB/s]
27%|██▋ | 59.4M/217M [03:28<10:22, 254kiB/s]
27%|██▋ | 59.4M/217M [03:28<09:19, 282kiB/s]
27%|██▋ | 59.5M/217M [03:29<09:01, 292kiB/s]
27%|██▋ | 59.5M/217M [03:29<08:12, 320kiB/s]
27%|██▋ | 59.6M/217M [03:29<07:43, 340kiB/s]
27%|██▋ | 59.6M/217M [03:29<07:12, 365kiB/s]
27%|██▋ | 59.7M/217M [03:29<06:43, 390kiB/s]
27%|██▋ | 59.7M/217M [03:29<06:27, 407kiB/s]
28%|██▊ | 59.8M/217M [03:29<06:10, 426kiB/s]
28%|██▊ | 59.8M/217M [03:29<06:34, 399kiB/s]
28%|██▊ | 59.9M/217M [03:30<06:06, 429kiB/s]
28%|██▊ | 59.9M/217M [03:30<06:04, 431kiB/s]
28%|██▊ | 60.0M/217M [03:30<07:36, 345kiB/s]
28%|██▊ | 60.0M/217M [03:30<07:41, 341kiB/s]
28%|██▊ | 60.0M/217M [03:30<07:45, 338kiB/s]
28%|██▊ | 60.1M/217M [03:30<08:42, 301kiB/s]
28%|██▊ | 60.1M/217M [03:30<08:15, 317kiB/s]
28%|██▊ | 60.2M/217M [03:31<09:08, 287kiB/s]
28%|██▊ | 60.2M/217M [03:31<08:02, 326kiB/s]
28%|██▊ | 60.3M/217M [03:31<08:12, 319kiB/s]
28%|██▊ | 60.3M/217M [03:31<08:22, 312kiB/s]
28%|██▊ | 60.3M/217M [03:31<08:27, 309kiB/s]
28%|██▊ | 60.4M/217M [03:31<08:23, 311kiB/s]
28%|██▊ | 60.4M/217M [03:31<08:10, 320kiB/s]
28%|██▊ | 60.5M/217M [03:31<08:39, 302kiB/s]
28%|██▊ | 60.5M/217M [03:32<08:12, 319kiB/s]
28%|██▊ | 60.6M/217M [03:32<09:40, 270kiB/s]
28%|██▊ | 60.6M/217M [03:32<09:02, 289kiB/s]
28%|██▊ | 60.6M/217M [03:32<09:47, 267kiB/s]
28%|██▊ | 60.7M/217M [03:32<09:52, 264kiB/s]
28%|██▊ | 60.7M/217M [03:32<09:53, 264kiB/s]
28%|██▊ | 60.7M/217M [03:32<09:37, 271kiB/s]
28%|██▊ | 60.8M/217M [03:33<09:30, 274kiB/s]
28%|██▊ | 60.8M/217M [03:33<08:53, 293kiB/s]
28%|██▊ | 60.9M/217M [03:33<08:17, 315kiB/s]
28%|██▊ | 60.9M/217M [03:33<07:43, 337kiB/s]
28%|██▊ | 61.0M/217M [03:33<07:21, 354kiB/s]
28%|██▊ | 61.0M/217M [03:33<06:52, 379kiB/s]
28%|██▊ | 61.1M/217M [03:33<06:35, 395kiB/s]
28%|██▊ | 61.1M/217M [03:33<06:19, 412kiB/s]
28%|██▊ | 61.2M/217M [03:34<06:23, 407kiB/s]
28%|██▊ | 61.2M/217M [03:34<06:26, 404kiB/s]
28%|██▊ | 61.2M/217M [03:34<07:07, 365kiB/s]
28%|██▊ | 61.3M/217M [03:34<06:59, 372kiB/s]
28%|██▊ | 61.3M/217M [03:34<06:57, 373kiB/s]
28%|██▊ | 61.4M/217M [03:34<07:01, 370kiB/s]
28%|██▊ | 61.4M/217M [03:34<07:05, 366kiB/s]
28%|██▊ | 61.4M/217M [03:34<08:47, 296kiB/s]
28%|██▊ | 61.5M/217M [03:35<10:06, 257kiB/s]
28%|██▊ | 61.5M/217M [03:35<10:07, 256kiB/s]
28%|██▊ | 61.5M/217M [03:35<11:38, 223kiB/s]
28%|██▊ | 61.6M/217M [03:35<13:10, 197kiB/s]
28%|██▊ | 61.6M/217M [03:35<12:23, 209kiB/s]
28%|██▊ | 61.6M/217M [03:35<12:02, 215kiB/s]
28%|██▊ | 61.7M/217M [03:35<11:14, 231kiB/s]
28%|██▊ | 61.7M/217M [03:36<12:02, 215kiB/s]
28%|██▊ | 61.7M/217M [03:36<10:46, 241kiB/s]
28%|██▊ | 61.8M/217M [03:36<11:34, 224kiB/s]
28%|██▊ | 61.8M/217M [03:36<11:56, 217kiB/s]
28%|██▊ | 61.8M/217M [03:36<11:55, 217kiB/s]
28%|██▊ | 61.8M/217M [03:36<12:50, 202kiB/s]
28%|██▊ | 61.9M/217M [03:36<12:37, 205kiB/s]
28%|██▊ | 61.9M/217M [03:37<11:44, 221kiB/s]
29%|██▊ | 61.9M/217M [03:37<10:52, 238kiB/s]
29%|██▊ | 62.0M/217M [03:37<11:02, 234kiB/s]
29%|██▊ | 62.0M/217M [03:37<11:36, 223kiB/s]
29%|██▊ | 62.0M/217M [03:37<11:11, 231kiB/s]
29%|██▊ | 62.1M/217M [03:37<10:47, 240kiB/s]
29%|██▊ | 62.1M/217M [03:37<10:35, 244kiB/s]
29%|██▊ | 62.1M/217M [03:37<10:01, 258kiB/s]
29%|██▊ | 62.2M/217M [03:38<09:25, 274kiB/s]
29%|██▊ | 62.2M/217M [03:38<09:24, 275kiB/s]
29%|██▊ | 62.2M/217M [03:38<09:22, 276kiB/s]
29%|██▊ | 62.3M/217M [03:38<09:27, 273kiB/s]
29%|██▊ | 62.3M/217M [03:38<09:32, 270kiB/s]
29%|██▊ | 62.3M/217M [03:38<09:09, 282kiB/s]
29%|██▊ | 62.4M/217M [03:38<09:14, 279kiB/s]
29%|██▊ | 62.4M/217M [03:39<08:40, 298kiB/s]
29%|██▊ | 62.5M/217M [03:39<09:46, 264kiB/s]
29%|██▉ | 62.5M/217M [03:39<08:26, 305kiB/s]
29%|██▉ | 62.5M/217M [03:39<08:47, 293kiB/s]
29%|██▉ | 62.6M/217M [03:39<08:52, 291kiB/s]
29%|██▉ | 62.6M/217M [03:39<08:31, 302kiB/s]
29%|██▉ | 62.7M/217M [03:39<08:23, 307kiB/s]
29%|██▉ | 62.7M/217M [03:39<08:04, 319kiB/s]
29%|██▉ | 62.7M/217M [03:40<09:10, 281kiB/s]
29%|██▉ | 62.8M/217M [03:40<07:55, 325kiB/s]
29%|██▉ | 62.8M/217M [03:40<08:04, 319kiB/s]
29%|██▉ | 62.9M/217M [03:40<08:24, 306kiB/s]
29%|██▉ | 62.9M/217M [03:40<08:28, 304kiB/s]
29%|██▉ | 62.9M/217M [03:40<09:16, 277kiB/s]
29%|██▉ | 63.0M/217M [03:40<08:49, 291kiB/s]
29%|██▉ | 63.0M/217M [03:40<09:30, 271kiB/s]
29%|██▉ | 63.0M/217M [03:41<09:31, 270kiB/s]
29%|██▉ | 63.1M/217M [03:41<09:27, 272kiB/s]
29%|██▉ | 63.1M/217M [03:41<08:48, 292kiB/s]
29%|██▉ | 63.1M/217M [03:41<09:03, 284kiB/s]
29%|██▉ | 63.2M/217M [03:41<09:28, 271kiB/s]
29%|██▉ | 63.2M/217M [03:41<09:36, 267kiB/s]
29%|██▉ | 63.2M/217M [03:41<09:20, 275kiB/s]
29%|██▉ | 63.3M/217M [03:41<09:19, 275kiB/s]
29%|██▉ | 63.3M/217M [03:42<09:04, 283kiB/s]
29%|██▉ | 63.3M/217M [03:42<08:43, 294kiB/s]
29%|██▉ | 63.4M/217M [03:42<08:13, 312kiB/s]
29%|██▉ | 63.4M/217M [03:42<07:36, 337kiB/s]
29%|██▉ | 63.5M/217M [03:42<07:06, 361kiB/s]
29%|██▉ | 63.5M/217M [03:42<07:38, 335kiB/s]
29%|██▉ | 63.6M/217M [03:42<07:04, 362kiB/s]
29%|██▉ | 63.6M/217M [03:42<07:09, 358kiB/s]
29%|██▉ | 63.7M/217M [03:43<07:18, 351kiB/s]
29%|██▉ | 63.7M/217M [03:43<07:08, 358kiB/s]
29%|██▉ | 63.8M/217M [03:43<07:50, 326kiB/s]
29%|██▉ | 63.8M/217M [03:43<07:27, 343kiB/s]
29%|██▉ | 63.8M/217M [03:43<07:41, 332kiB/s]
29%|██▉ | 63.9M/217M [03:43<07:54, 323kiB/s]
29%|██▉ | 63.9M/217M [03:43<07:56, 322kiB/s]
29%|██▉ | 63.9M/217M [03:43<09:10, 278kiB/s]
29%|██▉ | 64.0M/217M [03:44<09:05, 281kiB/s]
29%|██▉ | 64.0M/217M [03:44<09:14, 276kiB/s]
29%|██▉ | 64.0M/217M [03:44<09:12, 277kiB/s]
29%|██▉ | 64.1M/217M [03:44<08:58, 284kiB/s]
30%|██▉ | 64.1M/217M [03:44<08:48, 290kiB/s]
30%|██▉ | 64.2M/217M [03:44<08:30, 300kiB/s]
30%|██▉ | 64.2M/217M [03:44<07:58, 320kiB/s]
30%|██▉ | 64.3M/217M [03:45<07:31, 339kiB/s]
30%|██▉ | 64.3M/217M [03:45<07:58, 320kiB/s]
30%|██▉ | 64.4M/217M [03:45<07:11, 354kiB/s]
30%|██▉ | 64.4M/217M [03:45<07:15, 351kiB/s]
30%|██▉ | 64.4M/217M [03:45<07:28, 341kiB/s]
30%|██▉ | 64.5M/217M [03:45<08:18, 307kiB/s]
30%|██▉ | 64.5M/217M [03:45<10:18, 247kiB/s]
30%|██▉ | 64.5M/217M [03:46<11:29, 222kiB/s]
30%|██▉ | 64.6M/217M [03:46<12:23, 205kiB/s]
30%|██▉ | 64.6M/217M [03:46<12:32, 203kiB/s]
30%|██▉ | 64.6M/217M [03:46<13:06, 194kiB/s]
30%|██▉ | 64.7M/217M [03:46<12:30, 203kiB/s]
30%|██▉ | 64.7M/217M [03:46<14:10, 179kiB/s]
30%|██▉ | 64.8M/217M [03:47<11:50, 215kiB/s]
30%|██▉ | 64.8M/217M [03:47<11:57, 213kiB/s]
30%|██▉ | 64.8M/217M [03:47<12:24, 205kiB/s]
30%|██▉ | 64.8M/217M [03:47<12:32, 203kiB/s]
30%|██▉ | 64.9M/217M [03:47<13:05, 194kiB/s]
30%|██▉ | 64.9M/217M [03:47<13:19, 191kiB/s]
30%|██▉ | 64.9M/217M [03:48<13:25, 189kiB/s]
30%|██▉ | 64.9M/217M [03:48<13:27, 189kiB/s]
30%|██▉ | 65.0M/217M [03:48<15:40, 162kiB/s]
30%|██▉ | 65.0M/217M [03:48<16:29, 154kiB/s]
30%|██▉ | 65.0M/217M [03:48<16:14, 156kiB/s]
30%|██▉ | 65.0M/217M [03:48<16:22, 155kiB/s]
30%|██▉ | 65.0M/217M [03:48<16:29, 154kiB/s]
30%|██▉ | 65.1M/217M [03:49<14:57, 170kiB/s]
30%|██▉ | 65.1M/217M [03:49<13:38, 186kiB/s]
30%|██▉ | 65.1M/217M [03:49<12:33, 202kiB/s]
30%|██▉ | 65.2M/217M [03:49<11:41, 217kiB/s]
30%|███ | 65.2M/217M [03:49<11:52, 214kiB/s]
30%|███ | 65.2M/217M [03:49<10:52, 233kiB/s]
30%|███ | 65.3M/217M [03:49<12:21, 205kiB/s]
30%|███ | 65.3M/217M [03:49<11:50, 214kiB/s]
30%|███ | 65.3M/217M [03:50<12:54, 196kiB/s]
30%|███ | 65.4M/217M [03:50<14:42, 172kiB/s]
30%|███ | 65.4M/217M [03:50<12:59, 195kiB/s]
30%|███ | 65.4M/217M [03:50<13:20, 190kiB/s]
30%|███ | 65.5M/217M [03:50<13:09, 192kiB/s]
30%|███ | 65.5M/217M [03:51<12:47, 198kiB/s]
30%|███ | 65.5M/217M [03:51<11:55, 212kiB/s]
30%|███ | 65.6M/217M [03:51<11:18, 224kiB/s]
30%|███ | 65.6M/217M [03:51<10:17, 246kiB/s]
30%|███ | 65.6M/217M [03:51<09:44, 260kiB/s]
30%|███ | 65.7M/217M [03:51<08:55, 283kiB/s]
30%|███ | 65.7M/217M [03:51<09:56, 254kiB/s]
30%|███ | 65.8M/217M [03:51<08:20, 303kiB/s]
30%|███ | 65.8M/217M [03:52<08:26, 299kiB/s]
30%|███ | 65.8M/217M [03:52<08:28, 298kiB/s]
30%|███ | 65.8M/217M [03:52<08:56, 282kiB/s]
30%|███ | 65.9M/217M [03:52<09:07, 277kiB/s]
30%|███ | 65.9M/217M [03:52<09:07, 277kiB/s]
30%|███ | 65.9M/217M [03:52<10:23, 243kiB/s]
30%|███ | 66.0M/217M [03:52<12:45, 198kiB/s]
30%|███ | 66.0M/217M [03:53<12:31, 201kiB/s]
30%|███ | 66.0M/217M [03:53<11:56, 211kiB/s]
30%|███ | 66.1M/217M [03:53<11:11, 225kiB/s]
30%|███ | 66.1M/217M [03:53<10:40, 236kiB/s]
30%|███ | 66.1M/217M [03:53<09:28, 266kiB/s]
30%|███ | 66.2M/217M [03:53<09:07, 276kiB/s]
30%|███ | 66.2M/217M [03:53<08:20, 302kiB/s]
30%|███ | 66.3M/217M [03:53<08:35, 293kiB/s]
31%|███ | 66.3M/217M [03:54<08:14, 305kiB/s]
31%|███ | 66.3M/217M [03:54<08:24, 299kiB/s]
31%|███ | 66.4M/217M [03:54<08:32, 295kiB/s]
31%|███ | 66.4M/217M [03:54<08:21, 301kiB/s]
31%|███ | 66.4M/217M [03:54<08:18, 303kiB/s]
31%|███ | 66.5M/217M [03:54<07:58, 315kiB/s]
31%|███ | 66.5M/217M [03:54<07:35, 331kiB/s]
31%|███ | 66.6M/217M [03:54<07:10, 350kiB/s]
31%|███ | 66.6M/217M [03:54<06:52, 366kiB/s]
31%|███ | 66.7M/217M [03:55<06:29, 387kiB/s]
31%|███ | 66.7M/217M [03:55<07:18, 344kiB/s]
31%|███ | 66.8M/217M [03:55<07:19, 343kiB/s]
31%|███ | 66.8M/217M [03:55<08:16, 303kiB/s]
31%|███ | 66.8M/217M [03:55<08:57, 280kiB/s]
31%|███ | 66.8M/217M [03:55<09:58, 251kiB/s]
31%|███ | 66.9M/217M [03:56<10:55, 229kiB/s]
31%|███ | 66.9M/217M [03:56<11:16, 222kiB/s]
31%|███ | 66.9M/217M [03:56<10:50, 231kiB/s]
31%|███ | 67.0M/217M [03:56<10:27, 240kiB/s]
31%|███ | 67.0M/217M [03:56<10:12, 245kiB/s]
31%|███ | 67.0M/217M [03:56<10:11, 246kiB/s]
31%|███ | 67.1M/217M [03:56<10:18, 243kiB/s]
31%|███ | 67.1M/217M [03:56<10:16, 244kiB/s]
31%|███ | 67.1M/217M [03:57<10:23, 241kiB/s]
31%|███ | 67.2M/217M [03:57<10:35, 236kiB/s]
31%|███ | 67.2M/217M [03:57<11:18, 221kiB/s]
31%|███ | 67.2M/217M [03:57<11:46, 213kiB/s]
31%|███ | 67.2M/217M [03:57<11:32, 217kiB/s]
31%|███ | 67.3M/217M [03:57<11:42, 213kiB/s]
31%|███ | 67.3M/217M [03:57<12:03, 207kiB/s]
31%|███ | 67.3M/217M [03:58<11:49, 211kiB/s]
31%|███ | 67.4M/217M [03:58<11:29, 217kiB/s]
31%|███ | 67.4M/217M [03:58<11:13, 222kiB/s]
31%|███ | 67.4M/217M [03:58<10:29, 238kiB/s]
31%|███ | 67.5M/217M [03:58<11:20, 220kiB/s]
31%|███ | 67.5M/217M [03:58<10:23, 240kiB/s]
31%|███ | 67.5M/217M [03:58<10:44, 232kiB/s]
31%|███ | 67.6M/217M [03:59<10:21, 241kiB/s]
31%|███ | 67.6M/217M [03:59<09:56, 251kiB/s]
31%|███ | 67.6M/217M [03:59<09:39, 258kiB/s]
31%|███ | 67.7M/217M [03:59<09:11, 271kiB/s]
31%|███ | 67.7M/217M [03:59<08:50, 282kiB/s]
31%|███ | 67.7M/217M [03:59<08:10, 305kiB/s]
31%|███ | 67.8M/217M [03:59<07:33, 329kiB/s]
31%|███ | 67.8M/217M [03:59<07:07, 349kiB/s]
31%|███ | 67.9M/217M [03:59<07:31, 331kiB/s]
31%|███▏ | 67.9M/217M [04:00<07:00, 355kiB/s]
31%|███▏ | 68.0M/217M [04:00<07:11, 346kiB/s]
31%|███▏ | 68.0M/217M [04:00<07:12, 345kiB/s]
31%|███▏ | 68.0M/217M [04:00<07:20, 339kiB/s]
31%|███▏ | 68.1M/217M [04:00<07:10, 347kiB/s]
31%|███▏ | 68.1M/217M [04:00<06:56, 358kiB/s]
31%|███▏ | 68.2M/217M [04:00<07:23, 336kiB/s]
31%|███▏ | 68.2M/217M [04:01<08:03, 309kiB/s]
31%|███▏ | 68.3M/217M [04:01<08:53, 279kiB/s]
31%|███▏ | 68.3M/217M [04:01<10:24, 239kiB/s]
31%|███▏ | 68.3M/217M [04:01<10:24, 238kiB/s]
31%|███▏ | 68.4M/217M [04:01<13:58, 178kiB/s]
31%|███▏ | 68.4M/217M [04:01<14:37, 170kiB/s]
31%|███▏ | 68.4M/217M [04:02<15:47, 157kiB/s]
31%|███▏ | 68.4M/217M [04:02<15:14, 163kiB/s]
32%|███▏ | 68.5M/217M [04:02<13:48, 180kiB/s]
32%|███▏ | 68.5M/217M [04:02<12:27, 199kiB/s]
32%|███▏ | 68.5M/217M [04:02<11:30, 215kiB/s]
32%|███▏ | 68.6M/217M [04:02<11:03, 224kiB/s]
32%|███▏ | 68.6M/217M [04:02<11:07, 223kiB/s]
32%|███▏ | 68.6M/217M [04:03<10:52, 228kiB/s]
32%|███▏ | 68.7M/217M [04:03<10:25, 238kiB/s]
32%|███▏ | 68.7M/217M [04:03<09:58, 248kiB/s]
32%|███▏ | 68.7M/217M [04:03<09:47, 253kiB/s]
32%|███▏ | 68.8M/217M [04:03<09:00, 275kiB/s]
32%|███▏ | 68.8M/217M [04:03<08:20, 297kiB/s]
32%|███▏ | 68.9M/217M [04:03<07:48, 317kiB/s]
32%|███▏ | 68.9M/217M [04:04<07:20, 337kiB/s]
32%|███▏ | 69.0M/217M [04:04<06:49, 363kiB/s]
32%|███▏ | 69.0M/217M [04:04<06:27, 382kiB/s]
32%|███▏ | 69.1M/217M [04:04<06:07, 404kiB/s]
32%|███▏ | 69.1M/217M [04:04<06:18, 392kiB/s]
32%|███▏ | 69.2M/217M [04:04<06:13, 397kiB/s]
32%|███▏ | 69.2M/217M [04:04<06:14, 396kiB/s]
32%|███▏ | 69.3M/217M [04:04<06:27, 382kiB/s]
32%|███▏ | 69.3M/217M [04:05<08:11, 301kiB/s]
32%|███▏ | 69.4M/217M [04:05<07:11, 343kiB/s]
32%|███▏ | 69.4M/217M [04:05<07:12, 342kiB/s]
32%|███▏ | 69.4M/217M [04:05<07:08, 345kiB/s]
32%|███▏ | 69.5M/217M [04:05<07:09, 344kiB/s]
32%|███▏ | 69.5M/217M [04:05<07:34, 325kiB/s]
32%|███▏ | 69.6M/217M [04:05<07:18, 337kiB/s]
32%|███▏ | 69.6M/217M [04:05<07:01, 350kiB/s]
32%|███▏ | 69.6M/217M [04:06<06:34, 374kiB/s]
32%|███▏ | 69.7M/217M [04:06<06:22, 386kiB/s]
32%|███▏ | 69.7M/217M [04:06<06:02, 407kiB/s]
32%|███▏ | 69.8M/217M [04:06<05:31, 445kiB/s]
32%|███▏ | 69.9M/217M [04:06<05:58, 411kiB/s]
32%|███▏ | 69.9M/217M [04:06<05:35, 439kiB/s]
32%|███▏ | 70.0M/217M [04:06<05:52, 417kiB/s]
32%|███▏ | 70.0M/217M [04:06<06:12, 396kiB/s]
32%|███▏ | 70.0M/217M [04:06<06:38, 370kiB/s]
32%|███▏ | 70.1M/217M [04:07<06:48, 360kiB/s]
32%|███▏ | 70.1M/217M [04:07<06:58, 352kiB/s]
32%|███▏ | 70.2M/217M [04:07<07:42, 318kiB/s]
32%|███▏ | 70.2M/217M [04:07<07:22, 332kiB/s]
32%|███▏ | 70.2M/217M [04:07<07:46, 315kiB/s]
32%|███▏ | 70.3M/217M [04:07<08:17, 295kiB/s]
32%|███▏ | 70.3M/217M [04:07<08:22, 292kiB/s]
32%|███▏ | 70.3M/217M [04:07<08:15, 297kiB/s]
32%|███▏ | 70.4M/217M [04:08<08:07, 302kiB/s]
32%|███▏ | 70.4M/217M [04:08<07:48, 314kiB/s]
32%|███▏ | 70.5M/217M [04:08<07:33, 324kiB/s]
32%|███▏ | 70.5M/217M [04:08<07:07, 343kiB/s]
32%|███▏ | 70.6M/217M [04:08<07:38, 320kiB/s]
32%|███▏ | 70.6M/217M [04:08<07:10, 341kiB/s]
33%|███▎ | 70.6M/217M [04:08<07:14, 338kiB/s]
33%|███▎ | 70.7M/217M [04:08<07:20, 333kiB/s]
33%|███▎ | 70.7M/217M [04:09<07:25, 329kiB/s]
33%|███▎ | 70.8M/217M [04:09<07:13, 338kiB/s]
33%|███▎ | 70.8M/217M [04:09<08:09, 299kiB/s]
33%|███▎ | 70.8M/217M [04:09<07:24, 329kiB/s]
33%|███▎ | 70.9M/217M [04:09<07:47, 313kiB/s]
33%|███▎ | 70.9M/217M [04:09<08:03, 303kiB/s]
33%|███▎ | 70.9M/217M [04:09<08:08, 299kiB/s]
33%|███▎ | 71.0M/217M [04:09<07:43, 316kiB/s]
33%|███▎ | 71.0M/217M [04:10<07:10, 339kiB/s]
33%|███▎ | 71.1M/217M [04:10<07:26, 328kiB/s]
33%|███▎ | 71.1M/217M [04:10<06:59, 348kiB/s]
33%|███▎ | 71.2M/217M [04:10<07:39, 318kiB/s]
33%|███▎ | 71.2M/217M [04:10<06:47, 358kiB/s]
33%|███▎ | 71.3M/217M [04:10<06:49, 357kiB/s]
33%|███▎ | 71.3M/217M [04:10<06:57, 350kiB/s]
33%|███▎ | 71.4M/217M [04:10<06:54, 352kiB/s]
33%|███▎ | 71.4M/217M [04:11<07:04, 344kiB/s]
33%|███▎ | 71.4M/217M [04:11<07:31, 323kiB/s]
33%|███▎ | 71.5M/217M [04:11<07:43, 315kiB/s]
33%|███▎ | 71.5M/217M [04:11<08:30, 286kiB/s]
33%|███▎ | 71.5M/217M [04:11<08:54, 273kiB/s]
33%|███▎ | 71.6M/217M [04:11<09:48, 248kiB/s]
33%|███▎ | 71.6M/217M [04:11<09:51, 246kiB/s]
33%|███▎ | 71.6M/217M [04:12<09:45, 249kiB/s]
33%|███▎ | 71.7M/217M [04:12<09:12, 264kiB/s]
33%|███▎ | 71.7M/217M [04:12<08:59, 270kiB/s]
33%|███▎ | 71.7M/217M [04:12<08:14, 294kiB/s]
33%|███▎ | 71.8M/217M [04:12<07:44, 313kiB/s]
33%|███▎ | 71.8M/217M [04:12<07:12, 337kiB/s]
33%|███▎ | 71.9M/217M [04:12<06:50, 354kiB/s]
33%|███▎ | 71.9M/217M [04:12<06:22, 380kiB/s]
33%|███▎ | 72.0M/217M [04:13<07:07, 340kiB/s]
33%|███▎ | 72.0M/217M [04:13<06:07, 395kiB/s]
33%|███▎ | 72.1M/217M [04:13<06:23, 379kiB/s]
33%|███▎ | 72.1M/217M [04:13<06:21, 380kiB/s]
33%|███▎ | 72.2M/217M [04:13<06:26, 376kiB/s]
33%|███▎ | 72.2M/217M [04:13<06:11, 391kiB/s]
33%|███▎ | 72.3M/217M [04:13<06:01, 401kiB/s]
33%|███▎ | 72.3M/217M [04:13<05:57, 406kiB/s]
33%|███▎ | 72.4M/217M [04:14<05:43, 422kiB/s]
33%|███▎ | 72.4M/217M [04:14<05:39, 427kiB/s]
33%|███▎ | 72.5M/217M [04:14<05:46, 418kiB/s]
33%|███▎ | 72.5M/217M [04:14<07:27, 323kiB/s]
33%|███▎ | 72.6M/217M [04:14<06:50, 353kiB/s]
33%|███▎ | 72.6M/217M [04:14<07:13, 334kiB/s]
33%|███▎ | 72.6M/217M [04:14<07:10, 336kiB/s]
33%|███▎ | 72.7M/217M [04:14<07:24, 325kiB/s]
33%|███▎ | 72.7M/217M [04:15<08:52, 272kiB/s]
33%|███▎ | 72.7M/217M [04:15<08:39, 278kiB/s]
33%|███▎ | 72.8M/217M [04:15<08:47, 274kiB/s]
34%|███▎ | 72.8M/217M [04:15<08:50, 272kiB/s]
34%|███▎ | 72.8M/217M [04:15<08:37, 279kiB/s]
34%|███▎ | 72.9M/217M [04:15<09:46, 246kiB/s]
34%|███▎ | 72.9M/217M [04:15<08:58, 268kiB/s]
34%|███▎ | 73.0M/217M [04:16<10:35, 227kiB/s]
34%|███▎ | 73.0M/217M [04:16<10:39, 226kiB/s]
34%|███▎ | 73.0M/217M [04:16<11:30, 209kiB/s]
34%|███▎ | 73.1M/217M [04:16<11:26, 210kiB/s]
34%|███▎ | 73.1M/217M [04:16<12:33, 191kiB/s]
34%|███▎ | 73.1M/217M [04:16<12:12, 197kiB/s]
34%|███▎ | 73.1M/217M [04:17<12:37, 190kiB/s]
34%|███▎ | 73.2M/217M [04:17<12:20, 195kiB/s]
34%|███▎ | 73.2M/217M [04:17<11:46, 204kiB/s]
34%|███▎ | 73.2M/217M [04:17<12:53, 186kiB/s]
34%|███▎ | 73.3M/217M [04:17<10:58, 219kiB/s]
34%|███▎ | 73.3M/217M [04:17<11:02, 217kiB/s]
34%|███▍ | 73.4M/217M [04:18<10:50, 221kiB/s]
34%|███▍ | 73.4M/217M [04:18<10:31, 228kiB/s]
34%|███▍ | 73.4M/217M [04:18<10:06, 237kiB/s]
34%|███▍ | 73.5M/217M [04:18<09:37, 249kiB/s]
34%|███▍ | 73.5M/217M [04:18<08:57, 267kiB/s]
34%|███▍ | 73.5M/217M [04:18<09:30, 252kiB/s]
34%|███▍ | 73.6M/217M [04:18<09:05, 264kiB/s]
34%|███▍ | 73.6M/217M [04:18<09:05, 264kiB/s]
34%|███▍ | 73.6M/217M [04:19<10:56, 219kiB/s]
34%|███▍ | 73.6M/217M [04:19<12:06, 198kiB/s]
34%|███▍ | 73.7M/217M [04:19<11:59, 200kiB/s]
34%|███▍ | 73.7M/217M [04:19<11:20, 211kiB/s]
34%|███▍ | 73.7M/217M [04:19<10:26, 229kiB/s]
34%|███▍ | 73.8M/217M [04:19<10:56, 219kiB/s]
34%|███▍ | 73.8M/217M [04:20<10:18, 232kiB/s]
34%|███▍ | 73.8M/217M [04:20<10:18, 232kiB/s]
34%|███▍ | 73.9M/217M [04:20<09:50, 243kiB/s]
34%|███▍ | 73.9M/217M [04:20<09:36, 249kiB/s]
34%|███▍ | 73.9M/217M [04:20<08:55, 268kiB/s]
34%|███▍ | 74.0M/217M [04:20<09:40, 247kiB/s]
34%|███▍ | 74.0M/217M [04:20<09:06, 262kiB/s]
34%|███▍ | 74.0M/217M [04:20<09:29, 252kiB/s]
34%|███▍ | 74.1M/217M [04:21<09:30, 251kiB/s]
34%|███▍ | 74.1M/217M [04:21<10:49, 220kiB/s]
34%|███▍ | 74.1M/217M [04:21<12:15, 195kiB/s]
34%|███▍ | 74.2M/217M [04:21<11:39, 205kiB/s]
34%|███▍ | 74.2M/217M [04:21<11:14, 212kiB/s]
34%|███▍ | 74.2M/217M [04:21<10:33, 226kiB/s]
34%|███▍ | 74.3M/217M [04:21<09:56, 240kiB/s]
34%|███▍ | 74.3M/217M [04:22<09:23, 254kiB/s]
34%|███▍ | 74.3M/217M [04:22<08:51, 269kiB/s]
34%|███▍ | 74.4M/217M [04:22<08:03, 296kiB/s]
34%|███▍ | 74.4M/217M [04:22<07:33, 315kiB/s]
34%|███▍ | 74.5M/217M [04:22<07:06, 335kiB/s]
34%|███▍ | 74.5M/217M [04:22<06:38, 359kiB/s]
34%|███▍ | 74.6M/217M [04:22<06:16, 379kiB/s]
34%|███▍ | 74.6M/217M [04:22<05:53, 404kiB/s]
34%|███▍ | 74.7M/217M [04:22<05:37, 422kiB/s]
34%|███▍ | 74.7M/217M [04:23<05:19, 446kiB/s]
34%|███▍ | 74.8M/217M [04:23<05:02, 471kiB/s]
34%|███▍ | 74.9M/217M [04:23<04:42, 505kiB/s]
34%|███▍ | 74.9M/217M [04:23<05:16, 449kiB/s]
35%|███▍ | 75.0M/217M [04:23<04:33, 521kiB/s]
35%|███▍ | 75.1M/217M [04:23<05:05, 466kiB/s]
35%|███▍ | 75.1M/217M [04:23<05:22, 441kiB/s]
35%|███▍ | 75.2M/217M [04:24<05:54, 401kiB/s]
35%|███▍ | 75.2M/217M [04:24<06:05, 389kiB/s]
35%|███▍ | 75.3M/217M [04:24<06:05, 388kiB/s]
35%|███▍ | 75.3M/217M [04:24<06:01, 393kiB/s]
35%|███▍ | 75.4M/217M [04:24<05:50, 405kiB/s]
35%|███▍ | 75.4M/217M [04:24<05:40, 416kiB/s]
35%|███▍ | 75.4M/217M [04:24<05:28, 432kiB/s]
35%|███▍ | 75.5M/217M [04:24<05:13, 452kiB/s]
35%|███▍ | 75.6M/217M [04:25<05:37, 420kiB/s]
35%|███▍ | 75.6M/217M [04:25<05:17, 446kiB/s]
35%|███▍ | 75.7M/217M [04:25<05:27, 432kiB/s]
35%|███▍ | 75.7M/217M [04:25<05:28, 430kiB/s]
35%|███▍ | 75.8M/217M [04:25<05:22, 439kiB/s]
35%|███▍ | 75.8M/217M [04:25<05:21, 440kiB/s]
35%|███▍ | 75.9M/217M [04:25<05:13, 451kiB/s]
35%|███▍ | 75.9M/217M [04:25<05:46, 408kiB/s]
35%|███▍ | 76.0M/217M [04:25<05:32, 425kiB/s]
35%|███▍ | 76.0M/217M [04:26<05:55, 397kiB/s]
35%|███▌ | 76.1M/217M [04:26<06:09, 382kiB/s]
35%|███▌ | 76.1M/217M [04:26<06:32, 359kiB/s]
35%|███▌ | 76.2M/217M [04:26<06:55, 339kiB/s]
35%|███▌ | 76.2M/217M [04:26<07:08, 330kiB/s]
35%|███▌ | 76.2M/217M [04:26<07:08, 329kiB/s]
35%|███▌ | 76.3M/217M [04:26<07:14, 325kiB/s]
35%|███▌ | 76.3M/217M [04:27<08:39, 272kiB/s]
35%|███▌ | 76.4M/217M [04:27<08:03, 292kiB/s]
35%|███▌ | 76.4M/217M [04:27<08:21, 281kiB/s]
35%|███▌ | 76.4M/217M [04:27<09:11, 255kiB/s]
35%|███▌ | 76.4M/217M [04:27<09:44, 241kiB/s]
35%|███▌ | 76.5M/217M [04:27<10:11, 230kiB/s]
35%|███▌ | 76.5M/217M [04:27<10:10, 231kiB/s]
35%|███▌ | 76.5M/217M [04:28<10:14, 229kiB/s]
35%|███▌ | 76.6M/217M [04:28<11:39, 201kiB/s]
35%|███▌ | 76.6M/217M [04:28<11:45, 199kiB/s]
35%|███▌ | 76.6M/217M [04:28<12:37, 186kiB/s]
35%|███▌ | 76.6M/217M [04:28<13:27, 174kiB/s]
35%|███▌ | 76.7M/217M [04:28<13:43, 171kiB/s]
35%|███▌ | 76.7M/217M [04:29<14:53, 157kiB/s]
35%|███▌ | 76.7M/217M [04:29<14:21, 163kiB/s]
35%|███▌ | 76.7M/217M [04:29<14:22, 163kiB/s]
35%|███▌ | 76.8M/217M [04:29<14:24, 163kiB/s]
35%|███▌ | 76.8M/217M [04:29<16:19, 143kiB/s]
35%|███▌ | 76.8M/217M [04:29<16:57, 138kiB/s]
35%|███▌ | 76.8M/217M [04:29<17:27, 134kiB/s]
35%|███▌ | 76.8M/217M [04:30<15:15, 153kiB/s]
35%|███▌ | 76.9M/217M [04:30<14:14, 164kiB/s]
35%|███▌ | 76.9M/217M [04:30<12:54, 181kiB/s]
35%|███▌ | 76.9M/217M [04:30<11:49, 198kiB/s]
35%|███▌ | 77.0M/217M [04:30<10:43, 218kiB/s]
35%|███▌ | 77.0M/217M [04:30<09:57, 235kiB/s]
35%|███▌ | 77.0M/217M [04:30<09:52, 237kiB/s]
35%|███▌ | 77.1M/217M [04:30<09:18, 251kiB/s]
35%|███▌ | 77.1M/217M [04:31<09:29, 246kiB/s]
35%|███▌ | 77.1M/217M [04:31<09:20, 250kiB/s]
36%|███▌ | 77.2M/217M [04:31<09:14, 253kiB/s]
36%|███▌ | 77.2M/217M [04:31<08:54, 262kiB/s]
36%|███▌ | 77.2M/217M [04:31<09:09, 255kiB/s]
36%|███▌ | 77.3M/217M [04:31<09:17, 251kiB/s]
36%|███▌ | 77.3M/217M [04:31<10:11, 229kiB/s]
36%|███▌ | 77.3M/217M [04:32<10:00, 233kiB/s]
36%|███▌ | 77.3M/217M [04:32<09:23, 248kiB/s]
36%|███▌ | 77.4M/217M [04:32<09:04, 257kiB/s]
36%|███▌ | 77.4M/217M [04:32<08:38, 270kiB/s]
36%|███▌ | 77.4M/217M [04:32<08:11, 284kiB/s]
36%|███▌ | 77.5M/217M [04:32<07:46, 299kiB/s]
36%|███▌ | 77.5M/217M [04:32<07:18, 318kiB/s]
36%|███▌ | 77.6M/217M [04:32<06:51, 339kiB/s]
36%|███▌ | 77.6M/217M [04:32<06:27, 360kiB/s]
36%|███▌ | 77.7M/217M [04:33<07:07, 327kiB/s]
36%|███▌ | 77.8M/217M [04:33<05:59, 388kiB/s]
36%|███▌ | 77.8M/217M [04:33<06:06, 381kiB/s]
36%|███▌ | 77.8M/217M [04:33<06:17, 369kiB/s]
36%|███▌ | 77.9M/217M [04:33<07:37, 304kiB/s]
36%|███▌ | 77.9M/217M [04:33<07:39, 303kiB/s]
36%|███▌ | 77.9M/217M [04:33<07:46, 299kiB/s]
36%|███▌ | 78.0M/217M [04:34<07:39, 303kiB/s]
36%|███▌ | 78.0M/217M [04:34<07:14, 320kiB/s]
36%|███▌ | 78.1M/217M [04:34<06:53, 337kiB/s]
36%|███▌ | 78.1M/217M [04:34<07:15, 320kiB/s]
36%|███▌ | 78.2M/217M [04:34<07:16, 319kiB/s]
36%|███▌ | 78.2M/217M [04:34<08:01, 289kiB/s]
36%|███▌ | 78.2M/217M [04:34<08:44, 265kiB/s]
36%|███▌ | 78.3M/217M [04:35<08:56, 259kiB/s]
36%|███▌ | 78.3M/217M [04:35<08:42, 266kiB/s]
36%|███▌ | 78.3M/217M [04:35<08:44, 265kiB/s]
36%|███▌ | 78.4M/217M [04:35<09:26, 245kiB/s]
36%|███▌ | 78.4M/217M [04:35<08:13, 281kiB/s]
36%|███▌ | 78.5M/217M [04:35<08:22, 276kiB/s]
36%|███▌ | 78.5M/217M [04:35<08:17, 279kiB/s]
36%|███▌ | 78.5M/217M [04:35<08:11, 282kiB/s]
36%|███▌ | 78.6M/217M [04:36<08:07, 285kiB/s]
36%|███▌ | 78.6M/217M [04:36<07:46, 297kiB/s]
36%|███▌ | 78.7M/217M [04:36<07:17, 317kiB/s]
36%|███▌ | 78.7M/217M [04:36<06:53, 335kiB/s]
36%|███▌ | 78.8M/217M [04:36<08:17, 278kiB/s]
36%|███▋ | 78.8M/217M [04:36<08:11, 282kiB/s]
36%|███▋ | 78.8M/217M [04:37<08:08, 284kiB/s]
36%|███▋ | 78.9M/217M [04:37<12:23, 186kiB/s]
36%|███▋ | 78.9M/217M [04:37<14:28, 159kiB/s]
36%|███▋ | 78.9M/217M [04:37<14:04, 164kiB/s]
36%|███▋ | 79.0M/217M [04:37<13:12, 174kiB/s]
36%|███▋ | 79.0M/217M [04:38<12:13, 189kiB/s]
36%|███▋ | 79.0M/217M [04:38<11:22, 202kiB/s]
36%|███▋ | 79.1M/217M [04:38<10:23, 222kiB/s]
36%|███▋ | 79.1M/217M [04:38<09:35, 240kiB/s]
36%|███▋ | 79.1M/217M [04:38<08:49, 261kiB/s]
36%|███▋ | 79.1M/217M [04:38<08:37, 267kiB/s]
36%|███▋ | 79.2M/217M [04:38<08:47, 262kiB/s]
36%|███▋ | 79.2M/217M [04:38<08:53, 259kiB/s]
36%|███▋ | 79.3M/217M [04:39<08:39, 266kiB/s]
36%|███▋ | 79.3M/217M [04:39<08:23, 274kiB/s]
37%|███▋ | 79.3M/217M [04:39<08:22, 275kiB/s]
37%|███▋ | 79.3M/217M [04:39<08:35, 267kiB/s]
37%|███▋ | 79.4M/217M [04:39<09:17, 247kiB/s]
37%|███▋ | 79.4M/217M [04:39<09:28, 243kiB/s]
37%|███▋ | 79.4M/217M [04:39<09:26, 243kiB/s]
37%|███▋ | 79.5M/217M [04:39<09:01, 254kiB/s]
37%|███▋ | 79.5M/217M [04:40<08:41, 264kiB/s]
37%|███▋ | 79.5M/217M [04:40<08:12, 280kiB/s]
37%|███▋ | 79.6M/217M [04:40<07:35, 302kiB/s]
37%|███▋ | 79.6M/217M [04:40<07:07, 322kiB/s]
37%|███▋ | 79.7M/217M [04:40<06:40, 343kiB/s]
37%|███▋ | 79.7M/217M [04:40<06:18, 363kiB/s]
37%|███▋ | 79.8M/217M [04:40<06:03, 378kiB/s]
37%|███▋ | 79.8M/217M [04:40<05:40, 403kiB/s]
37%|███▋ | 79.9M/217M [04:41<05:33, 412kiB/s]
37%|███▋ | 79.9M/217M [04:41<06:21, 360kiB/s]
37%|███▋ | 80.0M/217M [04:41<06:22, 359kiB/s]
37%|███▋ | 80.0M/217M [04:41<08:03, 284kiB/s]
37%|███▋ | 80.0M/217M [04:41<09:22, 244kiB/s]
37%|███▋ | 80.1M/217M [04:41<08:39, 264kiB/s]
37%|███▋ | 80.1M/217M [04:41<08:58, 255kiB/s]
37%|███▋ | 80.2M/217M [04:42<09:12, 248kiB/s]
37%|███▋ | 80.2M/217M [04:42<08:45, 261kiB/s]
37%|███▋ | 80.2M/217M [04:42<08:33, 267kiB/s]
37%|███▋ | 80.2M/217M [04:42<08:13, 278kiB/s]
37%|███▋ | 80.3M/217M [04:42<07:58, 286kiB/s]
37%|███▋ | 80.3M/217M [04:42<08:10, 279kiB/s]
37%|███▋ | 80.4M/217M [04:42<07:36, 300kiB/s]
37%|███▋ | 80.4M/217M [04:42<08:21, 273kiB/s]
37%|███▋ | 80.4M/217M [04:43<08:24, 271kiB/s]
37%|███▋ | 80.4M/217M [04:43<09:47, 233kiB/s]
37%|███▋ | 80.5M/217M [04:43<10:51, 210kiB/s]
37%|███▋ | 80.5M/217M [04:43<10:13, 223kiB/s]
37%|███▋ | 80.5M/217M [04:43<09:31, 239kiB/s]
37%|███▋ | 80.6M/217M [04:43<08:59, 253kiB/s]
37%|███▋ | 80.6M/217M [04:43<08:10, 279kiB/s]
37%|███▋ | 80.7M/217M [04:43<07:35, 300kiB/s]
37%|███▋ | 80.7M/217M [04:44<07:07, 320kiB/s]
37%|███▋ | 80.8M/217M [04:44<06:34, 346kiB/s]
37%|███▋ | 80.8M/217M [04:44<06:10, 368kiB/s]
37%|███▋ | 80.8M/217M [04:44<06:20, 359kiB/s]
37%|███▋ | 80.9M/217M [04:44<06:17, 362kiB/s]
37%|███▋ | 80.9M/217M [04:44<06:16, 362kiB/s]
37%|███▋ | 81.0M/217M [04:44<06:56, 327kiB/s]
37%|███▋ | 81.0M/217M [04:45<06:36, 343kiB/s]
37%|███▋ | 81.1M/217M [04:45<06:50, 332kiB/s]
37%|███▋ | 81.1M/217M [04:45<07:04, 321kiB/s]
37%|███▋ | 81.1M/217M [04:45<07:07, 318kiB/s]
37%|███▋ | 81.2M/217M [04:45<07:09, 317kiB/s]
37%|███▋ | 81.2M/217M [04:45<07:09, 317kiB/s]
37%|███▋ | 81.3M/217M [04:45<06:43, 337kiB/s]
37%|███▋ | 81.3M/217M [04:45<06:23, 354kiB/s]
37%|███▋ | 81.4M/217M [04:45<06:10, 367kiB/s]
37%|███▋ | 81.4M/217M [04:46<06:42, 338kiB/s]
37%|███▋ | 81.5M/217M [04:46<05:56, 381kiB/s]
38%|███▊ | 81.5M/217M [04:46<06:04, 373kiB/s]
38%|███▊ | 81.6M/217M [04:46<06:32, 346kiB/s]
38%|███▊ | 81.6M/217M [04:46<06:40, 339kiB/s]
38%|███▊ | 81.6M/217M [04:46<07:56, 285kiB/s]
38%|███▊ | 81.7M/217M [04:47<07:43, 292kiB/s]
38%|███▊ | 81.7M/217M [04:47<08:21, 270kiB/s]
38%|███▊ | 81.8M/217M [04:47<08:41, 260kiB/s]
38%|███▊ | 81.8M/217M [04:47<09:07, 248kiB/s]
38%|███▊ | 81.8M/217M [04:47<09:12, 245kiB/s]
38%|███▊ | 81.8M/217M [04:47<09:41, 233kiB/s]
38%|███▊ | 81.9M/217M [04:47<10:03, 224kiB/s]
38%|███▊ | 81.9M/217M [04:47<09:36, 235kiB/s]
38%|███▊ | 81.9M/217M [04:48<09:32, 236kiB/s]
38%|███▊ | 82.0M/217M [04:48<08:48, 256kiB/s]
38%|███▊ | 82.0M/217M [04:48<09:09, 246kiB/s]
38%|███▊ | 82.0M/217M [04:48<09:11, 245kiB/s]
38%|███▊ | 82.1M/217M [04:48<09:53, 228kiB/s]
38%|███▊ | 82.1M/217M [04:48<10:28, 215kiB/s]
38%|███▊ | 82.1M/217M [04:48<10:24, 216kiB/s]
38%|███▊ | 82.1M/217M [04:49<11:47, 191kiB/s]
38%|███▊ | 82.2M/217M [04:49<11:12, 201kiB/s]
38%|███▊ | 82.2M/217M [04:49<10:53, 207kiB/s]
38%|███▊ | 82.2M/217M [04:49<09:54, 227kiB/s]
38%|███▊ | 82.3M/217M [04:49<09:26, 238kiB/s]
38%|███▊ | 82.3M/217M [04:49<08:46, 257kiB/s]
38%|███▊ | 82.3M/217M [04:49<07:54, 284kiB/s]
38%|███▊ | 82.4M/217M [04:49<07:16, 309kiB/s]
38%|███▊ | 82.4M/217M [04:50<06:44, 333kiB/s]
38%|███▊ | 82.5M/217M [04:50<06:23, 351kiB/s]
38%|███▊ | 82.5M/217M [04:50<05:56, 378kiB/s]
38%|███▊ | 82.6M/217M [04:50<05:41, 394kiB/s]
38%|███▊ | 82.6M/217M [04:50<05:23, 416kiB/s]
38%|███▊ | 82.7M/217M [04:50<05:13, 430kiB/s]
38%|███▊ | 82.8M/217M [04:50<04:51, 461kiB/s]
38%|███▊ | 82.8M/217M [04:50<04:39, 482kiB/s]
38%|███▊ | 82.9M/217M [04:51<04:25, 506kiB/s]
38%|███▊ | 83.0M/217M [04:51<04:15, 526kiB/s]
38%|███▊ | 83.0M/217M [04:51<04:01, 557kiB/s]
38%|███▊ | 83.1M/217M [04:51<03:53, 574kiB/s]
38%|███▊ | 83.2M/217M [04:51<03:45, 595kiB/s]
38%|███▊ | 83.2M/217M [04:51<03:39, 611kiB/s]
38%|███▊ | 83.3M/217M [04:51<03:43, 599kiB/s]
38%|███▊ | 83.4M/217M [04:51<04:02, 551kiB/s]
38%|███▊ | 83.4M/217M [04:51<04:32, 491kiB/s]
38%|███▊ | 83.5M/217M [04:52<04:39, 479kiB/s]
38%|███▊ | 83.5M/217M [04:52<04:54, 454kiB/s]
38%|███▊ | 83.6M/217M [04:52<05:00, 445kiB/s]
38%|███▊ | 83.6M/217M [04:52<05:11, 430kiB/s]
39%|███▊ | 83.7M/217M [04:52<05:08, 434kiB/s]
39%|███▊ | 83.7M/217M [04:52<05:01, 443kiB/s]
39%|███▊ | 83.8M/217M [04:52<04:56, 451kiB/s]
39%|███▊ | 83.8M/217M [04:52<04:46, 466kiB/s]
39%|███▊ | 83.9M/217M [04:53<04:48, 462kiB/s]
39%|███▊ | 83.9M/217M [04:53<05:03, 440kiB/s]
39%|███▊ | 84.0M/217M [04:53<05:05, 437kiB/s]
39%|███▊ | 84.0M/217M [04:53<05:09, 430kiB/s]
39%|███▊ | 84.1M/217M [04:53<05:07, 433kiB/s]
39%|███▊ | 84.1M/217M [04:53<05:02, 441kiB/s]
39%|███▊ | 84.2M/217M [04:53<04:53, 453kiB/s]
39%|███▉ | 84.3M/217M [04:53<04:44, 468kiB/s]
39%|███▉ | 84.3M/217M [04:53<04:48, 461kiB/s]
39%|███▉ | 84.4M/217M [04:54<04:58, 446kiB/s]
39%|███▉ | 84.4M/217M [04:54<05:12, 425kiB/s]
39%|███▉ | 84.5M/217M [04:54<05:05, 435kiB/s]
39%|███▉ | 84.5M/217M [04:54<05:05, 434kiB/s]
39%|███▉ | 84.6M/217M [04:54<05:18, 416kiB/s]
39%|███▉ | 84.6M/217M [04:54<06:04, 364kiB/s]
39%|███▉ | 84.6M/217M [04:54<06:32, 338kiB/s]
39%|███▉ | 84.7M/217M [04:55<08:05, 273kiB/s]
39%|███▉ | 84.7M/217M [04:55<08:10, 270kiB/s]
39%|███▉ | 84.7M/217M [04:55<09:26, 234kiB/s]
39%|███▉ | 84.8M/217M [04:55<09:55, 223kiB/s]
39%|███▉ | 84.8M/217M [04:55<09:52, 223kiB/s]
39%|███▉ | 84.8M/217M [04:55<09:26, 234kiB/s]
39%|███▉ | 84.9M/217M [04:55<09:51, 224kiB/s]
39%|███▉ | 84.9M/217M [04:56<09:59, 221kiB/s]
39%|███▉ | 84.9M/217M [04:56<10:07, 218kiB/s]
39%|███▉ | 85.0M/217M [04:56<09:57, 221kiB/s]
39%|███▉ | 85.0M/217M [04:56<09:48, 225kiB/s]
39%|███▉ | 85.0M/217M [04:56<09:10, 240kiB/s]
39%|███▉ | 85.1M/217M [04:56<08:56, 247kiB/s]
39%|███▉ | 85.1M/217M [04:56<08:27, 261kiB/s]
39%|███▉ | 85.1M/217M [04:56<07:40, 287kiB/s]
39%|███▉ | 85.2M/217M [04:57<07:06, 310kiB/s]
39%|███▉ | 85.2M/217M [04:57<06:39, 331kiB/s]
39%|███▉ | 85.3M/217M [04:57<06:14, 352kiB/s]
39%|███▉ | 85.3M/217M [04:57<05:53, 373kiB/s]
39%|███▉ | 85.4M/217M [04:57<05:27, 403kiB/s]
39%|███▉ | 85.4M/217M [04:57<05:16, 417kiB/s]
39%|███▉ | 85.5M/217M [04:57<04:58, 441kiB/s]
39%|███▉ | 85.6M/217M [04:57<04:43, 465kiB/s]
39%|███▉ | 85.6M/217M [04:58<04:28, 490kiB/s]
39%|███▉ | 85.7M/217M [04:58<04:14, 516kiB/s]
39%|███▉ | 85.8M/217M [04:58<04:05, 536kiB/s]
39%|███▉ | 85.8M/217M [04:58<03:55, 557kiB/s]
40%|███▉ | 85.9M/217M [04:58<03:54, 560kiB/s]
40%|███▉ | 85.9M/217M [04:58<04:06, 532kiB/s]
40%|███▉ | 86.0M/217M [04:58<04:10, 525kiB/s]
40%|███▉ | 86.0M/217M [04:58<04:30, 485kiB/s]
40%|███▉ | 86.1M/217M [04:58<04:30, 485kiB/s]
40%|███▉ | 86.2M/217M [04:59<04:24, 496kiB/s]
40%|███▉ | 86.2M/217M [04:59<04:40, 467kiB/s]
40%|███▉ | 86.3M/217M [04:59<04:38, 470kiB/s]
40%|███▉ | 86.3M/217M [04:59<04:47, 455kiB/s]
40%|███▉ | 86.4M/217M [04:59<05:00, 436kiB/s]
40%|███▉ | 86.4M/217M [04:59<05:37, 387kiB/s]
40%|███▉ | 86.5M/217M [04:59<05:24, 404kiB/s]
40%|███▉ | 86.5M/217M [05:00<05:44, 379kiB/s]
40%|███▉ | 86.6M/217M [05:00<05:48, 375kiB/s]
40%|███▉ | 86.6M/217M [05:00<06:15, 348kiB/s]
40%|███▉ | 86.6M/217M [05:00<06:49, 319kiB/s]
40%|███▉ | 86.7M/217M [05:00<07:11, 303kiB/s]
40%|███▉ | 86.7M/217M [05:00<07:24, 294kiB/s]
40%|███▉ | 86.7M/217M [05:00<07:29, 290kiB/s]
40%|███▉ | 86.8M/217M [05:00<07:46, 280kiB/s]
40%|███▉ | 86.8M/217M [05:01<08:47, 248kiB/s]
40%|███▉ | 86.8M/217M [05:01<08:55, 244kiB/s]
40%|███▉ | 86.9M/217M [05:01<08:51, 245kiB/s]
40%|███▉ | 86.9M/217M [05:01<08:45, 248kiB/s]
40%|████ | 86.9M/217M [05:01<08:19, 261kiB/s]
40%|████ | 87.0M/217M [05:01<07:58, 272kiB/s]
40%|████ | 87.0M/217M [05:01<07:39, 284kiB/s]
40%|████ | 87.0M/217M [05:01<07:21, 295kiB/s]
40%|████ | 87.1M/217M [05:02<07:05, 306kiB/s]
40%|████ | 87.1M/217M [05:02<07:23, 294kiB/s]
40%|████ | 87.1M/217M [05:02<07:32, 288kiB/s]
40%|████ | 87.2M/217M [05:02<07:25, 292kiB/s]
40%|████ | 87.2M/217M [05:02<07:12, 301kiB/s]
40%|████ | 87.2M/217M [05:02<07:07, 304kiB/s]
40%|████ | 87.3M/217M [05:02<06:44, 321kiB/s]
40%|████ | 87.3M/217M [05:02<06:27, 336kiB/s]
40%|████ | 87.4M/217M [05:02<06:07, 354kiB/s]
40%|████ | 87.4M/217M [05:03<05:44, 377kiB/s]
40%|████ | 87.5M/217M [05:03<05:32, 391kiB/s]
40%|████ | 87.5M/217M [05:03<05:12, 415kiB/s]
40%|████ | 87.6M/217M [05:03<05:26, 397kiB/s]
40%|████ | 87.6M/217M [05:03<05:19, 406kiB/s]
40%|████ | 87.7M/217M [05:03<05:22, 402kiB/s]
40%|████ | 87.7M/217M [05:03<05:20, 404kiB/s]
40%|████ | 87.8M/217M [05:03<05:18, 407kiB/s]
40%|████ | 87.8M/217M [05:04<06:11, 348kiB/s]
40%|████ | 87.9M/217M [05:04<05:22, 401kiB/s]
40%|████ | 87.9M/217M [05:04<05:34, 387kiB/s]
40%|████ | 88.0M/217M [05:04<05:40, 380kiB/s]
41%|████ | 88.0M/217M [05:04<05:41, 379kiB/s]
41%|████ | 88.1M/217M [05:04<05:32, 389kiB/s]
41%|████ | 88.1M/217M [05:04<05:25, 397kiB/s]
41%|████ | 88.2M/217M [05:04<05:14, 410kiB/s]
41%|████ | 88.2M/217M [05:05<05:03, 425kiB/s]
41%|████ | 88.3M/217M [05:05<04:48, 447kiB/s]
41%|████ | 88.4M/217M [05:05<04:33, 471kiB/s]
41%|████ | 88.4M/217M [05:05<04:58, 431kiB/s]
41%|████ | 88.5M/217M [05:05<04:30, 476kiB/s]
41%|████ | 88.6M/217M [05:05<04:50, 443kiB/s]
41%|████ | 88.6M/217M [05:05<05:03, 424kiB/s]
41%|████ | 88.6M/217M [05:06<05:50, 367kiB/s]
41%|████ | 88.7M/217M [05:06<05:47, 370kiB/s]
41%|████ | 88.7M/217M [05:06<07:33, 284kiB/s]
41%|████ | 88.8M/217M [05:06<06:40, 321kiB/s]
41%|████ | 88.8M/217M [05:06<07:27, 287kiB/s]
41%|████ | 88.8M/217M [05:06<07:25, 288kiB/s]
41%|████ | 88.9M/217M [05:06<08:02, 266kiB/s]
41%|████ | 88.9M/217M [05:07<08:23, 255kiB/s]
41%|████ | 88.9M/217M [05:07<08:18, 257kiB/s]
41%|████ | 89.0M/217M [05:07<08:03, 266kiB/s]
41%|████ | 89.0M/217M [05:07<07:37, 280kiB/s]
41%|████ | 89.0M/217M [05:07<07:21, 290kiB/s]
41%|████ | 89.1M/217M [05:07<07:08, 299kiB/s]
41%|████ | 89.1M/217M [05:07<07:07, 300kiB/s]
41%|████ | 89.2M/217M [05:07<06:59, 306kiB/s]
41%|████ | 89.2M/217M [05:08<07:06, 300kiB/s]
41%|████ | 89.2M/217M [05:08<06:55, 308kiB/s]
41%|████ | 89.3M/217M [05:08<06:50, 312kiB/s]
41%|████ | 89.3M/217M [05:08<06:39, 321kiB/s]
41%|████ | 89.4M/217M [05:08<06:22, 334kiB/s]
41%|████ | 89.4M/217M [05:08<06:03, 352kiB/s]
41%|████ | 89.5M/217M [05:08<05:50, 364kiB/s]
41%|████ | 89.5M/217M [05:08<05:32, 384kiB/s]
41%|████ | 89.6M/217M [05:09<05:12, 409kiB/s]
41%|████ | 89.6M/217M [05:09<05:02, 422kiB/s]
41%|████▏ | 89.7M/217M [05:09<05:38, 377kiB/s]
41%|████▏ | 89.7M/217M [05:09<05:00, 425kiB/s]
41%|████▏ | 89.8M/217M [05:09<05:10, 411kiB/s]
41%|████▏ | 89.8M/217M [05:09<05:14, 406kiB/s]
41%|████▏ | 89.9M/217M [05:09<06:00, 354kiB/s]
41%|████▏ | 89.9M/217M [05:10<06:12, 342kiB/s]
41%|████▏ | 90.0M/217M [05:10<06:18, 337kiB/s]
41%|████▏ | 90.0M/217M [05:10<06:51, 309kiB/s]
41%|████▏ | 90.0M/217M [05:10<07:31, 282kiB/s]
41%|████▏ | 90.1M/217M [05:10<08:41, 244kiB/s]
41%|████▏ | 90.1M/217M [05:10<08:53, 239kiB/s]
41%|████▏ | 90.1M/217M [05:10<09:12, 230kiB/s]
41%|████▏ | 90.2M/217M [05:11<09:17, 228kiB/s]
42%|████▏ | 90.2M/217M [05:11<08:55, 237kiB/s]
42%|████▏ | 90.2M/217M [05:11<08:43, 243kiB/s]
42%|████▏ | 90.3M/217M [05:11<08:25, 252kiB/s]
42%|████▏ | 90.3M/217M [05:11<07:56, 267kiB/s]
42%|████▏ | 90.3M/217M [05:11<07:10, 295kiB/s]
42%|████▏ | 90.4M/217M [05:11<08:18, 255kiB/s]
42%|████▏ | 90.4M/217M [05:11<07:03, 299kiB/s]
42%|████▏ | 90.5M/217M [05:12<07:56, 266kiB/s]
42%|████▏ | 90.5M/217M [05:12<08:45, 241kiB/s]
42%|████▏ | 90.5M/217M [05:12<08:55, 237kiB/s]
42%|████▏ | 90.6M/217M [05:12<09:01, 234kiB/s]
42%|████▏ | 90.6M/217M [05:12<08:46, 241kiB/s]
42%|████▏ | 90.6M/217M [05:12<08:26, 250kiB/s]
42%|████▏ | 90.7M/217M [05:12<08:08, 259kiB/s]
42%|████▏ | 90.7M/217M [05:13<07:42, 274kiB/s]
42%|████▏ | 90.7M/217M [05:13<07:00, 301kiB/s]
42%|████▏ | 90.8M/217M [05:13<06:33, 322kiB/s]
42%|████▏ | 90.8M/217M [05:13<06:13, 338kiB/s]
42%|████▏ | 90.9M/217M [05:13<05:45, 366kiB/s]
42%|████▏ | 90.9M/217M [05:13<05:50, 360kiB/s]
42%|████▏ | 91.0M/217M [05:13<06:06, 345kiB/s]
42%|████▏ | 91.0M/217M [05:13<05:58, 352kiB/s]
42%|████▏ | 91.1M/217M [05:14<06:06, 344kiB/s]
42%|████▏ | 91.1M/217M [05:14<06:22, 330kiB/s]
42%|████▏ | 91.1M/217M [05:14<06:43, 312kiB/s]
42%|████▏ | 91.2M/217M [05:14<06:48, 308kiB/s]
42%|████▏ | 91.2M/217M [05:14<07:05, 296kiB/s]
42%|████▏ | 91.2M/217M [05:14<08:06, 259kiB/s]
42%|████▏ | 91.3M/217M [05:14<07:30, 280kiB/s]
42%|████▏ | 91.3M/217M [05:14<07:37, 276kiB/s]
42%|████▏ | 91.3M/217M [05:15<07:46, 270kiB/s]
42%|████▏ | 91.4M/217M [05:15<07:25, 283kiB/s]
42%|████▏ | 91.4M/217M [05:15<07:28, 281kiB/s]
42%|████▏ | 91.5M/217M [05:15<07:05, 296kiB/s]
42%|████▏ | 91.5M/217M [05:15<07:20, 286kiB/s]
42%|████▏ | 91.6M/217M [05:15<06:52, 305kiB/s]
42%|████▏ | 91.6M/217M [05:15<06:59, 300kiB/s]
42%|████▏ | 91.6M/217M [05:16<07:00, 299kiB/s]
42%|████▏ | 91.7M/217M [05:16<06:53, 304kiB/s]
42%|████▏ | 91.7M/217M [05:16<07:41, 272kiB/s]
42%|████▏ | 91.7M/217M [05:16<07:16, 288kiB/s]
42%|████▏ | 91.8M/217M [05:16<07:36, 275kiB/s]
42%|████▏ | 91.8M/217M [05:16<07:40, 272kiB/s]
42%|████▏ | 91.8M/217M [05:16<07:43, 270kiB/s]
42%|████▏ | 91.9M/217M [05:16<07:52, 266kiB/s]
42%|████▏ | 91.9M/217M [05:17<08:40, 241kiB/s]
42%|████▏ | 91.9M/217M [05:17<08:26, 248kiB/s]
42%|████▏ | 91.9M/217M [05:17<08:17, 252kiB/s]
42%|████▏ | 92.0M/217M [05:17<08:10, 255kiB/s]
42%|████▏ | 92.0M/217M [05:17<07:56, 263kiB/s]
42%|████▏ | 92.1M/217M [05:17<07:18, 286kiB/s]
42%|████▏ | 92.1M/217M [05:17<06:50, 305kiB/s]
42%|████▏ | 92.2M/217M [05:17<06:21, 328kiB/s]
42%|████▏ | 92.2M/217M [05:18<05:58, 349kiB/s]
42%|████▏ | 92.3M/217M [05:18<05:45, 362kiB/s]
42%|████▏ | 92.3M/217M [05:18<05:20, 390kiB/s]
43%|████▎ | 92.4M/217M [05:18<05:08, 405kiB/s]
43%|████▎ | 92.4M/217M [05:18<04:54, 425kiB/s]
43%|████▎ | 92.5M/217M [05:18<04:37, 449kiB/s]
43%|████▎ | 92.5M/217M [05:18<04:23, 473kiB/s]
43%|████▎ | 92.6M/217M [05:18<04:10, 498kiB/s]
43%|████▎ | 92.7M/217M [05:19<04:21, 476kiB/s]
43%|████▎ | 92.7M/217M [05:19<04:13, 492kiB/s]
43%|████▎ | 92.8M/217M [05:19<04:12, 493kiB/s]
43%|████▎ | 92.8M/217M [05:19<04:14, 488kiB/s]
43%|████▎ | 92.9M/217M [05:19<04:15, 487kiB/s]
43%|████▎ | 92.9M/217M [05:19<04:11, 494kiB/s]
43%|████▎ | 93.0M/217M [05:19<04:06, 504kiB/s]
43%|████▎ | 93.1M/217M [05:19<03:59, 520kiB/s]
43%|████▎ | 93.1M/217M [05:19<03:54, 529kiB/s]
43%|████▎ | 93.2M/217M [05:20<03:49, 540kiB/s]
43%|████▎ | 93.3M/217M [05:20<03:39, 564kiB/s]
43%|████▎ | 93.3M/217M [05:20<03:36, 572kiB/s]
43%|████▎ | 93.4M/217M [05:20<03:29, 592kiB/s]
43%|████▎ | 93.5M/217M [05:20<03:25, 603kiB/s]
43%|████▎ | 93.5M/217M [05:20<03:42, 556kiB/s]
43%|████▎ | 93.6M/217M [05:20<03:41, 558kiB/s]
43%|████▎ | 93.7M/217M [05:20<04:02, 509kiB/s]
43%|████▎ | 93.7M/217M [05:21<04:27, 462kiB/s]
43%|████▎ | 93.8M/217M [05:21<04:49, 427kiB/s]
43%|████▎ | 93.8M/217M [05:21<05:01, 410kiB/s]
43%|████▎ | 93.8M/217M [05:21<05:04, 406kiB/s]
43%|████▎ | 93.9M/217M [05:21<04:55, 417kiB/s]
43%|████▎ | 93.9M/217M [05:21<04:51, 423kiB/s]
43%|████▎ | 94.0M/217M [05:21<04:42, 437kiB/s]
43%|████▎ | 94.0M/217M [05:21<05:08, 399kiB/s]
43%|████▎ | 94.1M/217M [05:22<04:44, 433kiB/s]
43%|████▎ | 94.2M/217M [05:22<04:55, 416kiB/s]
43%|████▎ | 94.2M/217M [05:22<04:56, 416kiB/s]
43%|████▎ | 94.3M/217M [05:22<04:56, 415kiB/s]
43%|████▎ | 94.3M/217M [05:22<04:51, 422kiB/s]
43%|████▎ | 94.4M/217M [05:22<04:41, 436kiB/s]
43%|████▎ | 94.4M/217M [05:22<04:36, 445kiB/s]
43%|████▎ | 94.5M/217M [05:22<04:31, 453kiB/s]
43%|████▎ | 94.5M/217M [05:22<04:20, 471kiB/s]
44%|████▎ | 94.6M/217M [05:23<04:08, 493kiB/s]
44%|████▎ | 94.7M/217M [05:23<04:00, 509kiB/s]
44%|████▎ | 94.7M/217M [05:23<03:53, 526kiB/s]
44%|████▎ | 94.8M/217M [05:23<03:44, 546kiB/s]
44%|████▎ | 94.8M/217M [05:23<03:34, 571kiB/s]
44%|████▎ | 94.9M/217M [05:23<03:28, 587kiB/s]
44%|████▎ | 95.0M/217M [05:23<03:18, 616kiB/s]
44%|████▍ | 95.1M/217M [05:23<03:11, 639kiB/s]
44%|████▍ | 95.2M/217M [05:23<03:06, 655kiB/s]
44%|████▍ | 95.2M/217M [05:24<02:59, 679kiB/s]
44%|████▍ | 95.3M/217M [05:24<02:54, 701kiB/s]
44%|████▍ | 95.4M/217M [05:24<02:48, 722kiB/s]
44%|████▍ | 95.5M/217M [05:24<02:43, 747kiB/s]
44%|████▍ | 95.6M/217M [05:24<02:43, 746kiB/s]
44%|████▍ | 95.6M/217M [05:24<02:52, 705kiB/s]
44%|████▍ | 95.7M/217M [05:24<02:57, 686kiB/s]
44%|████▍ | 95.8M/217M [05:24<03:10, 638kiB/s]
44%|████▍ | 95.8M/217M [05:24<03:18, 613kiB/s]
44%|████▍ | 95.9M/217M [05:25<03:38, 555kiB/s]
44%|████▍ | 96.0M/217M [05:25<03:57, 511kiB/s]
44%|████▍ | 96.0M/217M [05:25<04:02, 500kiB/s]
44%|████▍ | 96.1M/217M [05:25<04:00, 505kiB/s]
44%|████▍ | 96.2M/217M [05:25<03:55, 515kiB/s]
44%|████▍ | 96.2M/217M [05:25<03:47, 533kiB/s]
44%|████▍ | 96.3M/217M [05:25<03:41, 547kiB/s]
44%|████▍ | 96.4M/217M [05:25<03:34, 564kiB/s]
44%|████▍ | 96.4M/217M [05:26<03:44, 539kiB/s]
44%|████▍ | 96.5M/217M [05:26<03:40, 548kiB/s]
44%|████▍ | 96.5M/217M [05:26<03:41, 546kiB/s]
44%|████▍ | 96.6M/217M [05:26<03:42, 543kiB/s]
44%|████▍ | 96.7M/217M [05:26<03:57, 508kiB/s]
45%|████▍ | 96.7M/217M [05:26<03:52, 518kiB/s]
45%|████▍ | 96.8M/217M [05:26<03:47, 530kiB/s]
45%|████▍ | 96.8M/217M [05:26<03:40, 547kiB/s]
45%|████▍ | 96.9M/217M [05:27<03:36, 557kiB/s]
45%|████▍ | 97.0M/217M [05:27<03:32, 567kiB/s]
45%|████▍ | 97.0M/217M [05:27<03:24, 587kiB/s]
45%|████▍ | 97.1M/217M [05:27<03:19, 603kiB/s]
45%|████▍ | 97.2M/217M [05:27<03:11, 627kiB/s]
45%|████▍ | 97.3M/217M [05:27<03:06, 643kiB/s]
45%|████▍ | 97.4M/217M [05:27<03:02, 657kiB/s]
45%|████▍ | 97.4M/217M [05:27<03:20, 598kiB/s]
45%|████▍ | 97.5M/217M [05:27<03:00, 662kiB/s]
45%|████▍ | 97.6M/217M [05:28<03:10, 627kiB/s]
45%|████▍ | 97.7M/217M [05:28<03:13, 619kiB/s]
45%|████▍ | 97.7M/217M [05:28<03:16, 607kiB/s]
45%|████▌ | 97.8M/217M [05:28<03:15, 610kiB/s]
45%|████▌ | 97.9M/217M [05:28<03:11, 622kiB/s]
45%|████▌ | 97.9M/217M [05:28<03:09, 629kiB/s]
45%|████▌ | 98.0M/217M [05:28<03:07, 636kiB/s]
45%|████▌ | 98.1M/217M [05:28<03:02, 653kiB/s]
45%|████▌ | 98.2M/217M [05:28<02:59, 664kiB/s]
45%|████▌ | 98.3M/217M [05:29<02:55, 678kiB/s]
45%|████▌ | 98.3M/217M [05:29<02:51, 693kiB/s]
45%|████▌ | 98.4M/217M [05:29<02:48, 705kiB/s]
45%|████▌ | 98.5M/217M [05:29<02:43, 726kiB/s]
45%|████▌ | 98.6M/217M [05:29<02:39, 744kiB/s]
45%|████▌ | 98.7M/217M [05:29<02:53, 684kiB/s]
45%|████▌ | 98.7M/217M [05:29<02:42, 731kiB/s]
45%|████▌ | 98.8M/217M [05:29<02:44, 719kiB/s]
46%|████▌ | 98.9M/217M [05:30<03:22, 583kiB/s]
46%|████▌ | 99.0M/217M [05:30<03:12, 615kiB/s]
46%|████▌ | 99.0M/217M [05:30<03:51, 510kiB/s]
46%|████▌ | 99.1M/217M [05:30<04:06, 479kiB/s]
46%|████▌ | 99.1M/217M [05:30<04:19, 455kiB/s]
46%|████▌ | 99.2M/217M [05:30<04:30, 436kiB/s]
46%|████▌ | 99.2M/217M [05:30<04:57, 397kiB/s]
46%|████▌ | 99.3M/217M [05:31<05:10, 380kiB/s]
46%|████▌ | 99.3M/217M [05:31<05:41, 346kiB/s]
46%|████▌ | 99.4M/217M [05:31<05:53, 333kiB/s]
46%|████▌ | 99.4M/217M [05:31<05:43, 343kiB/s]
46%|████▌ | 99.5M/217M [05:31<05:32, 354kiB/s]
46%|████▌ | 99.5M/217M [05:31<05:26, 361kiB/s]
46%|████▌ | 99.6M/217M [05:31<05:13, 376kiB/s]
46%|████▌ | 99.6M/217M [05:31<04:58, 394kiB/s]
46%|████▌ | 99.7M/217M [05:32<04:44, 414kiB/s]
46%|████▌ | 99.7M/217M [05:32<04:43, 415kiB/s]
46%|████▌ | 99.8M/217M [05:32<04:44, 412kiB/s]
46%|████▌ | 99.8M/217M [05:32<05:12, 376kiB/s]
46%|████▌ | 99.8M/217M [05:32<05:19, 368kiB/s]
46%|████▌ | 99.9M/217M [05:32<05:12, 376kiB/s]
46%|████▌ | 99.9M/217M [05:32<05:34, 351kiB/s]
46%|████▌ | 100M/217M [05:32<05:07, 381kiB/s]
46%|████▌ | 100M/217M [05:33<05:08, 380kiB/s]
46%|████▌ | 100M/217M [05:33<05:38, 346kiB/s]
46%|████▌ | 100M/217M [05:33<05:39, 345kiB/s]
46%|████▌ | 100M/217M [05:33<05:33, 351kiB/s]
46%|████▌ | 100M/217M [05:33<05:14, 372kiB/s]
46%|████▌ | 100M/217M [05:33<05:05, 383kiB/s]
46%|████▌ | 100M/217M [05:33<04:54, 398kiB/s]
46%|████▌ | 100M/217M [05:33<04:41, 416kiB/s]
46%|████▌ | 100M/217M [05:34<04:29, 434kiB/s]
46%|████▌ | 100M/217M [05:34<04:25, 440kiB/s]
46%|████▋ | 101M/217M [05:34<04:10, 465kiB/s]
46%|████▋ | 101M/217M [05:34<03:54, 497kiB/s]
46%|████▋ | 101M/217M [05:34<03:55, 496kiB/s]
46%|████▋ | 101M/217M [05:34<04:15, 456kiB/s]
46%|████▋ | 101M/217M [05:34<04:25, 440kiB/s]
46%|████▋ | 101M/217M [05:34<04:31, 429kiB/s]
46%|████▋ | 101M/217M [05:34<04:21, 445kiB/s]
46%|████▋ | 101M/217M [05:35<04:19, 448kiB/s]
46%|████▋ | 101M/217M [05:35<04:15, 455kiB/s]
46%|████▋ | 101M/217M [05:35<04:10, 464kiB/s]
46%|████▋ | 101M/217M [05:35<04:13, 458kiB/s]
47%|████▋ | 101M/217M [05:35<04:38, 417kiB/s]
47%|████▋ | 101M/217M [05:35<04:54, 394kiB/s]
47%|████▋ | 101M/217M [05:35<04:59, 388kiB/s]
47%|████▋ | 101M/217M [05:35<04:54, 395kiB/s]
47%|████▋ | 101M/217M [05:35<04:45, 407kiB/s]
47%|████▋ | 101M/217M [05:36<04:33, 425kiB/s]
47%|████▋ | 101M/217M [05:36<04:21, 443kiB/s]
47%|████▋ | 101M/217M [05:36<04:15, 454kiB/s]
47%|████▋ | 101M/217M [05:36<04:02, 478kiB/s]
47%|████▋ | 102M/217M [05:36<03:50, 502kiB/s]
47%|████▋ | 102M/217M [05:36<03:42, 519kiB/s]
47%|████▋ | 102M/217M [05:36<03:32, 545kiB/s]
47%|████▋ | 102M/217M [05:36<03:24, 565kiB/s]
47%|████▋ | 102M/217M [05:36<03:18, 582kiB/s]
47%|████▋ | 102M/217M [05:37<03:11, 602kiB/s]
47%|████▋ | 102M/217M [05:37<03:03, 627kiB/s]
47%|████▋ | 102M/217M [05:37<02:58, 644kiB/s]
47%|████▋ | 102M/217M [05:37<02:51, 673kiB/s]
47%|████▋ | 102M/217M [05:37<02:45, 697kiB/s]
47%|████▋ | 102M/217M [05:37<02:41, 710kiB/s]
47%|████▋ | 102M/217M [05:37<02:36, 735kiB/s]
47%|████▋ | 102M/217M [05:37<02:33, 747kiB/s]
47%|████▋ | 103M/217M [05:37<02:28, 773kiB/s]
47%|████▋ | 103M/217M [05:38<02:24, 793kiB/s]
47%|████▋ | 103M/217M [05:38<02:19, 823kiB/s]
47%|████▋ | 103M/217M [05:38<02:14, 848kiB/s]
47%|████▋ | 103M/217M [05:38<02:11, 871kiB/s]
47%|████▋ | 103M/217M [05:38<02:08, 890kiB/s]
47%|████▋ | 103M/217M [05:38<02:24, 793kiB/s]
48%|████▊ | 103M/217M [05:38<02:25, 785kiB/s]
48%|████▊ | 103M/217M [05:38<02:24, 790kiB/s]
48%|████▊ | 103M/217M [05:39<02:31, 749kiB/s]
48%|████▊ | 104M/217M [05:39<02:54, 652kiB/s]
48%|████▊ | 104M/217M [05:39<02:59, 632kiB/s]
48%|████▊ | 104M/217M [05:39<03:04, 616kiB/s]
48%|████▊ | 104M/217M [05:39<03:09, 601kiB/s]
48%|████▊ | 104M/217M [05:39<03:14, 584kiB/s]
48%|████▊ | 104M/217M [05:39<03:11, 593kiB/s]
48%|████▊ | 104M/217M [05:39<03:04, 616kiB/s]
48%|████▊ | 104M/217M [05:39<03:01, 623kiB/s]
48%|████▊ | 104M/217M [05:40<02:53, 651kiB/s]
48%|████▊ | 104M/217M [05:40<02:48, 672kiB/s]
48%|████▊ | 104M/217M [05:40<02:44, 689kiB/s]
48%|████▊ | 104M/217M [05:40<02:39, 710kiB/s]
48%|████▊ | 104M/217M [05:40<02:48, 671kiB/s]
48%|████▊ | 104M/217M [05:40<02:45, 682kiB/s]
48%|████▊ | 105M/217M [05:40<02:49, 666kiB/s]
48%|████▊ | 105M/217M [05:40<03:02, 617kiB/s]
48%|████▊ | 105M/217M [05:41<03:16, 572kiB/s]
48%|████▊ | 105M/217M [05:41<03:18, 567kiB/s]
48%|████▊ | 105M/217M [05:41<03:38, 515kiB/s]
48%|████▊ | 105M/217M [05:41<03:41, 507kiB/s]
48%|████▊ | 105M/217M [05:41<03:49, 489kiB/s]
48%|████▊ | 105M/217M [05:41<03:44, 500kiB/s]
48%|████▊ | 105M/217M [05:41<03:37, 516kiB/s]
48%|████▊ | 105M/217M [05:41<03:32, 529kiB/s]
48%|████▊ | 105M/217M [05:42<03:24, 548kiB/s]
48%|████▊ | 105M/217M [05:42<03:18, 565kiB/s]
48%|████▊ | 105M/217M [05:42<03:12, 583kiB/s]
48%|████▊ | 105M/217M [05:42<03:07, 598kiB/s]
49%|████▊ | 105M/217M [05:42<03:00, 619kiB/s]
49%|████▊ | 106M/217M [05:42<02:58, 625kiB/s]
49%|████▊ | 106M/217M [05:42<02:59, 621kiB/s]
49%|████▊ | 106M/217M [05:42<03:11, 582kiB/s]
49%|████▊ | 106M/217M [05:42<03:17, 564kiB/s]
49%|████▊ | 106M/217M [05:43<03:20, 555kiB/s]
49%|████▊ | 106M/217M [05:43<03:12, 578kiB/s]
49%|████▊ | 106M/217M [05:43<03:11, 582kiB/s]
49%|████▉ | 106M/217M [05:43<03:32, 523kiB/s]
49%|████▉ | 106M/217M [05:43<03:21, 553kiB/s]
49%|████▉ | 106M/217M [05:43<03:25, 540kiB/s]
49%|████▉ | 106M/217M [05:43<03:29, 529kiB/s]
49%|████▉ | 106M/217M [05:43<03:51, 480kiB/s]
49%|████▉ | 106M/217M [05:44<03:49, 484kiB/s]
49%|████▉ | 106M/217M [05:44<03:43, 496kiB/s]
49%|████▉ | 106M/217M [05:44<03:35, 515kiB/s]
49%|████▉ | 106M/217M [05:44<03:30, 526kiB/s]
49%|████▉ | 107M/217M [05:44<03:23, 543kiB/s]
49%|████▉ | 107M/217M [05:44<03:16, 563kiB/s]
49%|████▉ | 107M/217M [05:44<03:08, 586kiB/s]
49%|████▉ | 107M/217M [05:44<03:01, 609kiB/s]
49%|████▉ | 107M/217M [05:44<02:54, 632kiB/s]
49%|████▉ | 107M/217M [05:45<02:46, 664kiB/s]
49%|████▉ | 107M/217M [05:45<02:56, 625kiB/s]
49%|████▉ | 107M/217M [05:45<02:52, 638kiB/s]
49%|████▉ | 107M/217M [05:45<03:03, 602kiB/s]
49%|████▉ | 107M/217M [05:45<03:01, 607kiB/s]
49%|████▉ | 107M/217M [05:45<03:02, 602kiB/s]
49%|████▉ | 107M/217M [05:45<03:08, 584kiB/s]
49%|████▉ | 107M/217M [05:45<03:18, 554kiB/s]
49%|████▉ | 107M/217M [05:46<03:38, 503kiB/s]
49%|████▉ | 107M/217M [05:46<04:36, 397kiB/s]
49%|████▉ | 108M/217M [05:46<05:05, 359kiB/s]
50%|████▉ | 108M/217M [05:46<05:23, 339kiB/s]
50%|████▉ | 108M/217M [05:46<06:07, 298kiB/s]
50%|████▉ | 108M/217M [05:46<06:14, 293kiB/s]
50%|████▉ | 108M/217M [05:46<06:06, 299kiB/s]
50%|████▉ | 108M/217M [05:47<05:44, 318kiB/s]
50%|████▉ | 108M/217M [05:47<05:29, 332kiB/s]
50%|████▉ | 108M/217M [05:47<05:12, 350kiB/s]
50%|████▉ | 108M/217M [05:47<04:52, 374kiB/s]
50%|████▉ | 108M/217M [05:47<04:38, 392kiB/s]
50%|████▉ | 108M/217M [05:47<04:21, 417kiB/s]
50%|████▉ | 108M/217M [05:47<04:10, 436kiB/s]
50%|████▉ | 108M/217M [05:47<04:01, 452kiB/s]
50%|████▉ | 108M/217M [05:48<03:45, 484kiB/s]
50%|████▉ | 108M/217M [05:48<03:35, 507kiB/s]
50%|████▉ | 108M/217M [05:48<03:40, 495kiB/s]
50%|████▉ | 108M/217M [05:48<03:52, 468kiB/s]
50%|████▉ | 108M/217M [05:48<03:57, 459kiB/s]
50%|████▉ | 108M/217M [05:48<03:58, 456kiB/s]
50%|████▉ | 108M/217M [05:48<03:53, 465kiB/s]
50%|████▉ | 109M/217M [05:48<03:50, 472kiB/s]
50%|████▉ | 109M/217M [05:48<03:45, 482kiB/s]
50%|█████ | 109M/217M [05:49<03:38, 497kiB/s]
50%|█████ | 109M/217M [05:49<03:34, 507kiB/s]
50%|█████ | 109M/217M [05:49<03:26, 525kiB/s]
50%|█████ | 109M/217M [05:49<03:48, 474kiB/s]
50%|█████ | 109M/217M [05:49<03:31, 511kiB/s]
50%|█████ | 109M/217M [05:49<03:38, 496kiB/s]
50%|█████ | 109M/217M [05:49<03:40, 492kiB/s]
50%|█████ | 109M/217M [05:50<03:39, 493kiB/s]
50%|█████ | 109M/217M [05:50<03:36, 499kiB/s]
50%|█████ | 109M/217M [05:50<03:33, 505kiB/s]
50%|█████ | 109M/217M [05:50<03:27, 522kiB/s]
50%|█████ | 109M/217M [05:50<03:22, 533kiB/s]
50%|█████ | 109M/217M [05:50<03:14, 553kiB/s]
50%|█████ | 110M/217M [05:50<03:10, 565kiB/s]
50%|█████ | 110M/217M [05:50<03:03, 586kiB/s]
50%|█████ | 110M/217M [05:50<02:54, 617kiB/s]
50%|█████ | 110M/217M [05:51<02:56, 610kiB/s]
51%|█████ | 110M/217M [05:51<02:59, 599kiB/s]
51%|█████ | 110M/217M [05:51<03:18, 543kiB/s]
51%|█████ | 110M/217M [05:51<03:35, 499kiB/s]
51%|█████ | 110M/217M [05:51<03:48, 471kiB/s]
51%|█████ | 110M/217M [05:51<03:59, 448kiB/s]
51%|█████ | 110M/217M [05:51<04:06, 435kiB/s]
51%|█████ | 110M/217M [05:51<04:07, 432kiB/s]
51%|█████ | 110M/217M [05:52<04:14, 421kiB/s]
51%|█████ | 110M/217M [05:52<04:00, 446kiB/s]
51%|█████ | 110M/217M [05:52<03:47, 470kiB/s]
51%|█████ | 110M/217M [05:52<03:39, 488kiB/s]
51%|█████ | 110M/217M [05:52<03:28, 512kiB/s]
51%|█████ | 110M/217M [05:52<03:23, 525kiB/s]
51%|█████ | 111M/217M [05:52<03:11, 558kiB/s]
51%|█████ | 111M/217M [05:52<03:09, 564kiB/s]
51%|█████ | 111M/217M [05:52<03:02, 584kiB/s]
51%|█████ | 111M/217M [05:53<02:54, 610kiB/s]
51%|█████ | 111M/217M [05:53<02:49, 627kiB/s]
51%|█████ | 111M/217M [05:53<02:41, 659kiB/s]
51%|█████ | 111M/217M [05:53<02:36, 679kiB/s]
51%|█████ | 111M/217M [05:53<02:31, 700kiB/s]
51%|█████ | 111M/217M [05:53<02:49, 627kiB/s]
51%|█████ | 111M/217M [05:53<02:29, 710kiB/s]
51%|█████ | 111M/217M [05:53<02:34, 687kiB/s]
51%|█████▏ | 111M/217M [05:54<02:37, 672kiB/s]
51%|█████▏ | 111M/217M [05:54<02:48, 629kiB/s]
51%|█████▏ | 112M/217M [05:54<02:53, 608kiB/s]
51%|█████▏ | 112M/217M [05:54<02:56, 600kiB/s]
51%|█████▏ | 112M/217M [05:54<03:10, 555kiB/s]
51%|█████▏ | 112M/217M [05:54<03:12, 550kiB/s]
51%|█████▏ | 112M/217M [05:54<03:29, 503kiB/s]
51%|█████▏ | 112M/217M [05:54<03:31, 499kiB/s]
51%|█████▏ | 112M/217M [05:54<03:23, 517kiB/s]
52%|█████▏ | 112M/217M [05:55<03:26, 511kiB/s]
52%|█████▏ | 112M/217M [05:55<03:30, 501kiB/s]
52%|█████▏ | 112M/217M [05:55<03:36, 485kiB/s]
52%|█████▏ | 112M/217M [05:55<03:41, 476kiB/s]
52%|█████▏ | 112M/217M [05:55<03:41, 475kiB/s]
52%|█████▏ | 112M/217M [05:55<03:50, 457kiB/s]
52%|█████▏ | 112M/217M [05:55<03:41, 474kiB/s]
52%|█████▏ | 112M/217M [05:55<03:33, 491kiB/s]
52%|█████▏ | 112M/217M [05:56<03:25, 511kiB/s]
52%|█████▏ | 112M/217M [05:56<03:20, 522kiB/s]
52%|█████▏ | 113M/217M [05:56<03:12, 544kiB/s]
52%|█████▏ | 113M/217M [05:56<03:07, 559kiB/s]
52%|█████▏ | 113M/217M [05:56<02:59, 582kiB/s]
52%|█████▏ | 113M/217M [05:56<02:55, 596kiB/s]
52%|█████▏ | 113M/217M [05:56<02:48, 621kiB/s]
52%|█████▏ | 113M/217M [05:56<03:04, 567kiB/s]
52%|█████▏ | 113M/217M [05:56<02:48, 620kiB/s]
52%|█████▏ | 113M/217M [05:57<02:46, 626kiB/s]
52%|█████▏ | 113M/217M [05:57<02:52, 605kiB/s]
52%|█████▏ | 113M/217M [05:57<02:57, 585kiB/s]
52%|█████▏ | 113M/217M [05:57<03:03, 567kiB/s]
52%|█████▏ | 113M/217M [05:57<03:05, 560kiB/s]
52%|█████▏ | 113M/217M [05:57<02:57, 585kiB/s]
52%|█████▏ | 113M/217M [05:57<03:01, 572kiB/s]
52%|█████▏ | 113M/217M [05:57<03:12, 538kiB/s]
52%|█████▏ | 114M/217M [05:57<03:17, 526kiB/s]
52%|█████▏ | 114M/217M [05:58<03:17, 524kiB/s]
52%|█████▏ | 114M/217M [05:58<04:12, 411kiB/s]
52%|█████▏ | 114M/217M [05:58<03:52, 445kiB/s]
52%|█████▏ | 114M/217M [05:58<04:11, 412kiB/s]
52%|█████▏ | 114M/217M [05:58<04:19, 399kiB/s]
52%|█████▏ | 114M/217M [05:58<04:19, 398kiB/s]
52%|█████▏ | 114M/217M [05:58<04:21, 395kiB/s]
52%|█████▏ | 114M/217M [05:59<04:15, 405kiB/s]
52%|█████▏ | 114M/217M [05:59<04:17, 401kiB/s]
52%|█████▏ | 114M/217M [05:59<04:19, 397kiB/s]
52%|█████▏ | 114M/217M [05:59<04:41, 367kiB/s]
53%|█████▎ | 114M/217M [05:59<04:34, 376kiB/s]
53%|█████▎ | 114M/217M [05:59<04:31, 380kiB/s]
53%|█████▎ | 114M/217M [05:59<04:27, 385kiB/s]
53%|█████▎ | 114M/217M [05:59<04:17, 401kiB/s]
53%|█████▎ | 114M/217M [05:59<04:08, 415kiB/s]
53%|█████▎ | 114M/217M [06:00<04:00, 428kiB/s]
53%|█████▎ | 114M/217M [06:00<03:53, 441kiB/s]
53%|█████▎ | 114M/217M [06:00<03:38, 470kiB/s]
53%|█████▎ | 115M/217M [06:00<03:26, 499kiB/s]
53%|█████▎ | 115M/217M [06:00<03:20, 513kiB/s]
53%|█████▎ | 115M/217M [06:00<03:13, 530kiB/s]
53%|█████▎ | 115M/217M [06:00<03:03, 559kiB/s]
53%|█████▎ | 115M/217M [06:00<03:00, 568kiB/s]
53%|█████▎ | 115M/217M [06:00<02:53, 591kiB/s]
53%|█████▎ | 115M/217M [06:01<02:47, 612kiB/s]
53%|█████▎ | 115M/217M [06:01<02:39, 642kiB/s]
53%|█████▎ | 115M/217M [06:01<02:34, 661kiB/s]
53%|█████▎ | 115M/217M [06:01<02:29, 685kiB/s]
53%|█████▎ | 115M/217M [06:01<02:46, 614kiB/s]
53%|█████▎ | 115M/217M [06:01<02:33, 666kiB/s]
53%|█████▎ | 115M/217M [06:01<02:39, 641kiB/s]
53%|█████▎ | 115M/217M [06:01<02:39, 637kiB/s]
53%|█████▎ | 116M/217M [06:02<02:43, 624kiB/s]
53%|█████▎ | 116M/217M [06:02<02:41, 628kiB/s]
53%|█████▎ | 116M/217M [06:02<02:37, 646kiB/s]
53%|█████▎ | 116M/217M [06:02<02:35, 655kiB/s]
53%|█████▎ | 116M/217M [06:02<02:31, 669kiB/s]
53%|█████▎ | 116M/217M [06:02<02:29, 678kiB/s]
53%|█████▎ | 116M/217M [06:02<02:25, 694kiB/s]
53%|█████▎ | 116M/217M [06:02<02:30, 674kiB/s]
53%|█████▎ | 116M/217M [06:02<02:30, 673kiB/s]
53%|█████▎ | 116M/217M [06:03<02:38, 639kiB/s]
54%|█████▎ | 116M/217M [06:03<03:07, 538kiB/s]
54%|█████▎ | 116M/217M [06:03<03:09, 534kiB/s]
54%|█████▎ | 116M/217M [06:03<03:18, 508kiB/s]
54%|█████▎ | 116M/217M [06:03<03:25, 490kiB/s]
54%|█████▎ | 117M/217M [06:03<03:30, 480kiB/s]
54%|█████▎ | 117M/217M [06:03<03:34, 470kiB/s]
54%|█████▎ | 117M/217M [06:03<03:35, 466kiB/s]
54%|█████▎ | 117M/217M [06:04<03:41, 454kiB/s]
54%|█████▎ | 117M/217M [06:04<03:32, 472kiB/s]
54%|█████▎ | 117M/217M [06:04<03:21, 500kiB/s]
54%|█████▍ | 117M/217M [06:04<03:15, 513kiB/s]
54%|█████▍ | 117M/217M [06:04<03:07, 536kiB/s]
54%|█████▍ | 117M/217M [06:04<02:58, 562kiB/s]
54%|█████▍ | 117M/217M [06:04<03:08, 531kiB/s]
54%|█████▍ | 117M/217M [06:04<03:07, 536kiB/s]
54%|█████▍ | 117M/217M [06:04<03:07, 533kiB/s]
54%|█████▍ | 117M/217M [06:05<03:17, 506kiB/s]
54%|█████▍ | 117M/217M [06:05<03:13, 517kiB/s]
54%|█████▍ | 117M/217M [06:05<03:33, 469kiB/s]
54%|█████▍ | 117M/217M [06:05<03:25, 485kiB/s]
54%|█████▍ | 117M/217M [06:05<03:40, 452kiB/s]
54%|█████▍ | 117M/217M [06:05<03:52, 429kiB/s]
54%|█████▍ | 118M/217M [06:05<04:00, 414kiB/s]
54%|█████▍ | 118M/217M [06:05<04:35, 362kiB/s]
54%|█████▍ | 118M/217M [06:06<04:38, 358kiB/s]
54%|█████▍ | 118M/217M [06:06<04:56, 336kiB/s]
54%|█████▍ | 118M/217M [06:06<04:48, 345kiB/s]
54%|█████▍ | 118M/217M [06:06<05:22, 309kiB/s]
54%|█████▍ | 118M/217M [06:06<04:54, 338kiB/s]
54%|█████▍ | 118M/217M [06:06<04:53, 339kiB/s]
54%|█████▍ | 118M/217M [06:06<04:56, 336kiB/s]
54%|█████▍ | 118M/217M [06:07<05:00, 331kiB/s]
54%|█████▍ | 118M/217M [06:07<04:55, 336kiB/s]
54%|█████▍ | 118M/217M [06:07<04:42, 351kiB/s]
54%|█████▍ | 118M/217M [06:07<04:32, 365kiB/s]
54%|█████▍ | 118M/217M [06:07<04:18, 384kiB/s]
54%|█████▍ | 118M/217M [06:07<04:10, 396kiB/s]
54%|█████▍ | 118M/217M [06:07<03:59, 414kiB/s]
54%|█████▍ | 118M/217M [06:07<03:44, 442kiB/s]
54%|█████▍ | 118M/217M [06:07<03:32, 467kiB/s]
54%|█████▍ | 118M/217M [06:08<03:24, 484kiB/s]
55%|█████▍ | 118M/217M [06:08<03:14, 509kiB/s]
55%|█████▍ | 119M/217M [06:08<03:05, 534kiB/s]
55%|█████▍ | 119M/217M [06:08<02:58, 552kiB/s]
55%|█████▍ | 119M/217M [06:08<02:54, 565kiB/s]
55%|█████▍ | 119M/217M [06:08<03:07, 525kiB/s]
55%|█████▍ | 119M/217M [06:08<02:55, 561kiB/s]
55%|█████▍ | 119M/217M [06:08<02:55, 561kiB/s]
55%|█████▍ | 119M/217M [06:09<03:07, 525kiB/s]
55%|█████▍ | 119M/217M [06:09<03:08, 521kiB/s]
55%|█████▍ | 119M/217M [06:09<03:11, 513kiB/s]
55%|█████▍ | 119M/217M [06:09<03:28, 471kiB/s]
55%|█████▍ | 119M/217M [06:09<03:39, 446kiB/s]
55%|█████▍ | 119M/217M [06:09<03:53, 420kiB/s]
55%|█████▍ | 119M/217M [06:09<04:27, 367kiB/s]
55%|█████▍ | 119M/217M [06:09<04:31, 361kiB/s]
55%|█████▍ | 119M/217M [06:10<04:57, 329kiB/s]
55%|█████▍ | 119M/217M [06:10<04:51, 336kiB/s]
55%|█████▍ | 119M/217M [06:10<04:38, 352kiB/s]
55%|█████▍ | 119M/217M [06:10<04:28, 365kiB/s]
55%|█████▍ | 120M/217M [06:10<04:10, 391kiB/s]
55%|█████▌ | 120M/217M [06:10<04:04, 400kiB/s]
55%|█████▌ | 120M/217M [06:10<03:53, 419kiB/s]
55%|█████▌ | 120M/217M [06:10<03:35, 453kiB/s]
55%|█████▌ | 120M/217M [06:11<03:27, 469kiB/s]
55%|█████▌ | 120M/217M [06:11<03:17, 493kiB/s]
55%|█████▌ | 120M/217M [06:11<03:35, 452kiB/s]
55%|█████▌ | 120M/217M [06:11<03:14, 499kiB/s]
55%|█████▌ | 120M/217M [06:11<03:23, 479kiB/s]
55%|█████▌ | 120M/217M [06:11<03:25, 474kiB/s]
55%|█████▌ | 120M/217M [06:11<03:24, 474kiB/s]
55%|█████▌ | 120M/217M [06:11<03:24, 475kiB/s]
55%|█████▌ | 120M/217M [06:12<03:18, 488kiB/s]
55%|█████▌ | 120M/217M [06:12<03:17, 492kiB/s]
55%|█████▌ | 120M/217M [06:12<03:09, 513kiB/s]
55%|█████▌ | 120M/217M [06:12<03:05, 524kiB/s]
55%|█████▌ | 120M/217M [06:12<03:00, 537kiB/s]
55%|█████▌ | 121M/217M [06:12<02:52, 561kiB/s]
55%|█████▌ | 121M/217M [06:12<02:48, 574kiB/s]
56%|█████▌ | 121M/217M [06:12<02:43, 593kiB/s]
56%|█████▌ | 121M/217M [06:12<02:35, 620kiB/s]
56%|█████▌ | 121M/217M [06:13<02:30, 639kiB/s]
56%|█████▌ | 121M/217M [06:13<02:26, 660kiB/s]
56%|█████▌ | 121M/217M [06:13<02:22, 676kiB/s]
56%|█████▌ | 121M/217M [06:13<02:38, 605kiB/s]
56%|█████▌ | 121M/217M [06:13<02:19, 691kiB/s]
56%|█████▌ | 121M/217M [06:13<02:26, 656kiB/s]
56%|█████▌ | 121M/217M [06:13<02:29, 640kiB/s]
56%|█████▌ | 121M/217M [06:13<02:31, 632kiB/s]
56%|█████▌ | 121M/217M [06:13<02:35, 617kiB/s]
56%|█████▌ | 122M/217M [06:14<02:31, 631kiB/s]
56%|█████▌ | 122M/217M [06:14<02:28, 644kiB/s]
56%|█████▌ | 122M/217M [06:14<02:40, 596kiB/s]
56%|█████▌ | 122M/217M [06:14<02:39, 598kiB/s]
56%|█████▌ | 122M/217M [06:14<02:40, 594kiB/s]
56%|█████▌ | 122M/217M [06:14<03:19, 479kiB/s]
56%|█████▌ | 122M/217M [06:14<03:30, 454kiB/s]
56%|█████▌ | 122M/217M [06:15<03:34, 445kiB/s]
56%|█████▌ | 122M/217M [06:15<03:43, 425kiB/s]
56%|█████▌ | 122M/217M [06:15<03:48, 417kiB/s]
56%|█████▌ | 122M/217M [06:15<03:39, 433kiB/s]
56%|█████▌ | 122M/217M [06:15<03:33, 447kiB/s]
56%|█████▌ | 122M/217M [06:15<03:21, 472kiB/s]
56%|█████▋ | 122M/217M [06:15<03:13, 490kiB/s]
56%|█████▋ | 122M/217M [06:15<03:15, 485kiB/s]
56%|█████▋ | 122M/217M [06:15<03:32, 447kiB/s]
56%|█████▋ | 122M/217M [06:16<03:33, 444kiB/s]
56%|█████▋ | 122M/217M [06:16<03:32, 445kiB/s]
56%|█████▋ | 123M/217M [06:16<03:39, 433kiB/s]
56%|█████▋ | 123M/217M [06:16<03:43, 424kiB/s]
56%|█████▋ | 123M/217M [06:16<04:12, 375kiB/s]
56%|█████▋ | 123M/217M [06:16<04:15, 370kiB/s]
56%|█████▋ | 123M/217M [06:16<04:40, 337kiB/s]
56%|█████▋ | 123M/217M [06:17<04:33, 346kiB/s]
57%|█████▋ | 123M/217M [06:17<04:47, 329kiB/s]
57%|█████▋ | 123M/217M [06:17<04:54, 321kiB/s]
57%|█████▋ | 123M/217M [06:17<04:54, 320kiB/s]
57%|█████▋ | 123M/217M [06:17<04:56, 318kiB/s]
57%|█████▋ | 123M/217M [06:17<05:21, 293kiB/s]
57%|█████▋ | 123M/217M [06:17<05:06, 308kiB/s]
57%|█████▋ | 123M/217M [06:17<05:21, 293kiB/s]
57%|█████▋ | 123M/217M [06:18<05:26, 289kiB/s]
57%|█████▋ | 123M/217M [06:18<05:24, 291kiB/s]
57%|█████▋ | 123M/217M [06:18<05:21, 293kiB/s]
57%|█████▋ | 123M/217M [06:18<05:04, 309kiB/s]
57%|█████▋ | 123M/217M [06:18<04:49, 326kiB/s]
57%|█████▋ | 123M/217M [06:18<04:31, 346kiB/s]
57%|█████▋ | 123M/217M [06:18<04:14, 369kiB/s]
57%|█████▋ | 123M/217M [06:18<04:01, 388kiB/s]
57%|█████▋ | 123M/217M [06:18<03:52, 404kiB/s]
57%|█████▋ | 123M/217M [06:19<03:35, 435kiB/s]
57%|█████▋ | 124M/217M [06:19<03:22, 462kiB/s]
57%|█████▋ | 124M/217M [06:19<03:12, 487kiB/s]
57%|█████▋ | 124M/217M [06:19<03:02, 514kiB/s]
57%|█████▋ | 124M/217M [06:19<02:54, 536kiB/s]
57%|█████▋ | 124M/217M [06:19<02:51, 547kiB/s]
57%|█████▋ | 124M/217M [06:19<02:43, 571kiB/s]
57%|█████▋ | 124M/217M [06:19<02:37, 591kiB/s]
57%|█████▋ | 124M/217M [06:20<02:54, 536kiB/s]
57%|█████▋ | 124M/217M [06:20<02:39, 586kiB/s]
57%|█████▋ | 124M/217M [06:20<02:46, 559kiB/s]
57%|█████▋ | 124M/217M [06:20<02:48, 554kiB/s]
57%|█████▋ | 124M/217M [06:20<02:49, 550kiB/s]
57%|█████▋ | 124M/217M [06:20<02:46, 559kiB/s]
57%|█████▋ | 124M/217M [06:20<02:43, 567kiB/s]
57%|█████▋ | 124M/217M [06:20<03:19, 465kiB/s]
57%|█████▋ | 125M/217M [06:21<02:54, 531kiB/s]
57%|█████▋ | 125M/217M [06:21<02:57, 521kiB/s]
57%|█████▋ | 125M/217M [06:21<02:57, 521kiB/s]
57%|█████▋ | 125M/217M [06:21<03:16, 471kiB/s]
57%|█████▋ | 125M/217M [06:21<03:22, 457kiB/s]
57%|█████▋ | 125M/217M [06:21<03:22, 456kiB/s]
57%|█████▋ | 125M/217M [06:21<04:03, 379kiB/s]
57%|█████▋ | 125M/217M [06:22<04:54, 314kiB/s]
57%|█████▋ | 125M/217M [06:22<05:06, 301kiB/s]
58%|█████▊ | 125M/217M [06:22<05:19, 289kiB/s]
58%|█████▊ | 125M/217M [06:22<05:51, 262kiB/s]
58%|█████▊ | 125M/217M [06:22<06:25, 239kiB/s]
58%|█████▊ | 125M/217M [06:22<06:34, 234kiB/s]
58%|█████▊ | 125M/217M [06:22<07:40, 200kiB/s]
58%|█████▊ | 125M/217M [06:23<07:30, 205kiB/s]
58%|█████▊ | 125M/217M [06:23<07:14, 212kiB/s]
58%|█████▊ | 125M/217M [06:23<07:00, 219kiB/s]
58%|█████▊ | 125M/217M [06:23<06:30, 236kiB/s]
58%|█████▊ | 125M/217M [06:23<06:10, 249kiB/s]
58%|█████▊ | 125M/217M [06:23<05:35, 275kiB/s]
58%|█████▊ | 125M/217M [06:23<05:09, 297kiB/s]
58%|█████▊ | 125M/217M [06:23<04:44, 323kiB/s]
58%|█████▊ | 125M/217M [06:24<04:30, 340kiB/s]
58%|█████▊ | 125M/217M [06:24<04:14, 361kiB/s]
58%|█████▊ | 126M/217M [06:24<03:57, 387kiB/s]
58%|█████▊ | 126M/217M [06:24<03:48, 402kiB/s]
58%|█████▊ | 126M/217M [06:24<03:32, 430kiB/s]
58%|█████▊ | 126M/217M [06:24<03:22, 451kiB/s]
58%|█████▊ | 126M/217M [06:24<03:10, 480kiB/s]
58%|█████▊ | 126M/217M [06:24<03:02, 502kiB/s]
58%|█████▊ | 126M/217M [06:25<02:53, 528kiB/s]
58%|█████▊ | 126M/217M [06:25<02:46, 548kiB/s]
58%|█████▊ | 126M/217M [06:25<02:39, 573kiB/s]
58%|█████▊ | 126M/217M [06:25<02:31, 602kiB/s]
58%|█████▊ | 126M/217M [06:25<02:26, 621kiB/s]
58%|█████▊ | 126M/217M [06:25<02:20, 649kiB/s]
58%|█████▊ | 126M/217M [06:25<02:16, 666kiB/s]
58%|█████▊ | 126M/217M [06:25<02:12, 686kiB/s]
58%|█████▊ | 127M/217M [06:25<02:07, 712kiB/s]
58%|█████▊ | 127M/217M [06:26<02:03, 735kiB/s]
58%|█████▊ | 127M/217M [06:26<02:01, 748kiB/s]
58%|█████▊ | 127M/217M [06:26<01:58, 765kiB/s]
58%|█████▊ | 127M/217M [06:26<02:03, 732kiB/s]
58%|█████▊ | 127M/217M [06:26<02:14, 672kiB/s]
58%|█████▊ | 127M/217M [06:26<02:17, 658kiB/s]
58%|█████▊ | 127M/217M [06:26<02:15, 666kiB/s]
59%|█████▊ | 127M/217M [06:26<02:11, 685kiB/s]
59%|█████▊ | 127M/217M [06:26<02:08, 701kiB/s]
59%|█████▊ | 127M/217M [06:27<02:07, 704kiB/s]
59%|█████▊ | 127M/217M [06:27<02:05, 716kiB/s]
59%|█████▊ | 128M/217M [06:27<02:02, 735kiB/s]
59%|█████▊ | 128M/217M [06:27<01:59, 748kiB/s]
59%|█████▉ | 128M/217M [06:27<01:57, 765kiB/s]
59%|█████▉ | 128M/217M [06:27<01:54, 779kiB/s]
59%|█████▉ | 128M/217M [06:27<01:53, 791kiB/s]
59%|█████▉ | 128M/217M [06:27<01:50, 812kiB/s]
59%|█████▉ | 128M/217M [06:27<01:47, 833kiB/s]
59%|█████▉ | 128M/217M [06:28<02:01, 732kiB/s]
59%|█████▉ | 128M/217M [06:28<01:47, 826kiB/s]
59%|█████▉ | 128M/217M [06:28<02:01, 730kiB/s]
59%|█████▉ | 128M/217M [06:28<02:12, 669kiB/s]
59%|█████▉ | 128M/217M [06:28<02:34, 576kiB/s]
59%|█████▉ | 129M/217M [06:28<02:43, 544kiB/s]
59%|█████▉ | 129M/217M [06:28<02:44, 540kiB/s]
59%|█████▉ | 129M/217M [06:29<02:42, 545kiB/s]
59%|█████▉ | 129M/217M [06:29<02:41, 547kiB/s]
59%|█████▉ | 129M/217M [06:29<02:38, 557kiB/s]
59%|█████▉ | 129M/217M [06:29<02:33, 576kiB/s]
59%|█████▉ | 129M/217M [06:29<02:28, 593kiB/s]
59%|█████▉ | 129M/217M [06:29<02:23, 616kiB/s]
59%|█████▉ | 129M/217M [06:29<02:17, 642kiB/s]
59%|█████▉ | 129M/217M [06:29<02:12, 665kiB/s]
59%|█████▉ | 129M/217M [06:30<02:09, 679kiB/s]
60%|█████▉ | 129M/217M [06:30<02:06, 695kiB/s]
60%|█████▉ | 129M/217M [06:30<02:02, 716kiB/s]
60%|█████▉ | 130M/217M [06:30<02:16, 645kiB/s]
60%|█████▉ | 130M/217M [06:30<02:01, 719kiB/s]
60%|█████▉ | 130M/217M [06:30<02:03, 711kiB/s]
60%|█████▉ | 130M/217M [06:30<02:12, 661kiB/s]
60%|█████▉ | 130M/217M [06:30<02:40, 543kiB/s]
60%|█████▉ | 130M/217M [06:31<02:40, 545kiB/s]
60%|█████▉ | 130M/217M [06:31<02:54, 501kiB/s]
60%|█████▉ | 130M/217M [06:31<02:53, 502kiB/s]
60%|█████▉ | 130M/217M [06:31<02:49, 516kiB/s]
60%|█████▉ | 130M/217M [06:31<02:46, 525kiB/s]
60%|█████▉ | 130M/217M [06:31<02:43, 532kiB/s]
60%|█████▉ | 130M/217M [06:31<02:39, 546kiB/s]
60%|█████▉ | 130M/217M [06:31<02:32, 569kiB/s]
60%|██████ | 130M/217M [06:31<02:29, 580kiB/s]
60%|██████ | 130M/217M [06:32<02:22, 610kiB/s]
60%|██████ | 131M/217M [06:32<02:17, 630kiB/s]
60%|██████ | 131M/217M [06:32<02:12, 654kiB/s]
60%|██████ | 131M/217M [06:32<02:08, 675kiB/s]
60%|██████ | 131M/217M [06:32<02:03, 698kiB/s]
60%|██████ | 131M/217M [06:32<02:00, 715kiB/s]
60%|██████ | 131M/217M [06:32<02:15, 639kiB/s]
60%|██████ | 131M/217M [06:32<01:59, 720kiB/s]
60%|██████ | 131M/217M [06:33<02:02, 701kiB/s]
60%|██████ | 131M/217M [06:33<02:09, 663kiB/s]
60%|██████ | 131M/217M [06:33<02:09, 664kiB/s]
60%|██████ | 131M/217M [06:33<02:08, 670kiB/s]
61%|██████ | 131M/217M [06:33<02:05, 683kiB/s]
61%|██████ | 132M/217M [06:33<02:04, 688kiB/s]
61%|██████ | 132M/217M [06:33<02:01, 703kiB/s]
61%|██████ | 132M/217M [06:33<02:00, 709kiB/s]
61%|██████ | 132M/217M [06:34<02:00, 710kiB/s]
61%|██████ | 132M/217M [06:34<02:06, 673kiB/s]
61%|██████ | 132M/217M [06:34<02:10, 652kiB/s]
61%|██████ | 132M/217M [06:34<02:17, 621kiB/s]
61%|██████ | 132M/217M [06:34<02:18, 614kiB/s]
61%|██████ | 132M/217M [06:34<02:23, 594kiB/s]
61%|██████ | 132M/217M [06:34<02:31, 560kiB/s]
61%|██████ | 132M/217M [06:34<02:49, 503kiB/s]
61%|██████ | 132M/217M [06:35<03:10, 447kiB/s]
61%|██████ | 132M/217M [06:35<03:19, 426kiB/s]
61%|██████ | 132M/217M [06:35<03:38, 388kiB/s]
61%|██████ | 132M/217M [06:35<04:34, 309kiB/s]
61%|██████ | 132M/217M [06:35<04:51, 291kiB/s]
61%|██████ | 133M/217M [06:35<04:50, 292kiB/s]
61%|██████ | 133M/217M [06:35<04:51, 291kiB/s]
61%|██████ | 133M/217M [06:35<04:48, 294kiB/s]
61%|██████ | 133M/217M [06:36<04:41, 301kiB/s]
61%|██████ | 133M/217M [06:36<04:49, 292kiB/s]
61%|██████ | 133M/217M [06:36<04:45, 297kiB/s]
61%|██████ | 133M/217M [06:36<04:54, 287kiB/s]
61%|██████ | 133M/217M [06:36<05:23, 261kiB/s]
61%|██████ | 133M/217M [06:36<05:16, 267kiB/s]
61%|██████ | 133M/217M [06:36<05:37, 250kiB/s]
61%|██████ | 133M/217M [06:36<05:34, 252kiB/s]
61%|██████ | 133M/217M [06:37<05:39, 248kiB/s]
61%|██████ | 133M/217M [06:37<05:23, 261kiB/s]
61%|██████ | 133M/217M [06:37<05:12, 270kiB/s]
61%|██████ | 133M/217M [06:37<05:01, 280kiB/s]
61%|██████ | 133M/217M [06:37<04:34, 307kiB/s]
61%|██████ | 133M/217M [06:37<04:46, 294kiB/s]
61%|██████ | 133M/217M [06:37<04:47, 292kiB/s]
61%|██████▏ | 133M/217M [06:37<04:55, 285kiB/s]
61%|██████▏ | 133M/217M [06:38<05:15, 267kiB/s]
61%|██████▏ | 133M/217M [06:38<05:06, 274kiB/s]
61%|██████▏ | 133M/217M [06:38<05:23, 260kiB/s]
61%|██████▏ | 133M/217M [06:38<05:30, 255kiB/s]
61%|██████▏ | 133M/217M [06:38<05:50, 240kiB/s]
61%|██████▏ | 133M/217M [06:38<05:40, 247kiB/s]
61%|██████▏ | 133M/217M [06:38<05:40, 246kiB/s]
61%|██████▏ | 133M/217M [06:39<06:07, 228kiB/s]
61%|██████▏ | 133M/217M [06:39<06:29, 215kiB/s]
61%|██████▏ | 133M/217M [06:39<06:45, 207kiB/s]
61%|██████▏ | 133M/217M [06:39<06:39, 210kiB/s]
61%|██████▏ | 134M/217M [06:39<06:29, 215kiB/s]
61%|██████▏ | 134M/217M [06:39<06:13, 224kiB/s]
61%|██████▏ | 134M/217M [06:39<05:48, 240kiB/s]
61%|██████▏ | 134M/217M [06:40<05:28, 255kiB/s]
62%|██████▏ | 134M/217M [06:40<05:24, 258kiB/s]
62%|██████▏ | 134M/217M [06:40<05:42, 244kiB/s]
62%|██████▏ | 134M/217M [06:40<05:42, 244kiB/s]
62%|██████▏ | 134M/217M [06:40<05:32, 251kiB/s]
62%|██████▏ | 134M/217M [06:40<05:20, 260kiB/s]
62%|██████▏ | 134M/217M [06:40<05:11, 268kiB/s]
62%|██████▏ | 134M/217M [06:40<04:54, 283kiB/s]
62%|██████▏ | 134M/217M [06:41<04:37, 301kiB/s]
62%|██████▏ | 134M/217M [06:41<04:16, 324kiB/s]
62%|██████▏ | 134M/217M [06:41<04:03, 342kiB/s]
62%|██████▏ | 134M/217M [06:41<03:47, 367kiB/s]
62%|██████▏ | 134M/217M [06:41<03:34, 387kiB/s]
62%|██████▏ | 134M/217M [06:41<03:25, 406kiB/s]
62%|██████▏ | 134M/217M [06:41<03:15, 425kiB/s]
62%|██████▏ | 134M/217M [06:41<03:08, 440kiB/s]
62%|██████▏ | 134M/217M [06:41<02:57, 467kiB/s]
62%|██████▏ | 134M/217M [06:42<02:47, 496kiB/s]
62%|██████▏ | 134M/217M [06:42<02:41, 514kiB/s]
62%|██████▏ | 135M/217M [06:42<02:35, 534kiB/s]
62%|██████▏ | 135M/217M [06:42<02:29, 553kiB/s]
62%|██████▏ | 135M/217M [06:42<02:22, 580kiB/s]
62%|██████▏ | 135M/217M [06:42<02:18, 598kiB/s]
62%|██████▏ | 135M/217M [06:42<02:22, 580kiB/s]
62%|██████▏ | 135M/217M [06:42<02:25, 568kiB/s]
62%|██████▏ | 135M/217M [06:43<02:30, 547kiB/s]
62%|██████▏ | 135M/217M [06:43<02:30, 548kiB/s]
62%|██████▏ | 135M/217M [06:43<02:28, 553kiB/s]
62%|██████▏ | 135M/217M [06:43<02:26, 560kiB/s]
62%|██████▏ | 135M/217M [06:43<02:25, 565kiB/s]
62%|██████▏ | 135M/217M [06:43<02:22, 576kiB/s]
62%|██████▏ | 135M/217M [06:43<02:41, 506kiB/s]
62%|██████▏ | 135M/217M [06:43<02:49, 485kiB/s]
62%|██████▏ | 135M/217M [06:44<03:37, 376kiB/s]
62%|██████▏ | 135M/217M [06:44<03:59, 342kiB/s]
62%|██████▏ | 135M/217M [06:44<04:39, 292kiB/s]
62%|██████▏ | 136M/217M [06:44<04:43, 289kiB/s]
62%|██████▏ | 136M/217M [06:44<04:43, 288kiB/s]
62%|██████▏ | 136M/217M [06:44<04:43, 288kiB/s]
62%|██████▏ | 136M/217M [06:44<04:28, 305kiB/s]
62%|██████▏ | 136M/217M [06:45<04:13, 322kiB/s]
62%|██████▏ | 136M/217M [06:45<03:58, 342kiB/s]
62%|██████▏ | 136M/217M [06:45<03:49, 356kiB/s]
63%|██████▎ | 136M/217M [06:45<03:32, 384kiB/s]
63%|██████▎ | 136M/217M [06:45<03:23, 400kiB/s]
63%|██████▎ | 136M/217M [06:45<03:14, 418kiB/s]
63%|██████▎ | 136M/217M [06:45<02:59, 452kiB/s]
63%|██████▎ | 136M/217M [06:45<03:12, 422kiB/s]
63%|██████▎ | 136M/217M [06:45<03:02, 445kiB/s]
63%|██████▎ | 136M/217M [06:46<03:05, 438kiB/s]
63%|██████▎ | 136M/217M [06:46<03:04, 439kiB/s]
63%|██████▎ | 136M/217M [06:46<03:07, 433kiB/s]
63%|██████▎ | 136M/217M [06:46<03:27, 390kiB/s]
63%|██████▎ | 136M/217M [06:46<03:17, 410kiB/s]
63%|██████▎ | 136M/217M [06:46<03:27, 389kiB/s]
63%|██████▎ | 136M/217M [06:46<03:33, 379kiB/s]
63%|██████▎ | 136M/217M [06:46<03:30, 384kiB/s]
63%|██████▎ | 137M/217M [06:47<03:23, 396kiB/s]
63%|██████▎ | 137M/217M [06:47<03:20, 403kiB/s]
63%|██████▎ | 137M/217M [06:47<03:17, 409kiB/s]
63%|██████▎ | 137M/217M [06:47<03:27, 388kiB/s]
63%|██████▎ | 137M/217M [06:47<03:19, 405kiB/s]
63%|██████▎ | 137M/217M [06:47<03:25, 393kiB/s]
63%|██████▎ | 137M/217M [06:47<03:24, 394kiB/s]
63%|██████▎ | 137M/217M [06:47<03:23, 396kiB/s]
63%|██████▎ | 137M/217M [06:48<03:23, 395kiB/s]
63%|██████▎ | 137M/217M [06:48<03:15, 411kiB/s]
63%|██████▎ | 137M/217M [06:48<03:10, 421kiB/s]
63%|██████▎ | 137M/217M [06:48<03:09, 422kiB/s]
63%|██████▎ | 137M/217M [06:48<03:17, 407kiB/s]
63%|██████▎ | 137M/217M [06:48<03:31, 380kiB/s]
63%|██████▎ | 137M/217M [06:48<03:28, 384kiB/s]
63%|██████▎ | 137M/217M [06:48<03:26, 388kiB/s]
63%|██████▎ | 137M/217M [06:49<03:23, 393kiB/s]
63%|██████▎ | 137M/217M [06:49<03:15, 410kiB/s]
63%|██████▎ | 137M/217M [06:49<03:09, 421kiB/s]
63%|██████▎ | 137M/217M [06:49<03:05, 431kiB/s]
63%|██████▎ | 137M/217M [06:49<02:57, 450kiB/s]
63%|██████▎ | 138M/217M [06:49<02:51, 464kiB/s]
63%|██████▎ | 138M/217M [06:49<02:44, 485kiB/s]
63%|██████▎ | 138M/217M [06:49<02:36, 509kiB/s]
63%|██████▎ | 138M/217M [06:49<02:30, 529kiB/s]
63%|██████▎ | 138M/217M [06:50<02:26, 544kiB/s]
63%|██████▎ | 138M/217M [06:50<02:18, 571kiB/s]
63%|██████▎ | 138M/217M [06:50<02:13, 593kiB/s]
64%|██████▎ | 138M/217M [06:50<02:13, 594kiB/s]
64%|██████▎ | 138M/217M [06:50<02:17, 578kiB/s]
64%|██████▎ | 138M/217M [06:50<02:27, 537kiB/s]
64%|██████▎ | 138M/217M [06:50<02:29, 530kiB/s]
64%|██████▎ | 138M/217M [06:50<02:28, 533kiB/s]
64%|██████▎ | 138M/217M [06:50<02:34, 513kiB/s]
64%|██████▎ | 138M/217M [06:51<02:33, 514kiB/s]
64%|██████▎ | 138M/217M [06:51<02:40, 493kiB/s]
64%|██████▎ | 138M/217M [06:51<02:44, 479kiB/s]
64%|██████▍ | 139M/217M [06:51<02:46, 474kiB/s]
64%|██████▍ | 139M/217M [06:51<03:02, 432kiB/s]
64%|██████▍ | 139M/217M [06:51<03:16, 400kiB/s]
64%|██████▍ | 139M/217M [06:51<03:24, 384kiB/s]
64%|██████▍ | 139M/217M [06:51<03:24, 383kiB/s]
64%|██████▍ | 139M/217M [06:52<03:25, 382kiB/s]
64%|██████▍ | 139M/217M [06:52<03:22, 387kiB/s]
64%|██████▍ | 139M/217M [06:52<03:12, 407kiB/s]
64%|██████▍ | 139M/217M [06:52<03:08, 416kiB/s]
64%|██████▍ | 139M/217M [06:52<03:10, 410kiB/s]
64%|██████▍ | 139M/217M [06:52<03:36, 361kiB/s]
64%|██████▍ | 139M/217M [06:52<03:41, 353kiB/s]
64%|██████▍ | 139M/217M [06:53<03:53, 335kiB/s]
64%|██████▍ | 139M/217M [06:53<03:59, 326kiB/s]
64%|██████▍ | 139M/217M [06:53<04:03, 321kiB/s]
64%|██████▍ | 139M/217M [06:53<04:13, 308kiB/s]
64%|██████▍ | 139M/217M [06:53<03:59, 325kiB/s]
64%|██████▍ | 139M/217M [06:53<03:47, 343kiB/s]
64%|██████▍ | 139M/217M [06:53<03:36, 359kiB/s]
64%|██████▍ | 139M/217M [06:53<03:30, 371kiB/s]
64%|██████▍ | 139M/217M [06:54<03:16, 396kiB/s]
64%|██████▍ | 140M/217M [06:54<03:07, 414kiB/s]
64%|██████▍ | 140M/217M [06:54<02:57, 438kiB/s]
64%|██████▍ | 140M/217M [06:54<02:48, 461kiB/s]
64%|██████▍ | 140M/217M [06:54<02:39, 487kiB/s]
64%|██████▍ | 140M/217M [06:54<02:30, 515kiB/s]
64%|██████▍ | 140M/217M [06:54<02:23, 538kiB/s]
64%|██████▍ | 140M/217M [06:54<02:32, 507kiB/s]
64%|██████▍ | 140M/217M [06:54<02:27, 524kiB/s]
64%|██████▍ | 140M/217M [06:55<02:29, 515kiB/s]
64%|██████▍ | 140M/217M [06:55<02:35, 496kiB/s]
64%|██████▍ | 140M/217M [06:55<02:37, 491kiB/s]
65%|██████▍ | 140M/217M [06:55<02:52, 446kiB/s]
65%|██████▍ | 140M/217M [06:55<03:06, 414kiB/s]
65%|██████▍ | 140M/217M [06:55<03:12, 400kiB/s]
65%|██████▍ | 140M/217M [06:55<03:10, 405kiB/s]
65%|██████▍ | 140M/217M [06:55<03:09, 406kiB/s]
65%|██████▍ | 140M/217M [06:56<03:03, 418kiB/s]
65%|██████▍ | 140M/217M [06:56<02:54, 441kiB/s]
65%|██████▍ | 141M/217M [06:56<02:47, 459kiB/s]
65%|██████▍ | 141M/217M [06:56<02:40, 479kiB/s]
65%|██████▍ | 141M/217M [06:56<02:33, 498kiB/s]
65%|██████▍ | 141M/217M [06:56<02:27, 519kiB/s]
65%|██████▍ | 141M/217M [06:56<02:21, 541kiB/s]
65%|██████▍ | 141M/217M [06:56<02:18, 553kiB/s]
65%|██████▍ | 141M/217M [06:57<02:10, 585kiB/s]
65%|██████▍ | 141M/217M [06:57<02:06, 602kiB/s]
65%|██████▍ | 141M/217M [06:57<02:02, 621kiB/s]
65%|██████▍ | 141M/217M [06:57<01:57, 650kiB/s]
65%|██████▌ | 141M/217M [06:57<01:54, 667kiB/s]
65%|██████▌ | 141M/217M [06:57<02:03, 616kiB/s]
65%|██████▌ | 141M/217M [06:57<01:55, 659kiB/s]
65%|██████▌ | 141M/217M [06:57<02:07, 595kiB/s]
65%|██████▌ | 142M/217M [06:57<02:17, 552kiB/s]
65%|██████▌ | 142M/217M [06:58<02:37, 480kiB/s]
65%|██████▌ | 142M/217M [06:58<02:42, 466kiB/s]
65%|██████▌ | 142M/217M [06:58<02:47, 453kiB/s]
65%|██████▌ | 142M/217M [06:58<02:49, 446kiB/s]
65%|██████▌ | 142M/217M [06:58<02:49, 446kiB/s]
65%|██████▌ | 142M/217M [06:58<03:25, 367kiB/s]
65%|██████▌ | 142M/217M [06:58<02:44, 459kiB/s]
65%|██████▌ | 142M/217M [06:59<02:48, 447kiB/s]
65%|██████▌ | 142M/217M [06:59<02:48, 447kiB/s]
65%|██████▌ | 142M/217M [06:59<02:51, 439kiB/s]
65%|██████▌ | 142M/217M [06:59<02:49, 444kiB/s]
65%|██████▌ | 142M/217M [06:59<02:46, 452kiB/s]
65%|██████▌ | 142M/217M [06:59<02:43, 459kiB/s]
65%|██████▌ | 142M/217M [06:59<02:38, 473kiB/s]
66%|██████▌ | 142M/217M [06:59<02:33, 488kiB/s]
66%|██████▌ | 142M/217M [06:59<02:28, 504kiB/s]
66%|██████▌ | 142M/217M [07:00<02:20, 532kiB/s]
66%|██████▌ | 143M/217M [07:00<02:16, 548kiB/s]
66%|██████▌ | 143M/217M [07:00<02:24, 516kiB/s]
66%|██████▌ | 143M/217M [07:00<02:21, 528kiB/s]
66%|██████▌ | 143M/217M [07:00<02:23, 520kiB/s]
66%|██████▌ | 143M/217M [07:00<02:23, 518kiB/s]
66%|██████▌ | 143M/217M [07:00<02:30, 493kiB/s]
66%|██████▌ | 143M/217M [07:00<02:27, 503kiB/s]
66%|██████▌ | 143M/217M [07:01<02:23, 520kiB/s]
66%|██████▌ | 143M/217M [07:01<02:21, 526kiB/s]
66%|██████▌ | 143M/217M [07:01<02:18, 536kiB/s]
66%|██████▌ | 143M/217M [07:01<02:15, 548kiB/s]
66%|██████▌ | 143M/217M [07:01<02:10, 568kiB/s]
66%|██████▌ | 143M/217M [07:01<02:08, 578kiB/s]
66%|██████▌ | 143M/217M [07:01<01:57, 630kiB/s]
66%|██████▌ | 143M/217M [07:01<02:03, 600kiB/s]
66%|██████▌ | 144M/217M [07:01<02:07, 578kiB/s]
66%|██████▌ | 144M/217M [07:02<02:27, 500kiB/s]
66%|██████▌ | 144M/217M [07:02<02:39, 462kiB/s]
66%|██████▌ | 144M/217M [07:02<03:24, 359kiB/s]
66%|██████▌ | 144M/217M [07:02<03:24, 359kiB/s]
66%|██████▌ | 144M/217M [07:02<03:32, 345kiB/s]
66%|██████▌ | 144M/217M [07:02<03:52, 316kiB/s]
66%|██████▌ | 144M/217M [07:02<03:49, 321kiB/s]
66%|██████▌ | 144M/217M [07:03<04:22, 280kiB/s]
66%|██████▌ | 144M/217M [07:03<03:59, 306kiB/s]
66%|██████▌ | 144M/217M [07:03<04:04, 300kiB/s]
66%|██████▋ | 144M/217M [07:03<04:45, 257kiB/s]
66%|██████▋ | 144M/217M [07:03<04:19, 282kiB/s]
66%|██████▋ | 144M/217M [07:03<04:32, 268kiB/s]
66%|██████▋ | 144M/217M [07:03<04:42, 259kiB/s]
66%|██████▋ | 144M/217M [07:04<04:40, 261kiB/s]
66%|██████▋ | 144M/217M [07:04<04:31, 269kiB/s]
66%|██████▋ | 144M/217M [07:04<04:18, 283kiB/s]
66%|██████▋ | 144M/217M [07:04<04:14, 287kiB/s]
66%|██████▋ | 144M/217M [07:04<03:54, 311kiB/s]
66%|██████▋ | 144M/217M [07:04<03:38, 335kiB/s]
66%|██████▋ | 144M/217M [07:04<03:26, 353kiB/s]
66%|██████▋ | 144M/217M [07:04<03:10, 383kiB/s]
66%|██████▋ | 144M/217M [07:04<03:01, 402kiB/s]
67%|██████▋ | 144M/217M [07:05<03:07, 387kiB/s]
67%|██████▋ | 145M/217M [07:05<03:07, 388kiB/s]
67%|██████▋ | 145M/217M [07:05<03:11, 380kiB/s]
67%|██████▋ | 145M/217M [07:05<03:13, 375kiB/s]
67%|██████▋ | 145M/217M [07:05<03:05, 390kiB/s]
67%|██████▋ | 145M/217M [07:05<03:04, 393kiB/s]
67%|██████▋ | 145M/217M [07:05<03:03, 395kiB/s]
67%|██████▋ | 145M/217M [07:05<02:53, 417kiB/s]
67%|██████▋ | 145M/217M [07:06<02:53, 418kiB/s]
67%|██████▋ | 145M/217M [07:06<03:04, 392kiB/s]
67%|██████▋ | 145M/217M [07:06<03:19, 363kiB/s]
67%|██████▋ | 145M/217M [07:06<03:28, 346kiB/s]
67%|██████▋ | 145M/217M [07:06<03:41, 327kiB/s]
67%|██████▋ | 145M/217M [07:06<03:46, 318kiB/s]
67%|██████▋ | 145M/217M [07:06<03:53, 309kiB/s]
67%|██████▋ | 145M/217M [07:06<03:54, 308kiB/s]
67%|██████▋ | 145M/217M [07:07<03:52, 310kiB/s]
67%|██████▋ | 145M/217M [07:07<03:39, 328kiB/s]
67%|██████▋ | 145M/217M [07:07<03:29, 343kiB/s]
67%|██████▋ | 145M/217M [07:07<03:17, 364kiB/s]
67%|██████▋ | 145M/217M [07:07<03:09, 378kiB/s]
67%|██████▋ | 145M/217M [07:07<02:59, 401kiB/s]
67%|██████▋ | 145M/217M [07:07<02:50, 420kiB/s]
67%|██████▋ | 146M/217M [07:07<03:07, 384kiB/s]
67%|██████▋ | 146M/217M [07:08<02:47, 429kiB/s]
67%|██████▋ | 146M/217M [07:08<02:53, 414kiB/s]
67%|██████▋ | 146M/217M [07:08<02:53, 413kiB/s]
67%|██████▋ | 146M/217M [07:08<02:51, 417kiB/s]
67%|██████▋ | 146M/217M [07:08<02:57, 403kiB/s]
67%|██████▋ | 146M/217M [07:08<02:58, 401kiB/s]
67%|██████▋ | 146M/217M [07:08<03:20, 355kiB/s]
67%|██████▋ | 146M/217M [07:08<03:23, 350kiB/s]
67%|██████▋ | 146M/217M [07:09<03:21, 353kiB/s]
67%|██████▋ | 146M/217M [07:09<03:17, 360kiB/s]
67%|██████▋ | 146M/217M [07:09<03:09, 377kiB/s]
67%|██████▋ | 146M/217M [07:09<02:59, 397kiB/s]
67%|██████▋ | 146M/217M [07:09<02:53, 410kiB/s]
67%|██████▋ | 146M/217M [07:09<02:44, 433kiB/s]
67%|██████▋ | 146M/217M [07:09<02:36, 454kiB/s]
67%|██████▋ | 146M/217M [07:09<02:27, 482kiB/s]
67%|██████▋ | 146M/217M [07:10<02:21, 502kiB/s]
67%|██████▋ | 147M/217M [07:10<02:15, 522kiB/s]
67%|██████▋ | 147M/217M [07:10<02:28, 476kiB/s]
67%|██████▋ | 147M/217M [07:10<02:15, 522kiB/s]
68%|██████▊ | 147M/217M [07:10<02:15, 521kiB/s]
68%|██████▊ | 147M/217M [07:10<02:18, 511kiB/s]
68%|██████▊ | 147M/217M [07:10<02:24, 487kiB/s]
68%|██████▊ | 147M/217M [07:10<02:26, 482kiB/s]
68%|██████▊ | 147M/217M [07:11<02:38, 445kiB/s]
68%|██████▊ | 147M/217M [07:11<02:48, 418kiB/s]
68%|██████▊ | 147M/217M [07:11<03:14, 361kiB/s]
68%|██████▊ | 147M/217M [07:11<03:07, 374kiB/s]
68%|██████▊ | 147M/217M [07:11<03:45, 311kiB/s]
68%|██████▊ | 147M/217M [07:11<03:58, 294kiB/s]
68%|██████▊ | 147M/217M [07:11<04:15, 275kiB/s]
68%|██████▊ | 147M/217M [07:12<04:26, 264kiB/s]
68%|██████▊ | 147M/217M [07:12<04:25, 264kiB/s]
68%|██████▊ | 147M/217M [07:12<04:16, 273kiB/s]
68%|██████▊ | 147M/217M [07:12<04:10, 280kiB/s]
68%|██████▊ | 147M/217M [07:12<04:02, 289kiB/s]
68%|██████▊ | 147M/217M [07:12<03:43, 313kiB/s]
68%|██████▊ | 147M/217M [07:12<03:28, 335kiB/s]
68%|██████▊ | 147M/217M [07:12<03:15, 357kiB/s]
68%|██████▊ | 148M/217M [07:13<03:04, 378kiB/s]
68%|██████▊ | 148M/217M [07:13<02:55, 398kiB/s]
68%|██████▊ | 148M/217M [07:13<02:41, 430kiB/s]
68%|██████▊ | 148M/217M [07:13<02:33, 452kiB/s]
68%|██████▊ | 148M/217M [07:13<02:26, 475kiB/s]
68%|██████▊ | 148M/217M [07:13<02:40, 433kiB/s]
68%|██████▊ | 148M/217M [07:13<02:35, 446kiB/s]
68%|██████▊ | 148M/217M [07:13<02:43, 424kiB/s]
68%|██████▊ | 148M/217M [07:14<02:49, 410kiB/s]
68%|██████▊ | 148M/217M [07:14<03:29, 330kiB/s]
68%|██████▊ | 148M/217M [07:14<03:40, 315kiB/s]
68%|██████▊ | 148M/217M [07:14<04:01, 287kiB/s]
68%|██████▊ | 148M/217M [07:14<04:12, 274kiB/s]
68%|██████▊ | 148M/217M [07:14<04:20, 265kiB/s]
68%|██████▊ | 148M/217M [07:14<04:19, 266kiB/s]
68%|██████▊ | 148M/217M [07:15<04:06, 280kiB/s]
68%|██████▊ | 148M/217M [07:15<03:58, 289kiB/s]
68%|██████▊ | 148M/217M [07:15<03:43, 308kiB/s]
68%|██████▊ | 148M/217M [07:15<03:25, 335kiB/s]
68%|██████▊ | 148M/217M [07:15<03:16, 351kiB/s]
68%|██████▊ | 148M/217M [07:15<03:06, 369kiB/s]
68%|██████▊ | 148M/217M [07:15<02:55, 391kiB/s]
68%|██████▊ | 149M/217M [07:15<03:09, 362kiB/s]
68%|██████▊ | 149M/217M [07:16<02:53, 397kiB/s]
68%|██████▊ | 149M/217M [07:16<02:57, 387kiB/s]
68%|██████▊ | 149M/217M [07:16<02:58, 383kiB/s]
68%|██████▊ | 149M/217M [07:16<02:55, 391kiB/s]
68%|██████▊ | 149M/217M [07:16<02:51, 399kiB/s]
69%|██████▊ | 149M/217M [07:16<02:47, 408kiB/s]
69%|██████▊ | 149M/217M [07:16<02:46, 411kiB/s]
69%|██████▊ | 149M/217M [07:16<02:38, 430kiB/s]
69%|██████▊ | 149M/217M [07:16<02:34, 441kiB/s]
69%|██████▊ | 149M/217M [07:17<02:25, 470kiB/s]
69%|██████▊ | 149M/217M [07:17<02:19, 487kiB/s]
69%|██████▊ | 149M/217M [07:17<02:14, 507kiB/s]
69%|██████▊ | 149M/217M [07:17<02:08, 527kiB/s]
69%|██████▊ | 149M/217M [07:17<02:09, 524kiB/s]
69%|██████▊ | 149M/217M [07:17<02:12, 514kiB/s]
69%|██████▉ | 149M/217M [07:17<02:15, 500kiB/s]
69%|██████▉ | 149M/217M [07:17<02:16, 498kiB/s]
69%|██████▉ | 150M/217M [07:18<02:19, 485kiB/s]
69%|██████▉ | 150M/217M [07:18<02:34, 440kiB/s]
69%|██████▉ | 150M/217M [07:18<02:26, 461kiB/s]
69%|██████▉ | 150M/217M [07:18<02:37, 429kiB/s]
69%|██████▉ | 150M/217M [07:18<03:04, 366kiB/s]
69%|██████▉ | 150M/217M [07:18<03:08, 359kiB/s]
69%|██████▉ | 150M/217M [07:18<03:31, 319kiB/s]
69%|██████▉ | 150M/217M [07:19<03:28, 324kiB/s]
69%|██████▉ | 150M/217M [07:19<03:15, 344kiB/s]
69%|██████▉ | 150M/217M [07:19<03:10, 354kiB/s]
69%|██████▉ | 150M/217M [07:19<03:01, 372kiB/s]
69%|██████▉ | 150M/217M [07:19<02:49, 396kiB/s]
69%|██████▉ | 150M/217M [07:19<02:42, 413kiB/s]
69%|██████▉ | 150M/217M [07:19<02:32, 440kiB/s]
69%|██████▉ | 150M/217M [07:19<02:44, 407kiB/s]
69%|██████▉ | 150M/217M [07:19<02:28, 452kiB/s]
69%|██████▉ | 150M/217M [07:20<02:34, 433kiB/s]
69%|██████▉ | 150M/217M [07:20<02:35, 430kiB/s]
69%|██████▉ | 150M/217M [07:20<02:35, 429kiB/s]
69%|██████▉ | 150M/217M [07:20<02:31, 442kiB/s]
69%|██████▉ | 151M/217M [07:20<02:29, 447kiB/s]
69%|██████▉ | 151M/217M [07:20<02:27, 453kiB/s]
69%|██████▉ | 151M/217M [07:20<02:23, 466kiB/s]
69%|██████▉ | 151M/217M [07:20<02:17, 486kiB/s]
69%|██████▉ | 151M/217M [07:21<02:33, 434kiB/s]
69%|██████▉ | 151M/217M [07:21<02:12, 500kiB/s]
69%|██████▉ | 151M/217M [07:21<02:15, 491kiB/s]
69%|██████▉ | 151M/217M [07:21<02:15, 488kiB/s]
70%|██████▉ | 151M/217M [07:21<02:30, 440kiB/s]
70%|██████▉ | 151M/217M [07:21<02:31, 437kiB/s]
70%|██████▉ | 151M/217M [07:21<03:04, 358kiB/s]
70%|██████▉ | 151M/217M [07:22<03:26, 320kiB/s]
70%|██████▉ | 151M/217M [07:22<03:40, 300kiB/s]
70%|██████▉ | 151M/217M [07:22<04:05, 269kiB/s]
70%|██████▉ | 151M/217M [07:22<04:17, 256kiB/s]
70%|██████▉ | 151M/217M [07:22<04:24, 249kiB/s]
70%|██████▉ | 151M/217M [07:22<04:16, 257kiB/s]
70%|██████▉ | 151M/217M [07:22<04:37, 238kiB/s]
70%|██████▉ | 151M/217M [07:23<04:18, 255kiB/s]
70%|██████▉ | 151M/217M [07:23<04:21, 252kiB/s]
70%|██████▉ | 151M/217M [07:23<04:12, 260kiB/s]
70%|██████▉ | 151M/217M [07:23<04:12, 261kiB/s]
70%|██████▉ | 152M/217M [07:23<04:05, 268kiB/s]
70%|██████▉ | 152M/217M [07:23<04:02, 272kiB/s]
70%|██████▉ | 152M/217M [07:23<04:03, 270kiB/s]
70%|██████▉ | 152M/217M [07:23<04:54, 223kiB/s]
70%|██████▉ | 152M/217M [07:24<04:37, 236kiB/s]
70%|██████▉ | 152M/217M [07:24<05:08, 212kiB/s]
70%|██████▉ | 152M/217M [07:24<05:14, 208kiB/s]
70%|██████▉ | 152M/217M [07:24<05:14, 209kiB/s]
70%|██████▉ | 152M/217M [07:24<04:50, 226kiB/s]
70%|██████▉ | 152M/217M [07:24<04:33, 240kiB/s]
70%|██████▉ | 152M/217M [07:24<04:17, 254kiB/s]
70%|██████▉ | 152M/217M [07:25<04:03, 269kiB/s]
70%|██████▉ | 152M/217M [07:25<03:38, 300kiB/s]
70%|██████▉ | 152M/217M [07:25<03:24, 319kiB/s]
70%|██████▉ | 152M/217M [07:25<03:10, 342kiB/s]
70%|██████▉ | 152M/217M [07:25<02:57, 367kiB/s]
70%|███████ | 152M/217M [07:25<02:49, 384kiB/s]
70%|███████ | 152M/217M [07:25<02:40, 406kiB/s]
70%|███████ | 152M/217M [07:25<02:33, 423kiB/s]
70%|███████ | 152M/217M [07:26<02:31, 428kiB/s]
70%|███████ | 152M/217M [07:26<02:40, 404kiB/s]
70%|███████ | 152M/217M [07:26<02:43, 397kiB/s]
70%|███████ | 152M/217M [07:26<02:42, 398kiB/s]
70%|███████ | 152M/217M [07:26<02:38, 408kiB/s]
70%|███████ | 153M/217M [07:26<02:44, 393kiB/s]
70%|███████ | 153M/217M [07:26<02:44, 394kiB/s]
70%|███████ | 153M/217M [07:26<02:47, 386kiB/s]
70%|███████ | 153M/217M [07:27<03:04, 350kiB/s]
70%|███████ | 153M/217M [07:27<03:51, 279kiB/s]
70%|███████ | 153M/217M [07:27<03:48, 282kiB/s]
70%|███████ | 153M/217M [07:27<03:48, 283kiB/s]
70%|███████ | 153M/217M [07:27<03:47, 284kiB/s]
70%|███████ | 153M/217M [07:27<04:05, 263kiB/s]
70%|███████ | 153M/217M [07:27<04:21, 246kiB/s]
70%|███████ | 153M/217M [07:27<04:23, 244kiB/s]
70%|███████ | 153M/217M [07:28<04:11, 256kiB/s]
70%|███████ | 153M/217M [07:28<04:00, 268kiB/s]
70%|███████ | 153M/217M [07:28<03:50, 278kiB/s]
70%|███████ | 153M/217M [07:28<03:31, 303kiB/s]
70%|███████ | 153M/217M [07:28<03:18, 324kiB/s]
70%|███████ | 153M/217M [07:28<03:02, 351kiB/s]
70%|███████ | 153M/217M [07:28<02:52, 371kiB/s]
71%|███████ | 153M/217M [07:28<02:45, 386kiB/s]
71%|███████ | 153M/217M [07:29<02:36, 410kiB/s]
71%|███████ | 153M/217M [07:29<02:47, 383kiB/s]
71%|███████ | 153M/217M [07:29<02:29, 427kiB/s]
71%|███████ | 153M/217M [07:29<02:32, 419kiB/s]
71%|███████ | 153M/217M [07:29<02:36, 409kiB/s]
71%|███████ | 154M/217M [07:29<02:32, 418kiB/s]
71%|███████ | 154M/217M [07:29<02:30, 422kiB/s]
71%|███████ | 154M/217M [07:29<02:41, 395kiB/s]
71%|███████ | 154M/217M [07:30<02:40, 397kiB/s]
71%|███████ | 154M/217M [07:30<02:43, 388kiB/s]
71%|███████ | 154M/217M [07:30<02:47, 380kiB/s]
71%|███████ | 154M/217M [07:30<02:44, 385kiB/s]
71%|███████ | 154M/217M [07:30<02:40, 396kiB/s]
71%|███████ | 154M/217M [07:30<02:38, 400kiB/s]
71%|███████ | 154M/217M [07:30<02:33, 413kiB/s]
71%|███████ | 154M/217M [07:30<02:26, 432kiB/s]
71%|███████ | 154M/217M [07:31<02:19, 453kiB/s]
71%|███████ | 154M/217M [07:31<02:12, 476kiB/s]
71%|███████ | 154M/217M [07:31<02:25, 434kiB/s]
71%|███████ | 154M/217M [07:31<02:29, 422kiB/s]
71%|███████ | 154M/217M [07:31<02:26, 429kiB/s]
71%|███████ | 154M/217M [07:31<02:35, 404kiB/s]
71%|███████ | 154M/217M [07:31<02:58, 351kiB/s]
71%|███████ | 154M/217M [07:32<03:00, 347kiB/s]
71%|███████ | 154M/217M [07:32<03:01, 347kiB/s]
71%|███████ | 155M/217M [07:32<03:13, 324kiB/s]
71%|███████ | 155M/217M [07:32<03:02, 344kiB/s]
71%|███████ | 155M/217M [07:32<02:51, 365kiB/s]
71%|███████ | 155M/217M [07:32<02:45, 378kiB/s]
71%|███████ | 155M/217M [07:32<02:36, 400kiB/s]
71%|███████ | 155M/217M [07:32<02:29, 417kiB/s]
71%|███████▏ | 155M/217M [07:32<02:26, 428kiB/s]
71%|███████▏ | 155M/217M [07:33<02:25, 428kiB/s]
71%|███████▏ | 155M/217M [07:33<02:35, 402kiB/s]
71%|███████▏ | 155M/217M [07:33<02:52, 361kiB/s]
71%|███████▏ | 155M/217M [07:33<02:57, 350kiB/s]
71%|███████▏ | 155M/217M [07:33<03:00, 345kiB/s]
71%|███████▏ | 155M/217M [07:33<03:02, 340kiB/s]
71%|███████▏ | 155M/217M [07:33<03:15, 319kiB/s]
71%|███████▏ | 155M/217M [07:33<03:38, 285kiB/s]
71%|███████▏ | 155M/217M [07:34<03:16, 317kiB/s]
71%|███████▏ | 155M/217M [07:34<03:16, 315kiB/s]
71%|███████▏ | 155M/217M [07:34<03:19, 310kiB/s]
71%|███████▏ | 155M/217M [07:34<03:16, 316kiB/s]
72%|███████▏ | 155M/217M [07:34<03:07, 331kiB/s]
72%|███████▏ | 155M/217M [07:34<03:00, 343kiB/s]
72%|███████▏ | 155M/217M [07:34<02:55, 352kiB/s]
72%|███████▏ | 156M/217M [07:35<02:46, 370kiB/s]
72%|███████▏ | 156M/217M [07:35<02:38, 389kiB/s]
72%|███████▏ | 156M/217M [07:35<02:32, 404kiB/s]
72%|███████▏ | 156M/217M [07:35<02:26, 420kiB/s]
72%|███████▏ | 156M/217M [07:35<02:16, 449kiB/s]
72%|███████▏ | 156M/217M [07:35<02:22, 431kiB/s]
72%|███████▏ | 156M/217M [07:35<02:14, 457kiB/s]
72%|███████▏ | 156M/217M [07:35<02:18, 444kiB/s]
72%|███████▏ | 156M/217M [07:36<02:20, 437kiB/s]
72%|███████▏ | 156M/217M [07:36<02:18, 442kiB/s]
72%|███████▏ | 156M/217M [07:36<02:26, 418kiB/s]
72%|███████▏ | 156M/217M [07:36<02:36, 391kiB/s]
72%|███████▏ | 156M/217M [07:36<02:40, 381kiB/s]
72%|███████▏ | 156M/217M [07:36<02:42, 377kiB/s]
72%|███████▏ | 156M/217M [07:36<02:41, 377kiB/s]
72%|███████▏ | 156M/217M [07:36<02:36, 389kiB/s]
72%|███████▏ | 156M/217M [07:37<02:28, 409kiB/s]
72%|███████▏ | 156M/217M [07:37<02:24, 420kiB/s]
72%|███████▏ | 156M/217M [07:37<02:19, 435kiB/s]
72%|███████▏ | 157M/217M [07:37<02:22, 427kiB/s]
72%|███████▏ | 157M/217M [07:37<02:23, 422kiB/s]
72%|███████▏ | 157M/217M [07:37<02:26, 414kiB/s]
72%|███████▏ | 157M/217M [07:37<02:39, 380kiB/s]
72%|███████▏ | 157M/217M [07:37<02:41, 374kiB/s]
72%|███████▏ | 157M/217M [07:37<02:46, 364kiB/s]
72%|███████▏ | 157M/217M [07:38<03:06, 325kiB/s]
72%|███████▏ | 157M/217M [07:38<03:04, 328kiB/s]
72%|███████▏ | 157M/217M [07:38<02:58, 338kiB/s]
72%|███████▏ | 157M/217M [07:38<02:49, 356kiB/s]
72%|███████▏ | 157M/217M [07:38<02:41, 375kiB/s]
72%|███████▏ | 157M/217M [07:38<02:34, 391kiB/s]
72%|███████▏ | 157M/217M [07:38<02:25, 413kiB/s]
72%|███████▏ | 157M/217M [07:38<02:21, 427kiB/s]
72%|███████▏ | 157M/217M [07:39<02:13, 451kiB/s]
72%|███████▏ | 157M/217M [07:39<02:06, 476kiB/s]
72%|███████▏ | 157M/217M [07:39<02:00, 496kiB/s]
72%|███████▏ | 157M/217M [07:39<01:55, 518kiB/s]
72%|███████▏ | 157M/217M [07:39<01:51, 539kiB/s]
72%|███████▏ | 157M/217M [07:39<02:02, 489kiB/s]
73%|███████▎ | 158M/217M [07:39<01:50, 542kiB/s]
73%|███████▎ | 158M/217M [07:39<01:51, 537kiB/s]
73%|███████▎ | 158M/217M [07:40<01:51, 533kiB/s]
73%|███████▎ | 158M/217M [07:40<02:00, 493kiB/s]
73%|███████▎ | 158M/217M [07:40<01:57, 504kiB/s]
73%|███████▎ | 158M/217M [07:40<01:54, 517kiB/s]
73%|███████▎ | 158M/217M [07:40<01:52, 527kiB/s]
73%|███████▎ | 158M/217M [07:40<02:07, 464kiB/s]
73%|███████▎ | 158M/217M [07:40<02:11, 452kiB/s]
73%|███████▎ | 158M/217M [07:40<02:28, 399kiB/s]
73%|███████▎ | 158M/217M [07:41<02:38, 372kiB/s]
73%|███████▎ | 158M/217M [07:41<02:54, 338kiB/s]
73%|███████▎ | 158M/217M [07:41<03:04, 321kiB/s]
73%|███████▎ | 158M/217M [07:41<03:14, 303kiB/s]
73%|███████▎ | 158M/217M [07:41<03:16, 301kiB/s]
73%|███████▎ | 158M/217M [07:41<03:21, 292kiB/s]
73%|███████▎ | 158M/217M [07:42<03:26, 285kiB/s]
73%|███████▎ | 158M/217M [07:42<03:15, 301kiB/s]
73%|███████▎ | 158M/217M [07:42<03:20, 293kiB/s]
73%|███████▎ | 158M/217M [07:42<03:19, 294kiB/s]
73%|███████▎ | 159M/217M [07:42<03:48, 257kiB/s]
73%|███████▎ | 159M/217M [07:42<03:24, 286kiB/s]
73%|███████▎ | 159M/217M [07:42<04:05, 239kiB/s]
73%|███████▎ | 159M/217M [07:43<04:14, 231kiB/s]
73%|███████▎ | 159M/217M [07:43<04:35, 213kiB/s]
73%|███████▎ | 159M/217M [07:43<04:41, 208kiB/s]
73%|███████▎ | 159M/217M [07:43<04:33, 214kiB/s]
73%|███████▎ | 159M/217M [07:43<04:18, 227kiB/s]
73%|███████▎ | 159M/217M [07:43<04:06, 237kiB/s]
73%|███████▎ | 159M/217M [07:43<03:48, 256kiB/s]
73%|███████▎ | 159M/217M [07:44<03:39, 266kiB/s]
73%|███████▎ | 159M/217M [07:44<03:58, 245kiB/s]
73%|███████▎ | 159M/217M [07:44<03:17, 296kiB/s]
73%|███████▎ | 159M/217M [07:44<03:17, 295kiB/s]
73%|███████▎ | 159M/217M [07:44<03:36, 269kiB/s]
73%|███████▎ | 159M/217M [07:44<03:41, 263kiB/s]
73%|███████▎ | 159M/217M [07:44<03:54, 248kiB/s]
73%|███████▎ | 159M/217M [07:44<03:58, 244kiB/s]
73%|███████▎ | 159M/217M [07:45<03:52, 250kiB/s]
73%|███████▎ | 159M/217M [07:45<03:42, 261kiB/s]
73%|███████▎ | 159M/217M [07:45<03:30, 276kiB/s]
73%|███████▎ | 159M/217M [07:45<03:36, 268kiB/s]
73%|███████▎ | 159M/217M [07:45<03:55, 247kiB/s]
73%|███████▎ | 159M/217M [07:45<03:50, 251kiB/s]
73%|███████▎ | 159M/217M [07:45<03:51, 250kiB/s]
73%|███████▎ | 159M/217M [07:45<03:42, 260kiB/s]
73%|███████▎ | 159M/217M [07:46<03:43, 259kiB/s]
73%|███████▎ | 159M/217M [07:46<03:47, 255kiB/s]
73%|███████▎ | 159M/217M [07:46<04:04, 237kiB/s]
73%|███████▎ | 159M/217M [07:46<04:04, 236kiB/s]
73%|███████▎ | 160M/217M [07:46<03:56, 245kiB/s]
73%|███████▎ | 160M/217M [07:46<03:50, 251kiB/s]
73%|███████▎ | 160M/217M [07:46<03:34, 269kiB/s]
73%|███████▎ | 160M/217M [07:46<03:26, 279kiB/s]
73%|███████▎ | 160M/217M [07:47<03:08, 306kiB/s]
74%|███████▎ | 160M/217M [07:47<02:56, 326kiB/s]
74%|███████▎ | 160M/217M [07:47<02:45, 348kiB/s]
74%|███████▎ | 160M/217M [07:47<02:37, 365kiB/s]
74%|███████▎ | 160M/217M [07:47<02:43, 352kiB/s]
74%|███████▎ | 160M/217M [07:47<02:37, 363kiB/s]
74%|███████▎ | 160M/217M [07:47<02:37, 364kiB/s]
74%|███████▎ | 160M/217M [07:47<02:36, 365kiB/s]
74%|███████▎ | 160M/217M [07:48<02:33, 373kiB/s]
74%|███████▎ | 160M/217M [07:48<02:27, 389kiB/s]
74%|███████▎ | 160M/217M [07:48<02:24, 396kiB/s]
74%|███████▎ | 160M/217M [07:48<02:20, 406kiB/s]
74%|███████▍ | 160M/217M [07:48<02:22, 400kiB/s]
74%|███████▍ | 160M/217M [07:48<02:29, 380kiB/s]
74%|███████▍ | 160M/217M [07:48<02:31, 376kiB/s]
74%|███████▍ | 160M/217M [07:48<02:28, 383kiB/s]
74%|███████▍ | 160M/217M [07:49<02:44, 345kiB/s]
74%|███████▍ | 160M/217M [07:49<02:35, 364kiB/s]
74%|███████▍ | 161M/217M [07:49<02:36, 363kiB/s]
74%|███████▍ | 161M/217M [07:49<02:40, 354kiB/s]
74%|███████▍ | 161M/217M [07:49<02:41, 351kiB/s]
74%|███████▍ | 161M/217M [07:49<02:59, 316kiB/s]
74%|███████▍ | 161M/217M [07:49<02:51, 330kiB/s]
74%|███████▍ | 161M/217M [07:49<02:57, 318kiB/s]
74%|███████▍ | 161M/217M [07:50<02:42, 348kiB/s]
74%|███████▍ | 161M/217M [07:50<02:42, 347kiB/s]
74%|███████▍ | 161M/217M [07:50<02:43, 346kiB/s]
74%|███████▍ | 161M/217M [07:50<02:42, 347kiB/s]
74%|███████▍ | 161M/217M [07:50<02:52, 327kiB/s]
74%|███████▍ | 161M/217M [07:50<02:45, 340kiB/s]
74%|███████▍ | 161M/217M [07:50<02:37, 358kiB/s]
74%|███████▍ | 161M/217M [07:50<02:48, 334kiB/s]
74%|███████▍ | 161M/217M [07:51<02:30, 373kiB/s]
74%|███████▍ | 161M/217M [07:51<02:37, 357kiB/s]
74%|███████▍ | 161M/217M [07:51<02:36, 358kiB/s]
74%|███████▍ | 161M/217M [07:51<02:35, 361kiB/s]
74%|███████▍ | 161M/217M [07:51<02:30, 371kiB/s]
74%|███████▍ | 161M/217M [07:51<02:46, 336kiB/s]
74%|███████▍ | 161M/217M [07:51<02:33, 363kiB/s]
74%|███████▍ | 161M/217M [07:52<02:36, 357kiB/s]
74%|███████▍ | 162M/217M [07:52<02:34, 361kiB/s]
74%|███████▍ | 162M/217M [07:52<02:58, 313kiB/s]
74%|███████▍ | 162M/217M [07:52<02:41, 345kiB/s]
74%|███████▍ | 162M/217M [07:52<02:41, 344kiB/s]
74%|███████▍ | 162M/217M [07:52<02:43, 340kiB/s]
74%|███████▍ | 162M/217M [07:52<02:47, 331kiB/s]
74%|███████▍ | 162M/217M [07:52<02:59, 310kiB/s]
74%|███████▍ | 162M/217M [07:53<02:50, 325kiB/s]
74%|███████▍ | 162M/217M [07:53<02:43, 338kiB/s]
75%|███████▍ | 162M/217M [07:53<02:34, 358kiB/s]
75%|███████▍ | 162M/217M [07:53<02:27, 375kiB/s]
75%|███████▍ | 162M/217M [07:53<02:19, 396kiB/s]
75%|███████▍ | 162M/217M [07:53<02:13, 412kiB/s]
75%|███████▍ | 162M/217M [07:53<02:08, 431kiB/s]
75%|███████▍ | 162M/217M [07:53<02:01, 455kiB/s]
75%|███████▍ | 162M/217M [07:54<01:54, 481kiB/s]
75%|███████▍ | 162M/217M [07:54<01:48, 506kiB/s]
75%|███████▍ | 162M/217M [07:54<01:43, 531kiB/s]
75%|███████▍ | 162M/217M [07:54<01:39, 549kiB/s]
75%|███████▍ | 163M/217M [07:54<01:35, 571kiB/s]
75%|███████▍ | 163M/217M [07:54<01:32, 590kiB/s]
75%|███████▍ | 163M/217M [07:54<01:28, 619kiB/s]
75%|███████▍ | 163M/217M [07:54<01:24, 642kiB/s]
75%|███████▍ | 163M/217M [07:54<01:22, 663kiB/s]
75%|███████▍ | 163M/217M [07:55<01:18, 690kiB/s]
75%|███████▌ | 163M/217M [07:55<01:17, 702kiB/s]
75%|███████▌ | 163M/217M [07:55<01:14, 723kiB/s]
75%|███████▌ | 163M/217M [07:55<01:11, 754kiB/s]
75%|███████▌ | 163M/217M [07:55<01:08, 789kiB/s]
75%|███████▌ | 163M/217M [07:55<01:07, 804kiB/s]
75%|███████▌ | 163M/217M [07:55<01:06, 805kiB/s]
75%|███████▌ | 164M/217M [07:55<01:07, 802kiB/s]
75%|███████▌ | 164M/217M [07:55<01:11, 747kiB/s]
75%|███████▌ | 164M/217M [07:56<01:15, 708kiB/s]
75%|███████▌ | 164M/217M [07:56<01:17, 688kiB/s]
75%|███████▌ | 164M/217M [07:56<01:15, 704kiB/s]
75%|███████▌ | 164M/217M [07:56<01:14, 716kiB/s]
75%|███████▌ | 164M/217M [07:56<01:12, 734kiB/s]
76%|███████▌ | 164M/217M [07:56<01:10, 749kiB/s]
76%|███████▌ | 164M/217M [07:56<01:09, 765kiB/s]
76%|███████▌ | 164M/217M [07:56<01:08, 776kiB/s]
76%|███████▌ | 164M/217M [07:56<01:11, 741kiB/s]
76%|███████▌ | 164M/217M [07:57<01:12, 732kiB/s]
76%|███████▌ | 164M/217M [07:57<01:18, 676kiB/s]
76%|███████▌ | 165M/217M [07:57<01:19, 666kiB/s]
76%|███████▌ | 165M/217M [07:57<01:18, 668kiB/s]
76%|███████▌ | 165M/217M [07:57<01:20, 650kiB/s]
76%|███████▌ | 165M/217M [07:57<01:19, 658kiB/s]
76%|███████▌ | 165M/217M [07:57<01:16, 686kiB/s]
76%|███████▌ | 165M/217M [07:57<01:15, 693kiB/s]
76%|███████▌ | 165M/217M [07:57<01:14, 704kiB/s]
76%|███████▌ | 165M/217M [07:58<01:12, 719kiB/s]
76%|███████▌ | 165M/217M [07:58<01:09, 745kiB/s]
76%|███████▌ | 165M/217M [07:58<01:07, 765kiB/s]
76%|███████▌ | 165M/217M [07:58<01:07, 764kiB/s]
76%|███████▌ | 165M/217M [07:58<01:11, 729kiB/s]
76%|███████▌ | 166M/217M [07:58<01:12, 719kiB/s]
76%|███████▌ | 166M/217M [07:58<01:15, 686kiB/s]
76%|███████▌ | 166M/217M [07:58<01:15, 686kiB/s]
76%|███████▋ | 166M/217M [07:58<01:18, 661kiB/s]
76%|███████▋ | 166M/217M [07:59<01:15, 680kiB/s]
76%|███████▋ | 166M/217M [07:59<01:13, 698kiB/s]
76%|███████▋ | 166M/217M [07:59<01:12, 710kiB/s]
76%|███████▋ | 166M/217M [07:59<01:10, 722kiB/s]
76%|███████▋ | 166M/217M [07:59<01:08, 749kiB/s]
77%|███████▋ | 166M/217M [07:59<01:09, 735kiB/s]
77%|███████▋ | 166M/217M [07:59<01:13, 694kiB/s]
77%|███████▋ | 166M/217M [07:59<01:16, 664kiB/s]
77%|███████▋ | 166M/217M [08:00<01:24, 605kiB/s]
77%|███████▋ | 167M/217M [08:00<01:23, 607kiB/s]
77%|███████▋ | 167M/217M [08:00<01:23, 609kiB/s]
77%|███████▋ | 167M/217M [08:00<01:22, 617kiB/s]
77%|███████▋ | 167M/217M [08:00<01:20, 627kiB/s]
77%|███████▋ | 167M/217M [08:00<01:18, 640kiB/s]
77%|███████▋ | 167M/217M [08:00<01:15, 666kiB/s]
77%|███████▋ | 167M/217M [08:00<01:17, 648kiB/s]
77%|███████▋ | 167M/217M [08:00<01:17, 649kiB/s]
77%|███████▋ | 167M/217M [08:01<01:22, 610kiB/s]
77%|███████▋ | 167M/217M [08:01<01:22, 609kiB/s]
77%|███████▋ | 167M/217M [08:01<01:32, 540kiB/s]
77%|███████▋ | 167M/217M [08:01<01:44, 478kiB/s]
77%|███████▋ | 167M/217M [08:01<01:47, 463kiB/s]
77%|███████▋ | 167M/217M [08:01<02:02, 407kiB/s]
77%|███████▋ | 167M/217M [08:01<02:01, 411kiB/s]
77%|███████▋ | 167M/217M [08:01<02:14, 370kiB/s]
77%|███████▋ | 167M/217M [08:02<02:19, 357kiB/s]
77%|███████▋ | 168M/217M [08:02<02:18, 360kiB/s]
77%|███████▋ | 168M/217M [08:02<02:10, 380kiB/s]
77%|███████▋ | 168M/217M [08:02<02:05, 395kiB/s]
77%|███████▋ | 168M/217M [08:02<02:01, 408kiB/s]
77%|███████▋ | 168M/217M [08:02<01:56, 426kiB/s]
77%|███████▋ | 168M/217M [08:02<01:50, 447kiB/s]
77%|███████▋ | 168M/217M [08:02<01:46, 465kiB/s]
77%|███████▋ | 168M/217M [08:03<01:41, 485kiB/s]
77%|███████▋ | 168M/217M [08:03<01:37, 507kiB/s]
77%|███████▋ | 168M/217M [08:03<01:32, 530kiB/s]
77%|███████▋ | 168M/217M [08:03<01:40, 490kiB/s]
77%|███████▋ | 168M/217M [08:03<01:34, 518kiB/s]
77%|███████▋ | 168M/217M [08:03<01:37, 505kiB/s]
77%|███████▋ | 168M/217M [08:03<01:39, 493kiB/s]
77%|███████▋ | 168M/217M [08:03<01:46, 458kiB/s]
78%|███████▊ | 168M/217M [08:04<01:45, 463kiB/s]
78%|███████▊ | 168M/217M [08:04<01:52, 435kiB/s]
78%|███████▊ | 169M/217M [08:04<01:54, 425kiB/s]
78%|███████▊ | 169M/217M [08:04<01:57, 415kiB/s]
78%|███████▊ | 169M/217M [08:04<01:54, 425kiB/s]
78%|███████▊ | 169M/217M [08:04<01:51, 436kiB/s]
78%|███████▊ | 169M/217M [08:04<02:03, 393kiB/s]
78%|███████▊ | 169M/217M [08:04<02:10, 373kiB/s]
78%|███████▊ | 169M/217M [08:05<02:21, 343kiB/s]
78%|███████▊ | 169M/217M [08:05<02:37, 307kiB/s]
78%|███████▊ | 169M/217M [08:05<02:45, 293kiB/s]
78%|███████▊ | 169M/217M [08:05<02:52, 280kiB/s]
78%|███████▊ | 169M/217M [08:05<02:48, 287kiB/s]
78%|███████▊ | 169M/217M [08:05<02:44, 294kiB/s]
78%|███████▊ | 169M/217M [08:05<02:23, 336kiB/s]
78%|███████▊ | 169M/217M [08:05<02:33, 314kiB/s]
78%|███████▊ | 169M/217M [08:06<02:24, 335kiB/s]
78%|███████▊ | 169M/217M [08:06<02:14, 359kiB/s]
78%|███████▊ | 169M/217M [08:06<02:08, 373kiB/s]
78%|███████▊ | 169M/217M [08:06<02:01, 395kiB/s]
78%|███████▊ | 169M/217M [08:06<01:56, 411kiB/s]
78%|███████▊ | 169M/217M [08:06<01:51, 431kiB/s]
78%|███████▊ | 169M/217M [08:06<01:45, 455kiB/s]
78%|███████▊ | 169M/217M [08:06<01:45, 455kiB/s]
78%|███████▊ | 170M/217M [08:07<01:50, 432kiB/s]
78%|███████▊ | 170M/217M [08:07<01:51, 429kiB/s]
78%|███████▊ | 170M/217M [08:07<01:50, 430kiB/s]
78%|███████▊ | 170M/217M [08:07<01:47, 443kiB/s]
78%|███████▊ | 170M/217M [08:07<01:46, 445kiB/s]
78%|███████▊ | 170M/217M [08:07<01:44, 456kiB/s]
78%|███████▊ | 170M/217M [08:07<01:42, 464kiB/s]
78%|███████▊ | 170M/217M [08:07<01:38, 482kiB/s]
78%|███████▊ | 170M/217M [08:07<01:35, 498kiB/s]
78%|███████▊ | 170M/217M [08:08<01:32, 510kiB/s]
78%|███████▊ | 170M/217M [08:08<01:27, 540kiB/s]
78%|███████▊ | 170M/217M [08:08<01:24, 559kiB/s]
78%|███████▊ | 170M/217M [08:08<01:28, 533kiB/s]
78%|███████▊ | 170M/217M [08:08<01:25, 549kiB/s]
78%|███████▊ | 170M/217M [08:08<01:26, 545kiB/s]
78%|███████▊ | 170M/217M [08:08<01:29, 524kiB/s]
78%|███████▊ | 170M/217M [08:08<01:29, 524kiB/s]
78%|███████▊ | 171M/217M [08:08<01:27, 532kiB/s]
79%|███████▊ | 171M/217M [08:09<01:45, 444kiB/s]
79%|███████▊ | 171M/217M [08:09<01:50, 423kiB/s]
79%|███████▊ | 171M/217M [08:09<02:17, 338kiB/s]
79%|███████▊ | 171M/217M [08:09<03:18, 235kiB/s]
79%|███████▊ | 171M/217M [08:09<03:11, 244kiB/s]
79%|███████▊ | 171M/217M [08:10<03:35, 216kiB/s]
79%|███████▊ | 171M/217M [08:10<03:45, 206kiB/s]
79%|███████▊ | 171M/217M [08:10<03:43, 208kiB/s]
79%|███████▊ | 171M/217M [08:10<03:33, 218kiB/s]
79%|███████▊ | 171M/217M [08:10<03:17, 234kiB/s]
79%|███████▊ | 171M/217M [08:10<03:05, 249kiB/s]
79%|███████▊ | 171M/217M [08:10<02:52, 268kiB/s]
79%|███████▊ | 171M/217M [08:11<02:35, 297kiB/s]
79%|███████▊ | 171M/217M [08:11<02:23, 322kiB/s]
79%|███████▊ | 171M/217M [08:11<02:15, 342kiB/s]
79%|███████▉ | 171M/217M [08:11<02:07, 362kiB/s]
79%|███████▉ | 171M/217M [08:11<01:59, 386kiB/s]
79%|███████▉ | 171M/217M [08:11<01:53, 405kiB/s]
79%|███████▉ | 171M/217M [08:11<01:48, 423kiB/s]
79%|███████▉ | 171M/217M [08:11<01:41, 454kiB/s]
79%|███████▉ | 171M/217M [08:11<01:36, 477kiB/s]
79%|███████▉ | 171M/217M [08:12<01:31, 499kiB/s]
79%|███████▉ | 172M/217M [08:12<01:26, 526kiB/s]
79%|███████▉ | 172M/217M [08:12<01:24, 542kiB/s]
79%|███████▉ | 172M/217M [08:12<01:20, 564kiB/s]
79%|███████▉ | 172M/217M [08:12<01:17, 584kiB/s]
79%|███████▉ | 172M/217M [08:12<01:14, 611kiB/s]
79%|███████▉ | 172M/217M [08:12<01:20, 566kiB/s]
79%|███████▉ | 172M/217M [08:12<01:14, 610kiB/s]
79%|███████▉ | 172M/217M [08:13<01:16, 593kiB/s]
79%|███████▉ | 172M/217M [08:13<01:17, 580kiB/s]
79%|███████▉ | 172M/217M [08:13<01:18, 575kiB/s]
79%|███████▉ | 172M/217M [08:13<01:17, 582kiB/s]
79%|███████▉ | 172M/217M [08:13<01:16, 590kiB/s]
79%|███████▉ | 172M/217M [08:13<01:22, 547kiB/s]
79%|███████▉ | 172M/217M [08:13<01:20, 554kiB/s]
79%|███████▉ | 173M/217M [08:13<01:22, 540kiB/s]
79%|███████▉ | 173M/217M [08:13<01:24, 526kiB/s]
79%|███████▉ | 173M/217M [08:14<01:47, 415kiB/s]
79%|███████▉ | 173M/217M [08:14<02:10, 343kiB/s]
79%|███████▉ | 173M/217M [08:14<02:02, 364kiB/s]
80%|███████▉ | 173M/217M [08:14<02:03, 361kiB/s]
80%|███████▉ | 173M/217M [08:14<02:32, 292kiB/s]
80%|███████▉ | 173M/217M [08:14<02:39, 279kiB/s]
80%|███████▉ | 173M/217M [08:15<02:47, 265kiB/s]
80%|███████▉ | 173M/217M [08:15<02:47, 265kiB/s]
80%|███████▉ | 173M/217M [08:15<02:48, 263kiB/s]
80%|███████▉ | 173M/217M [08:15<02:43, 271kiB/s]
80%|███████▉ | 173M/217M [08:15<02:39, 277kiB/s]
80%|███████▉ | 173M/217M [08:15<02:33, 289kiB/s]
80%|███████▉ | 173M/217M [08:15<03:11, 231kiB/s]
80%|███████▉ | 173M/217M [08:16<03:02, 243kiB/s]
80%|███████▉ | 173M/217M [08:16<02:59, 246kiB/s]
80%|███████▉ | 173M/217M [08:16<03:25, 214kiB/s]
80%|███████▉ | 173M/217M [08:16<03:35, 204kiB/s]
80%|███████▉ | 173M/217M [08:16<03:20, 220kiB/s]
80%|███████▉ | 173M/217M [08:16<03:11, 230kiB/s]
80%|███████▉ | 173M/217M [08:16<02:57, 248kiB/s]
80%|███████▉ | 173M/217M [08:16<03:03, 240kiB/s]
80%|███████▉ | 173M/217M [08:17<02:58, 246kiB/s]
80%|███████▉ | 173M/217M [08:17<03:01, 241kiB/s]
80%|███████▉ | 173M/217M [08:17<02:56, 248kiB/s]
80%|███████▉ | 173M/217M [08:17<02:48, 260kiB/s]
80%|███████▉ | 173M/217M [08:17<02:43, 267kiB/s]
80%|███████▉ | 174M/217M [08:17<02:38, 276kiB/s]
80%|███████▉ | 174M/217M [08:17<02:25, 300kiB/s]
80%|███████▉ | 174M/217M [08:17<02:19, 312kiB/s]
80%|███████▉ | 174M/217M [08:18<02:09, 336kiB/s]
80%|███████▉ | 174M/217M [08:18<02:03, 354kiB/s]
80%|███████▉ | 174M/217M [08:18<01:55, 376kiB/s]
80%|███████▉ | 174M/217M [08:18<01:50, 393kiB/s]
80%|████████ | 174M/217M [08:18<01:44, 418kiB/s]
80%|████████ | 174M/217M [08:18<01:37, 445kiB/s]
80%|████████ | 174M/217M [08:18<01:33, 463kiB/s]
80%|████████ | 174M/217M [08:18<01:27, 492kiB/s]
80%|████████ | 174M/217M [08:19<01:22, 520kiB/s]
80%|████████ | 174M/217M [08:19<01:26, 500kiB/s]
80%|████████ | 174M/217M [08:19<01:26, 496kiB/s]
80%|████████ | 174M/217M [08:19<01:27, 491kiB/s]
80%|████████ | 174M/217M [08:19<01:28, 484kiB/s]
80%|████████ | 174M/217M [08:19<01:27, 490kiB/s]
80%|████████ | 174M/217M [08:19<01:25, 500kiB/s]
80%|████████ | 175M/217M [08:19<01:24, 509kiB/s]
80%|████████ | 175M/217M [08:20<01:21, 521kiB/s]
80%|████████ | 175M/217M [08:20<01:19, 533kiB/s]
80%|████████ | 175M/217M [08:20<01:17, 552kiB/s]
80%|████████ | 175M/217M [08:20<01:27, 488kiB/s]
80%|████████ | 175M/217M [08:20<01:16, 553kiB/s]
81%|████████ | 175M/217M [08:20<01:18, 543kiB/s]
81%|████████ | 175M/217M [08:20<01:17, 547kiB/s]
81%|████████ | 175M/217M [08:20<01:23, 507kiB/s]
81%|████████ | 175M/217M [08:20<01:24, 499kiB/s]
81%|████████ | 175M/217M [08:21<01:22, 508kiB/s]
81%|████████ | 175M/217M [08:21<01:20, 522kiB/s]
81%|████████ | 175M/217M [08:21<01:19, 531kiB/s]
81%|████████ | 175M/217M [08:21<01:18, 531kiB/s]
81%|████████ | 175M/217M [08:21<01:22, 508kiB/s]
81%|████████ | 175M/217M [08:21<01:27, 479kiB/s]
81%|████████ | 176M/217M [08:21<01:28, 472kiB/s]
81%|████████ | 176M/217M [08:21<01:29, 466kiB/s]
81%|████████ | 176M/217M [08:22<01:33, 445kiB/s]
81%|████████ | 176M/217M [08:22<01:33, 444kiB/s]
81%|████████ | 176M/217M [08:22<01:45, 394kiB/s]
81%|████████ | 176M/217M [08:22<01:52, 368kiB/s]
81%|████████ | 176M/217M [08:22<02:12, 314kiB/s]
81%|████████ | 176M/217M [08:22<01:59, 348kiB/s]
81%|████████ | 176M/217M [08:22<02:03, 336kiB/s]
81%|████████ | 176M/217M [08:22<02:01, 341kiB/s]
81%|████████ | 176M/217M [08:23<02:03, 335kiB/s]
81%|████████ | 176M/217M [08:23<02:13, 309kiB/s]
81%|████████ | 176M/217M [08:23<02:05, 328kiB/s]
81%|████████ | 176M/217M [08:23<02:02, 335kiB/s]
81%|████████ | 176M/217M [08:23<01:56, 353kiB/s]
81%|████████ | 176M/217M [08:23<01:50, 373kiB/s]
81%|████████ | 176M/217M [08:23<01:46, 386kiB/s]
81%|████████ | 176M/217M [08:23<01:39, 411kiB/s]
81%|████████ | 176M/217M [08:24<01:35, 427kiB/s]
81%|████████ | 176M/217M [08:24<01:29, 457kiB/s]
81%|████████ | 176M/217M [08:24<01:30, 451kiB/s]
81%|████████ | 177M/217M [08:24<01:45, 386kiB/s]
81%|████████▏ | 177M/217M [08:24<01:43, 392kiB/s]
81%|████████▏ | 177M/217M [08:24<01:48, 374kiB/s]
81%|████████▏ | 177M/217M [08:24<01:50, 368kiB/s]
81%|████████▏ | 177M/217M [08:24<02:07, 318kiB/s]
81%|████████▏ | 177M/217M [08:25<02:08, 316kiB/s]
81%|████████▏ | 177M/217M [08:25<02:17, 295kiB/s]
81%|████████▏ | 177M/217M [08:25<02:11, 308kiB/s]
81%|████████▏ | 177M/217M [08:25<02:15, 300kiB/s]
81%|████████▏ | 177M/217M [08:25<02:16, 296kiB/s]
81%|████████▏ | 177M/217M [08:25<02:15, 298kiB/s]
81%|████████▏ | 177M/217M [08:25<02:34, 262kiB/s]
81%|████████▏ | 177M/217M [08:26<02:43, 247kiB/s]
81%|████████▏ | 177M/217M [08:26<02:42, 249kiB/s]
81%|████████▏ | 177M/217M [08:26<02:39, 253kiB/s]
81%|████████▏ | 177M/217M [08:26<02:32, 265kiB/s]
82%|████████▏ | 177M/217M [08:26<02:26, 274kiB/s]
82%|████████▏ | 177M/217M [08:26<02:16, 295kiB/s]
82%|████████▏ | 177M/217M [08:26<02:07, 314kiB/s]
82%|████████▏ | 177M/217M [08:26<01:58, 338kiB/s]
82%|████████▏ | 177M/217M [08:27<01:49, 365kiB/s]
82%|████████▏ | 177M/217M [08:27<01:44, 381kiB/s]
82%|████████▏ | 177M/217M [08:27<01:38, 405kiB/s]
82%|████████▏ | 177M/217M [08:27<01:33, 426kiB/s]
82%|████████▏ | 177M/217M [08:27<01:42, 388kiB/s]
82%|████████▏ | 178M/217M [08:27<01:32, 432kiB/s]
82%|████████▏ | 178M/217M [08:27<01:36, 413kiB/s]
82%|████████▏ | 178M/217M [08:27<01:35, 415kiB/s]
82%|████████▏ | 178M/217M [08:28<01:35, 416kiB/s]
82%|████████▏ | 178M/217M [08:28<01:32, 425kiB/s]
82%|████████▏ | 178M/217M [08:28<01:34, 419kiB/s]
82%|████████▏ | 178M/217M [08:28<01:40, 392kiB/s]
82%|████████▏ | 178M/217M [08:28<01:47, 366kiB/s]
82%|████████▏ | 178M/217M [08:28<01:47, 367kiB/s]
82%|████████▏ | 178M/217M [08:28<01:46, 370kiB/s]
82%|████████▏ | 178M/217M [08:28<01:42, 381kiB/s]
82%|████████▏ | 178M/217M [08:29<01:54, 341kiB/s]
82%|████████▏ | 178M/217M [08:29<01:46, 369kiB/s]
82%|████████▏ | 178M/217M [08:29<01:50, 353kiB/s]
82%|████████▏ | 178M/217M [08:29<02:00, 324kiB/s]
82%|████████▏ | 178M/217M [08:29<02:10, 299kiB/s]
82%|████████▏ | 178M/217M [08:29<02:17, 285kiB/s]
82%|████████▏ | 178M/217M [08:29<02:18, 282kiB/s]
82%|████████▏ | 178M/217M [08:29<02:20, 277kiB/s]
82%|████████▏ | 178M/217M [08:30<02:27, 263kiB/s]
82%|████████▏ | 178M/217M [08:30<02:37, 247kiB/s]
82%|████████▏ | 178M/217M [08:30<02:39, 244kiB/s]
82%|████████▏ | 178M/217M [08:30<02:36, 248kiB/s]
82%|████████▏ | 178M/217M [08:30<02:28, 261kiB/s]
82%|████████▏ | 179M/217M [08:30<02:24, 268kiB/s]
82%|████████▏ | 179M/217M [08:30<02:18, 280kiB/s]
82%|████████▏ | 179M/217M [08:30<02:11, 295kiB/s]
82%|████████▏ | 179M/217M [08:31<02:01, 319kiB/s]
82%|████████▏ | 179M/217M [08:31<02:14, 287kiB/s]
82%|████████▏ | 179M/217M [08:31<02:01, 319kiB/s]
82%|████████▏ | 179M/217M [08:31<02:04, 310kiB/s]
82%|████████▏ | 179M/217M [08:31<02:04, 309kiB/s]
82%|████████▏ | 179M/217M [08:31<02:04, 309kiB/s]
82%|████████▏ | 179M/217M [08:31<02:00, 319kiB/s]
82%|████████▏ | 179M/217M [08:31<01:57, 327kiB/s]
82%|████████▏ | 179M/217M [08:32<01:51, 343kiB/s]
82%|████████▏ | 179M/217M [08:32<01:46, 359kiB/s]
82%|████████▏ | 179M/217M [08:32<01:41, 377kiB/s]
82%|████████▏ | 179M/217M [08:32<01:35, 401kiB/s]
82%|████████▏ | 179M/217M [08:32<01:32, 412kiB/s]
82%|████████▏ | 179M/217M [08:32<01:28, 432kiB/s]
83%|████████▎ | 179M/217M [08:32<01:23, 456kiB/s]
83%|████████▎ | 179M/217M [08:32<01:22, 460kiB/s]
83%|████████▎ | 179M/217M [08:33<01:26, 436kiB/s]
83%|████████▎ | 179M/217M [08:33<01:27, 432kiB/s]
83%|████████▎ | 179M/217M [08:33<01:29, 422kiB/s]
83%|████████▎ | 180M/217M [08:33<01:31, 415kiB/s]
83%|████████▎ | 180M/217M [08:33<01:44, 361kiB/s]
83%|████████▎ | 180M/217M [08:33<01:47, 350kiB/s]
83%|████████▎ | 180M/217M [08:33<01:45, 356kiB/s]
83%|████████▎ | 180M/217M [08:33<01:43, 362kiB/s]
83%|████████▎ | 180M/217M [08:34<01:41, 371kiB/s]
83%|████████▎ | 180M/217M [08:34<01:37, 384kiB/s]
83%|████████▎ | 180M/217M [08:34<01:42, 366kiB/s]
83%|████████▎ | 180M/217M [08:34<01:35, 391kiB/s]
83%|████████▎ | 180M/217M [08:34<01:38, 378kiB/s]
83%|████████▎ | 180M/217M [08:34<01:38, 379kiB/s]
83%|████████▎ | 180M/217M [08:34<01:36, 384kiB/s]
83%|████████▎ | 180M/217M [08:34<01:35, 388kiB/s]
83%|████████▎ | 180M/217M [08:35<01:34, 395kiB/s]
83%|████████▎ | 180M/217M [08:35<01:29, 413kiB/s]
83%|████████▎ | 180M/217M [08:35<01:27, 422kiB/s]
83%|████████▎ | 180M/217M [08:35<01:30, 409kiB/s]
83%|████████▎ | 180M/217M [08:35<01:28, 418kiB/s]
83%|████████▎ | 180M/217M [08:35<01:32, 399kiB/s]
83%|████████▎ | 180M/217M [08:35<01:29, 412kiB/s]
83%|████████▎ | 181M/217M [08:35<01:28, 414kiB/s]
83%|████████▎ | 181M/217M [08:35<01:28, 414kiB/s]
83%|████████▎ | 181M/217M [08:36<01:28, 413kiB/s]
83%|████████▎ | 181M/217M [08:36<01:36, 381kiB/s]
83%|████████▎ | 181M/217M [08:36<01:36, 377kiB/s]
83%|████████▎ | 181M/217M [08:36<01:35, 381kiB/s]
83%|████████▎ | 181M/217M [08:36<01:34, 386kiB/s]
83%|████████▎ | 181M/217M [08:36<01:33, 389kiB/s]
83%|████████▎ | 181M/217M [08:36<01:33, 390kiB/s]
83%|████████▎ | 181M/217M [08:37<01:39, 366kiB/s]
83%|████████▎ | 181M/217M [08:37<01:40, 362kiB/s]
83%|████████▎ | 181M/217M [08:37<01:49, 330kiB/s]
83%|████████▎ | 181M/217M [08:37<01:49, 332kiB/s]
83%|████████▎ | 181M/217M [08:37<01:54, 316kiB/s]
83%|████████▎ | 181M/217M [08:37<01:56, 311kiB/s]
83%|████████▎ | 181M/217M [08:37<01:57, 306kiB/s]
83%|████████▎ | 181M/217M [08:37<01:52, 319kiB/s]
83%|████████▎ | 181M/217M [08:38<02:04, 289kiB/s]
83%|████████▎ | 181M/217M [08:38<01:51, 322kiB/s]
83%|████████▎ | 181M/217M [08:38<01:53, 316kiB/s]
83%|████████▎ | 181M/217M [08:38<01:53, 315kiB/s]
84%|████████▎ | 181M/217M [08:38<01:54, 314kiB/s]
84%|████████▎ | 181M/217M [08:38<01:54, 314kiB/s]
84%|████████▎ | 182M/217M [08:38<01:48, 331kiB/s]
84%|████████▎ | 182M/217M [08:39<01:44, 342kiB/s]
84%|████████▎ | 182M/217M [08:39<01:40, 354kiB/s]
84%|████████▎ | 182M/217M [08:39<01:34, 376kiB/s]
84%|████████▎ | 182M/217M [08:39<01:30, 393kiB/s]
84%|████████▎ | 182M/217M [08:39<01:25, 417kiB/s]
84%|████████▎ | 182M/217M [08:39<01:31, 389kiB/s]
84%|████████▎ | 182M/217M [08:39<01:26, 409kiB/s]
84%|████████▎ | 182M/217M [08:39<01:41, 347kiB/s]
84%|████████▎ | 182M/217M [08:40<01:38, 360kiB/s]
84%|████████▍ | 182M/217M [08:40<01:39, 353kiB/s]
84%|████████▍ | 182M/217M [08:40<01:43, 342kiB/s]
84%|████████▍ | 182M/217M [08:40<01:43, 341kiB/s]
84%|████████▍ | 182M/217M [08:40<02:04, 283kiB/s]
84%|████████▍ | 182M/217M [08:40<01:59, 293kiB/s]
84%|████████▍ | 182M/217M [08:40<02:05, 281kiB/s]
84%|████████▍ | 182M/217M [08:40<02:08, 273kiB/s]
84%|████████▍ | 182M/217M [08:41<02:07, 274kiB/s]
84%|████████▍ | 182M/217M [08:41<02:04, 280kiB/s]
84%|████████▍ | 182M/217M [08:41<01:59, 292kiB/s]
84%|████████▍ | 182M/217M [08:41<01:55, 301kiB/s]
84%|████████▍ | 182M/217M [08:41<01:48, 320kiB/s]
84%|████████▍ | 182M/217M [08:41<01:42, 340kiB/s]
84%|████████▍ | 183M/217M [08:41<01:38, 354kiB/s]
84%|████████▍ | 183M/217M [08:41<01:48, 319kiB/s]
84%|████████▍ | 183M/217M [08:42<01:32, 377kiB/s]
84%|████████▍ | 183M/217M [08:42<01:31, 379kiB/s]
84%|████████▍ | 183M/217M [08:42<01:31, 376kiB/s]
84%|████████▍ | 183M/217M [08:42<01:58, 290kiB/s]
84%|████████▍ | 183M/217M [08:42<02:02, 281kiB/s]
84%|████████▍ | 183M/217M [08:42<02:06, 272kiB/s]
84%|████████▍ | 183M/217M [08:42<02:02, 282kiB/s]
84%|████████▍ | 183M/217M [08:42<01:59, 287kiB/s]
84%|████████▍ | 183M/217M [08:43<01:50, 311kiB/s]
84%|████████▍ | 183M/217M [08:43<01:44, 328kiB/s]
84%|████████▍ | 183M/217M [08:43<01:39, 345kiB/s]
84%|████████▍ | 183M/217M [08:43<01:38, 348kiB/s]
84%|████████▍ | 183M/217M [08:43<01:41, 338kiB/s]
84%|████████▍ | 183M/217M [08:43<01:43, 330kiB/s]
84%|████████▍ | 183M/217M [08:43<01:45, 325kiB/s]
84%|████████▍ | 183M/217M [08:43<01:33, 364kiB/s]
84%|████████▍ | 183M/217M [08:44<01:38, 347kiB/s]
84%|████████▍ | 183M/217M [08:44<01:48, 315kiB/s]
84%|████████▍ | 183M/217M [08:44<01:48, 312kiB/s]
84%|████████▍ | 183M/217M [08:44<02:03, 275kiB/s]
84%|████████▍ | 183M/217M [08:44<02:25, 233kiB/s]
84%|████████▍ | 183M/217M [08:44<02:34, 219kiB/s]
84%|████████▍ | 183M/217M [08:44<02:30, 225kiB/s]
84%|████████▍ | 183M/217M [08:45<02:39, 212kiB/s]
84%|████████▍ | 184M/217M [08:45<02:42, 208kiB/s]
84%|████████▍ | 184M/217M [08:45<02:38, 212kiB/s]
84%|████████▍ | 184M/217M [08:45<02:33, 220kiB/s]
85%|████████▍ | 184M/217M [08:45<02:29, 225kiB/s]
85%|████████▍ | 184M/217M [08:45<02:19, 241kiB/s]
85%|████████▍ | 184M/217M [08:46<02:24, 232kiB/s]
85%|████████▍ | 184M/217M [08:46<02:26, 229kiB/s]
85%|████████▍ | 184M/217M [08:46<02:29, 225kiB/s]
85%|████████▍ | 184M/217M [08:46<02:45, 203kiB/s]
85%|████████▍ | 184M/217M [08:46<02:41, 207kiB/s]
85%|████████▍ | 184M/217M [08:46<02:38, 211kiB/s]
85%|████████▍ | 184M/217M [08:46<02:29, 223kiB/s]
85%|████████▍ | 184M/217M [08:47<02:23, 233kiB/s]
85%|████████▍ | 184M/217M [08:47<02:01, 274kiB/s]
85%|████████▍ | 184M/217M [08:47<02:02, 273kiB/s]
85%|████████▍ | 184M/217M [08:47<01:58, 281kiB/s]
85%|████████▍ | 184M/217M [08:47<02:05, 264kiB/s]
85%|████████▍ | 184M/217M [08:47<01:50, 301kiB/s]
85%|████████▍ | 184M/217M [08:47<01:53, 291kiB/s]
85%|████████▍ | 184M/217M [08:48<02:18, 239kiB/s]
85%|████████▍ | 184M/217M [08:48<02:03, 268kiB/s]
85%|████████▍ | 184M/217M [08:48<02:10, 253kiB/s]
85%|████████▍ | 184M/217M [08:48<02:11, 251kiB/s]
85%|████████▍ | 184M/217M [08:48<02:10, 252kiB/s]
85%|████████▍ | 184M/217M [08:48<02:16, 241kiB/s]
85%|████████▍ | 184M/217M [08:48<02:10, 252kiB/s]
85%|████████▍ | 184M/217M [08:48<02:12, 249kiB/s]
85%|████████▍ | 184M/217M [08:49<02:10, 251kiB/s]
85%|████████▍ | 184M/217M [08:49<02:08, 255kiB/s]
85%|████████▍ | 185M/217M [08:49<02:07, 258kiB/s]
85%|████████▍ | 185M/217M [08:49<02:00, 271kiB/s]
85%|████████▍ | 185M/217M [08:49<01:55, 284kiB/s]
85%|████████▍ | 185M/217M [08:49<01:47, 304kiB/s]
85%|████████▍ | 185M/217M [08:49<01:41, 323kiB/s]
85%|████████▌ | 185M/217M [08:49<01:35, 343kiB/s]
85%|████████▌ | 185M/217M [08:50<01:29, 364kiB/s]
85%|████████▌ | 185M/217M [08:50<01:23, 387kiB/s]
85%|████████▌ | 185M/217M [08:50<01:19, 408kiB/s]
85%|████████▌ | 185M/217M [08:50<01:30, 359kiB/s]
85%|████████▌ | 185M/217M [08:50<01:16, 423kiB/s]
85%|████████▌ | 185M/217M [08:50<01:18, 413kiB/s]
85%|████████▌ | 185M/217M [08:50<01:17, 413kiB/s]
85%|████████▌ | 185M/217M [08:50<01:17, 417kiB/s]
85%|████████▌ | 185M/217M [08:51<01:15, 423kiB/s]
85%|████████▌ | 185M/217M [08:51<01:13, 435kiB/s]
85%|████████▌ | 185M/217M [08:51<01:12, 439kiB/s]
85%|████████▌ | 185M/217M [08:51<01:19, 404kiB/s]
85%|████████▌ | 185M/217M [08:51<01:12, 437kiB/s]
85%|████████▌ | 185M/217M [08:51<01:16, 416kiB/s]
85%|████████▌ | 186M/217M [08:51<01:14, 427kiB/s]
85%|████████▌ | 186M/217M [08:51<01:15, 421kiB/s]
85%|████████▌ | 186M/217M [08:52<01:15, 419kiB/s]
85%|████████▌ | 186M/217M [08:52<01:12, 434kiB/s]
85%|████████▌ | 186M/217M [08:52<01:12, 437kiB/s]
85%|████████▌ | 186M/217M [08:52<01:10, 450kiB/s]
86%|████████▌ | 186M/217M [08:52<01:07, 465kiB/s]
86%|████████▌ | 186M/217M [08:52<01:04, 485kiB/s]
86%|████████▌ | 186M/217M [08:52<01:13, 424kiB/s]
86%|████████▌ | 186M/217M [08:52<01:04, 485kiB/s]
86%|████████▌ | 186M/217M [08:53<01:06, 470kiB/s]
86%|████████▌ | 186M/217M [08:53<01:08, 455kiB/s]
86%|████████▌ | 186M/217M [08:53<01:07, 460kiB/s]
86%|████████▌ | 186M/217M [08:53<01:07, 461kiB/s]
86%|████████▌ | 186M/217M [08:53<01:06, 468kiB/s]
86%|████████▌ | 186M/217M [08:53<01:05, 475kiB/s]
86%|████████▌ | 186M/217M [08:53<01:02, 492kiB/s]
86%|████████▌ | 186M/217M [08:53<01:11, 430kiB/s]
86%|████████▌ | 187M/217M [08:54<01:03, 486kiB/s]
86%|████████▌ | 187M/217M [08:54<01:06, 460kiB/s]
86%|████████▌ | 187M/217M [08:54<01:11, 428kiB/s]
86%|████████▌ | 187M/217M [08:54<01:11, 425kiB/s]
86%|████████▌ | 187M/217M [08:54<01:21, 373kiB/s]
86%|████████▌ | 187M/217M [08:54<01:25, 357kiB/s]
86%|████████▌ | 187M/217M [08:54<01:26, 352kiB/s]
86%|████████▌ | 187M/217M [08:54<01:27, 347kiB/s]
86%|████████▌ | 187M/217M [08:55<01:30, 337kiB/s]
86%|████████▌ | 187M/217M [08:55<01:31, 331kiB/s]
86%|████████▌ | 187M/217M [08:55<01:32, 326kiB/s]
86%|████████▌ | 187M/217M [08:55<01:33, 324kiB/s]
86%|████████▌ | 187M/217M [08:55<02:01, 249kiB/s]
86%|████████▌ | 187M/217M [08:55<01:39, 303kiB/s]
86%|████████▌ | 187M/217M [08:55<01:41, 297kiB/s]
86%|████████▌ | 187M/217M [08:56<01:40, 299kiB/s]
86%|████████▌ | 187M/217M [08:56<01:38, 305kiB/s]
86%|████████▌ | 187M/217M [08:56<01:33, 320kiB/s]
86%|████████▌ | 187M/217M [08:56<01:29, 334kiB/s]
86%|████████▌ | 187M/217M [08:56<01:25, 350kiB/s]
86%|████████▋ | 187M/217M [08:56<01:20, 369kiB/s]
86%|████████▋ | 187M/217M [08:56<01:16, 390kiB/s]
86%|████████▋ | 188M/217M [08:56<01:12, 413kiB/s]
86%|████████▋ | 188M/217M [08:56<01:09, 428kiB/s]
86%|████████▋ | 188M/217M [08:57<01:05, 452kiB/s]
86%|████████▋ | 188M/217M [08:57<01:02, 475kiB/s]
86%|████████▋ | 188M/217M [08:57<01:00, 492kiB/s]
86%|████████▋ | 188M/217M [08:57<00:56, 521kiB/s]
86%|████████▋ | 188M/217M [08:57<00:53, 547kiB/s]
87%|████████▋ | 188M/217M [08:57<00:51, 568kiB/s]
87%|████████▋ | 188M/217M [08:57<00:49, 586kiB/s]
87%|████████▋ | 188M/217M [08:57<00:47, 611kiB/s]
87%|████████▋ | 188M/217M [08:58<00:45, 634kiB/s]
87%|████████▋ | 188M/217M [08:58<00:44, 655kiB/s]
87%|████████▋ | 188M/217M [08:58<00:42, 679kiB/s]
87%|████████▋ | 188M/217M [08:58<00:40, 704kiB/s]
87%|████████▋ | 189M/217M [08:58<00:39, 724kiB/s]
87%|████████▋ | 189M/217M [08:58<00:38, 742kiB/s]
87%|████████▋ | 189M/217M [08:58<00:37, 760kiB/s]
87%|████████▋ | 189M/217M [08:58<00:36, 784kiB/s]
87%|████████▋ | 189M/217M [08:58<00:35, 809kiB/s]
87%|████████▋ | 189M/217M [08:59<00:33, 833kiB/s]
87%|████████▋ | 189M/217M [08:59<00:35, 803kiB/s]
87%|████████▋ | 189M/217M [08:59<00:41, 682kiB/s]
87%|████████▋ | 189M/217M [08:59<00:39, 708kiB/s]
87%|████████▋ | 189M/217M [08:59<00:47, 590kiB/s]
87%|████████▋ | 189M/217M [08:59<00:49, 563kiB/s]
87%|████████▋ | 189M/217M [08:59<00:52, 529kiB/s]
87%|████████▋ | 189M/217M [09:00<00:59, 464kiB/s]
87%|████████▋ | 190M/217M [09:00<01:01, 450kiB/s]
87%|████████▋ | 190M/217M [09:00<01:03, 437kiB/s]
87%|████████▋ | 190M/217M [09:00<01:02, 440kiB/s]
87%|████████▋ | 190M/217M [09:00<00:59, 461kiB/s]
87%|████████▋ | 190M/217M [09:00<00:59, 464kiB/s]
87%|████████▋ | 190M/217M [09:00<00:59, 459kiB/s]
87%|████████▋ | 190M/217M [09:00<01:03, 432kiB/s]
87%|████████▋ | 190M/217M [09:01<01:13, 372kiB/s]
87%|████████▋ | 190M/217M [09:01<01:15, 360kiB/s]
87%|████████▋ | 190M/217M [09:01<01:27, 311kiB/s]
87%|████████▋ | 190M/217M [09:01<01:27, 311kiB/s]
87%|████████▋ | 190M/217M [09:01<01:26, 315kiB/s]
87%|████████▋ | 190M/217M [09:01<01:22, 328kiB/s]
88%|████████▊ | 190M/217M [09:01<01:18, 348kiB/s]
88%|████████▊ | 190M/217M [09:01<01:14, 362kiB/s]
88%|████████▊ | 190M/217M [09:02<01:09, 388kiB/s]
88%|████████▊ | 190M/217M [09:02<01:06, 405kiB/s]
88%|████████▊ | 190M/217M [09:02<01:02, 433kiB/s]
88%|████████▊ | 190M/217M [09:02<00:59, 454kiB/s]
88%|████████▊ | 190M/217M [09:02<00:56, 474kiB/s]
88%|████████▊ | 191M/217M [09:02<00:53, 499kiB/s]
88%|████████▊ | 191M/217M [09:02<00:50, 529kiB/s]
88%|████████▊ | 191M/217M [09:02<00:56, 473kiB/s]
88%|████████▊ | 191M/217M [09:03<00:50, 527kiB/s]
88%|████████▊ | 191M/217M [09:03<00:50, 523kiB/s]
88%|████████▊ | 191M/217M [09:03<00:51, 518kiB/s]
88%|████████▊ | 191M/217M [09:03<00:50, 520kiB/s]
88%|████████▊ | 191M/217M [09:03<00:54, 480kiB/s]
88%|████████▊ | 191M/217M [09:03<00:53, 493kiB/s]
88%|████████▊ | 191M/217M [09:03<00:51, 508kiB/s]
88%|████████▊ | 191M/217M [09:03<00:53, 492kiB/s]
88%|████████▊ | 191M/217M [09:03<00:53, 489kiB/s]
88%|████████▊ | 191M/217M [09:04<00:56, 460kiB/s]
88%|████████▊ | 191M/217M [09:04<01:03, 412kiB/s]
88%|████████▊ | 191M/217M [09:04<01:06, 388kiB/s]
88%|████████▊ | 191M/217M [09:04<01:09, 373kiB/s]
88%|████████▊ | 191M/217M [09:04<01:15, 344kiB/s]
88%|████████▊ | 191M/217M [09:04<01:14, 347kiB/s]
88%|████████▊ | 191M/217M [09:04<01:16, 336kiB/s]
88%|████████▊ | 192M/217M [09:04<01:12, 355kiB/s]
88%|████████▊ | 192M/217M [09:05<01:09, 368kiB/s]
88%|████████▊ | 192M/217M [09:05<01:16, 333kiB/s]
88%|████████▊ | 192M/217M [09:05<01:08, 373kiB/s]
88%|████████▊ | 192M/217M [09:05<01:10, 362kiB/s]
88%|████████▊ | 192M/217M [09:05<01:10, 361kiB/s]
88%|████████▊ | 192M/217M [09:05<01:10, 363kiB/s]
88%|████████▊ | 192M/217M [09:05<01:08, 373kiB/s]
88%|████████▊ | 192M/217M [09:06<01:16, 332kiB/s]
88%|████████▊ | 192M/217M [09:06<01:06, 379kiB/s]
88%|████████▊ | 192M/217M [09:06<01:06, 381kiB/s]
88%|████████▊ | 192M/217M [09:06<01:09, 363kiB/s]
88%|████████▊ | 192M/217M [09:06<01:09, 362kiB/s]
88%|████████▊ | 192M/217M [09:06<01:08, 365kiB/s]
88%|████████▊ | 192M/217M [09:06<01:06, 377kiB/s]
88%|████████▊ | 192M/217M [09:07<01:14, 336kiB/s]
89%|████████▊ | 192M/217M [09:07<01:06, 373kiB/s]
89%|████████▊ | 192M/217M [09:07<01:08, 361kiB/s]
89%|████████▊ | 192M/217M [09:07<01:08, 362kiB/s]
89%|████████▊ | 192M/217M [09:07<01:08, 364kiB/s]
89%|████████▊ | 193M/217M [09:07<01:06, 374kiB/s]
89%|████████▊ | 193M/217M [09:07<01:05, 380kiB/s]
89%|████████▊ | 193M/217M [09:07<01:02, 392kiB/s]
89%|████████▊ | 193M/217M [09:08<01:00, 407kiB/s]
89%|████████▊ | 193M/217M [09:08<00:57, 426kiB/s]
89%|████████▊ | 193M/217M [09:08<00:55, 440kiB/s]
89%|████████▊ | 193M/217M [09:08<00:53, 461kiB/s]
89%|████████▉ | 193M/217M [09:08<00:50, 482kiB/s]
89%|████████▉ | 193M/217M [09:08<00:48, 503kiB/s]
89%|████████▉ | 193M/217M [09:08<00:45, 530kiB/s]
89%|████████▉ | 193M/217M [09:08<00:43, 550kiB/s]
89%|████████▉ | 193M/217M [09:08<00:44, 544kiB/s]
89%|████████▉ | 193M/217M [09:09<00:45, 529kiB/s]
89%|████████▉ | 193M/217M [09:09<00:53, 445kiB/s]
89%|████████▉ | 193M/217M [09:09<00:54, 443kiB/s]
89%|████████▉ | 193M/217M [09:09<00:58, 411kiB/s]
89%|████████▉ | 193M/217M [09:09<01:04, 367kiB/s]
89%|████████▉ | 193M/217M [09:09<01:12, 329kiB/s]
89%|████████▉ | 194M/217M [09:10<01:22, 288kiB/s]
89%|████████▉ | 194M/217M [09:10<01:36, 246kiB/s]
89%|████████▉ | 194M/217M [09:10<01:53, 209kiB/s]
89%|████████▉ | 194M/217M [09:10<02:02, 193kiB/s]
89%|████████▉ | 194M/217M [09:10<02:03, 192kiB/s]
89%|████████▉ | 194M/217M [09:10<02:17, 172kiB/s]
89%|████████▉ | 194M/217M [09:11<02:14, 175kiB/s]
89%|████████▉ | 194M/217M [09:11<02:07, 186kiB/s]
89%|████████▉ | 194M/217M [09:11<02:04, 188kiB/s]
89%|████████▉ | 194M/217M [09:11<02:03, 191kiB/s]
89%|████████▉ | 194M/217M [09:11<02:01, 193kiB/s]
89%|████████▉ | 194M/217M [09:11<02:16, 172kiB/s]
89%|████████▉ | 194M/217M [09:12<02:05, 186kiB/s]
89%|████████▉ | 194M/217M [09:12<02:25, 161kiB/s]
89%|████████▉ | 194M/217M [09:12<02:29, 157kiB/s]
89%|████████▉ | 194M/217M [09:12<02:34, 151kiB/s]
89%|████████▉ | 194M/217M [09:12<03:05, 126kiB/s]
89%|████████▉ | 194M/217M [09:12<02:41, 144kiB/s]
89%|████████▉ | 194M/217M [09:13<02:50, 136kiB/s]
89%|████████▉ | 194M/217M [09:13<02:45, 140kiB/s]
89%|████████▉ | 194M/217M [09:13<02:42, 143kiB/s]
89%|████████▉ | 194M/217M [09:13<02:28, 157kiB/s]
89%|████████▉ | 194M/217M [09:13<02:11, 177kiB/s]
89%|████████▉ | 194M/217M [09:13<01:57, 197kiB/s]
89%|████████▉ | 194M/217M [09:13<01:48, 214kiB/s]
89%|████████▉ | 194M/217M [09:13<01:38, 234kiB/s]
89%|████████▉ | 194M/217M [09:14<01:30, 254kiB/s]
89%|████████▉ | 194M/217M [09:14<01:25, 270kiB/s]
89%|████████▉ | 194M/217M [09:14<01:16, 302kiB/s]
89%|████████▉ | 194M/217M [09:14<01:10, 326kiB/s]
89%|████████▉ | 194M/217M [09:14<01:11, 319kiB/s]
89%|████████▉ | 194M/217M [09:14<01:12, 317kiB/s]
89%|████████▉ | 194M/217M [09:14<01:13, 312kiB/s]
90%|████████▉ | 194M/217M [09:14<01:28, 258kiB/s]
90%|████████▉ | 195M/217M [09:15<01:15, 303kiB/s]
90%|████████▉ | 195M/217M [09:15<01:20, 281kiB/s]
90%|████████▉ | 195M/217M [09:15<01:21, 277kiB/s]
90%|████████▉ | 195M/217M [09:15<01:22, 274kiB/s]
90%|████████▉ | 195M/217M [09:15<01:19, 284kiB/s]
90%|████████▉ | 195M/217M [09:15<01:17, 292kiB/s]
90%|████████▉ | 195M/217M [09:15<01:11, 314kiB/s]
90%|████████▉ | 195M/217M [09:15<01:08, 330kiB/s]
90%|████████▉ | 195M/217M [09:16<01:04, 349kiB/s]
90%|████████▉ | 195M/217M [09:16<01:00, 369kiB/s]
90%|████████▉ | 195M/217M [09:16<00:57, 389kiB/s]
90%|████████▉ | 195M/217M [09:16<00:53, 415kiB/s]
90%|████████▉ | 195M/217M [09:16<00:50, 444kiB/s]
90%|████████▉ | 195M/217M [09:16<00:47, 465kiB/s]
90%|████████▉ | 195M/217M [09:16<00:45, 484kiB/s]
90%|████████▉ | 195M/217M [09:16<00:43, 506kiB/s]
90%|████████▉ | 195M/217M [09:17<00:43, 505kiB/s]
90%|████████▉ | 195M/217M [09:17<00:43, 499kiB/s]
90%|████████▉ | 195M/217M [09:17<00:45, 485kiB/s]
90%|████████▉ | 195M/217M [09:17<00:45, 475kiB/s]
90%|████████▉ | 196M/217M [09:17<00:52, 411kiB/s]
90%|█████████ | 196M/217M [09:17<00:55, 392kiB/s]
90%|█████████ | 196M/217M [09:17<01:00, 360kiB/s]
90%|█████████ | 196M/217M [09:17<01:02, 349kiB/s]
90%|█████████ | 196M/217M [09:18<01:05, 332kiB/s]
90%|█████████ | 196M/217M [09:18<01:12, 299kiB/s]
90%|█████████ | 196M/217M [09:18<01:16, 282kiB/s]
90%|█████████ | 196M/217M [09:18<01:23, 256kiB/s]
90%|█████████ | 196M/217M [09:18<01:23, 259kiB/s]
90%|█████████ | 196M/217M [09:18<01:23, 257kiB/s]
90%|█████████ | 196M/217M [09:18<01:20, 265kiB/s]
90%|█████████ | 196M/217M [09:19<01:15, 284kiB/s]
90%|█████████ | 196M/217M [09:19<01:09, 305kiB/s]
90%|█████████ | 196M/217M [09:19<01:06, 319kiB/s]
90%|█████████ | 196M/217M [09:19<01:02, 340kiB/s]
90%|█████████ | 196M/217M [09:19<01:04, 328kiB/s]
90%|█████████ | 196M/217M [09:19<01:05, 324kiB/s]
90%|█████████ | 196M/217M [09:19<01:05, 321kiB/s]
90%|█████████ | 196M/217M [09:19<01:04, 328kiB/s]
90%|█████████ | 196M/217M [09:20<01:02, 336kiB/s]
90%|█████████ | 196M/217M [09:20<01:00, 346kiB/s]
90%|█████████ | 196M/217M [09:20<00:57, 361kiB/s]
90%|█████████ | 196M/217M [09:20<00:56, 369kiB/s]
90%|█████████ | 196M/217M [09:20<00:53, 387kiB/s]
90%|█████████ | 197M/217M [09:20<00:51, 405kiB/s]
90%|█████████ | 197M/217M [09:20<00:47, 431kiB/s]
91%|█████████ | 197M/217M [09:20<00:44, 461kiB/s]
91%|█████████ | 197M/217M [09:21<00:43, 476kiB/s]
91%|█████████ | 197M/217M [09:21<00:41, 498kiB/s]
91%|█████████ | 197M/217M [09:21<00:44, 459kiB/s]
91%|█████████ | 197M/217M [09:21<00:39, 516kiB/s]
91%|█████████ | 197M/217M [09:21<00:40, 499kiB/s]
91%|█████████ | 197M/217M [09:21<00:44, 451kiB/s]
91%|█████████ | 197M/217M [09:21<00:49, 407kiB/s]
91%|█████████ | 197M/217M [09:22<00:51, 389kiB/s]
91%|█████████ | 197M/217M [09:22<00:56, 356kiB/s]
91%|█████████ | 197M/217M [09:22<00:55, 360kiB/s]
91%|█████████ | 197M/217M [09:22<00:56, 357kiB/s]
91%|█████████ | 197M/217M [09:22<00:57, 347kiB/s]
91%|█████████ | 197M/217M [09:22<01:05, 306kiB/s]
91%|█████████ | 197M/217M [09:22<01:12, 275kiB/s]
91%|█████████ | 197M/217M [09:22<01:15, 264kiB/s]
91%|█████████ | 197M/217M [09:23<01:18, 253kiB/s]
91%|█████████ | 197M/217M [09:23<01:16, 260kiB/s]
91%|█████████ | 198M/217M [09:23<01:21, 243kiB/s]
91%|█████████ | 198M/217M [09:23<01:11, 277kiB/s]
91%|█████████ | 198M/217M [09:23<01:12, 272kiB/s]
91%|█████████ | 198M/217M [09:23<01:10, 278kiB/s]
91%|█████████ | 198M/217M [09:23<01:09, 284kiB/s]
91%|█████████ | 198M/217M [09:24<01:08, 284kiB/s]
91%|█████████ | 198M/217M [09:24<01:16, 257kiB/s]
91%|█████████ | 198M/217M [09:24<01:09, 279kiB/s]
91%|█████████ | 198M/217M [09:24<01:10, 278kiB/s]
91%|█████████ | 198M/217M [09:24<01:11, 273kiB/s]
91%|█████████ | 198M/217M [09:24<01:08, 282kiB/s]
91%|█████████ | 198M/217M [09:24<01:07, 288kiB/s]
91%|█████████ | 198M/217M [09:24<01:05, 296kiB/s]
91%|█████████ | 198M/217M [09:24<01:04, 298kiB/s]
91%|█████████ | 198M/217M [09:25<01:09, 277kiB/s]
91%|█████████ | 198M/217M [09:25<01:10, 272kiB/s]
91%|█████████ | 198M/217M [09:25<01:10, 271kiB/s]
91%|█████████ | 198M/217M [09:25<01:15, 254kiB/s]
91%|█████████ | 198M/217M [09:25<01:15, 252kiB/s]
91%|█████████ | 198M/217M [09:25<01:18, 244kiB/s]
91%|█████████ | 198M/217M [09:25<01:18, 243kiB/s]
91%|█████████ | 198M/217M [09:26<01:23, 227kiB/s]
91%|█████████▏| 198M/217M [09:26<01:24, 224kiB/s]
91%|█████████▏| 198M/217M [09:26<01:26, 219kiB/s]
91%|█████████▏| 198M/217M [09:26<01:37, 194kiB/s]
91%|█████████▏| 198M/217M [09:26<01:36, 197kiB/s]
91%|█████████▏| 198M/217M [09:26<01:39, 190kiB/s]
91%|█████████▏| 198M/217M [09:27<01:37, 193kiB/s]
91%|█████████▏| 198M/217M [09:27<01:31, 206kiB/s]
91%|█████████▏| 199M/217M [09:27<01:23, 225kiB/s]
91%|█████████▏| 199M/217M [09:27<01:17, 240kiB/s]
91%|█████████▏| 199M/217M [09:27<01:13, 255kiB/s]
91%|█████████▏| 199M/217M [09:27<01:09, 269kiB/s]
91%|█████████▏| 199M/217M [09:27<01:02, 299kiB/s]
91%|█████████▏| 199M/217M [09:27<01:01, 303kiB/s]
91%|█████████▏| 199M/217M [09:28<00:55, 332kiB/s]
91%|█████████▏| 199M/217M [09:28<00:52, 352kiB/s]
92%|█████████▏| 199M/217M [09:28<00:48, 379kiB/s]
92%|█████████▏| 199M/217M [09:28<00:46, 398kiB/s]
92%|█████████▏| 199M/217M [09:28<00:43, 419kiB/s]
92%|█████████▏| 199M/217M [09:28<00:41, 445kiB/s]
92%|█████████▏| 199M/217M [09:28<00:42, 431kiB/s]
92%|█████████▏| 199M/217M [09:28<00:45, 404kiB/s]
92%|█████████▏| 199M/217M [09:29<00:44, 409kiB/s]
92%|█████████▏| 199M/217M [09:29<00:49, 362kiB/s]
92%|█████████▏| 199M/217M [09:29<00:46, 386kiB/s]
92%|█████████▏| 199M/217M [09:29<00:47, 382kiB/s]
92%|█████████▏| 199M/217M [09:29<00:51, 351kiB/s]
92%|█████████▏| 199M/217M [09:29<00:57, 313kiB/s]
92%|█████████▏| 199M/217M [09:29<00:53, 336kiB/s]
92%|█████████▏| 199M/217M [09:30<00:53, 331kiB/s]
92%|█████████▏| 200M/217M [09:30<00:54, 327kiB/s]
92%|█████████▏| 200M/217M [09:30<00:55, 320kiB/s]
92%|█████████▏| 200M/217M [09:30<00:58, 303kiB/s]
92%|█████████▏| 200M/217M [09:30<01:07, 263kiB/s]
92%|█████████▏| 200M/217M [09:30<01:12, 243kiB/s]
92%|█████████▏| 200M/217M [09:30<01:10, 249kiB/s]
92%|█████████▏| 200M/217M [09:30<01:09, 254kiB/s]
92%|█████████▏| 200M/217M [09:31<01:06, 266kiB/s]
92%|█████████▏| 200M/217M [09:31<01:03, 276kiB/s]
92%|█████████▏| 200M/217M [09:31<00:57, 302kiB/s]
92%|█████████▏| 200M/217M [09:31<00:54, 318kiB/s]
92%|█████████▏| 200M/217M [09:31<00:54, 317kiB/s]
92%|█████████▏| 200M/217M [09:31<00:51, 333kiB/s]
92%|█████████▏| 200M/217M [09:31<00:52, 329kiB/s]
92%|█████████▏| 200M/217M [09:32<01:01, 281kiB/s]
92%|█████████▏| 200M/217M [09:32<00:55, 309kiB/s]
92%|█████████▏| 200M/217M [09:32<00:58, 293kiB/s]
92%|█████████▏| 200M/217M [09:32<01:00, 285kiB/s]
92%|█████████▏| 200M/217M [09:32<00:59, 290kiB/s]
92%|█████████▏| 200M/217M [09:32<00:59, 287kiB/s]
92%|█████████▏| 200M/217M [09:32<00:57, 297kiB/s]
92%|█████████▏| 200M/217M [09:32<00:53, 315kiB/s]
92%|█████████▏| 200M/217M [09:32<00:58, 289kiB/s]
92%|█████████▏| 200M/217M [09:33<00:53, 316kiB/s]
92%|█████████▏| 200M/217M [09:33<00:55, 305kiB/s]
92%|█████████▏| 200M/217M [09:33<00:55, 306kiB/s]
92%|█████████▏| 200M/217M [09:33<00:55, 300kiB/s]
92%|█████████▏| 201M/217M [09:33<00:55, 301kiB/s]
92%|█████████▏| 201M/217M [09:33<01:01, 273kiB/s]
92%|█████████▏| 201M/217M [09:33<01:06, 251kiB/s]
92%|█████████▏| 201M/217M [09:33<01:09, 239kiB/s]
92%|█████████▏| 201M/217M [09:34<01:07, 247kiB/s]
92%|█████████▏| 201M/217M [09:34<01:03, 262kiB/s]
92%|█████████▏| 201M/217M [09:34<00:58, 285kiB/s]
92%|█████████▏| 201M/217M [09:34<00:53, 310kiB/s]
92%|█████████▏| 201M/217M [09:34<00:50, 329kiB/s]
92%|█████████▏| 201M/217M [09:34<00:46, 352kiB/s]
92%|█████████▏| 201M/217M [09:34<00:44, 372kiB/s]
92%|█████████▏| 201M/217M [09:34<00:42, 382kiB/s]
92%|█████████▏| 201M/217M [09:35<00:42, 383kiB/s]
93%|█████████▎| 201M/217M [09:35<00:44, 363kiB/s]
93%|█████████▎| 201M/217M [09:35<00:44, 365kiB/s]
93%|█████████▎| 201M/217M [09:35<00:48, 332kiB/s]
93%|█████████▎| 201M/217M [09:35<00:46, 344kiB/s]
93%|█████████▎| 201M/217M [09:35<00:52, 308kiB/s]
93%|█████████▎| 201M/217M [09:35<01:00, 265kiB/s]
93%|█████████▎| 201M/217M [09:36<01:04, 250kiB/s]
93%|█████████▎| 201M/217M [09:36<01:09, 231kiB/s]
93%|█████████▎| 201M/217M [09:36<01:09, 230kiB/s]
93%|█████████▎| 201M/217M [09:36<01:12, 218kiB/s]
93%|█████████▎| 201M/217M [09:36<01:19, 201kiB/s]
93%|█████████▎| 201M/217M [09:36<01:28, 180kiB/s]
93%|█████████▎| 201M/217M [09:36<01:29, 177kiB/s]
93%|█████████▎| 201M/217M [09:37<01:29, 177kiB/s]
93%|█████████▎| 201M/217M [09:37<01:31, 173kiB/s]
93%|█████████▎| 201M/217M [09:37<01:37, 162kiB/s]
93%|█████████▎| 202M/217M [09:37<01:30, 175kiB/s]
93%|█████████▎| 202M/217M [09:37<01:34, 167kiB/s]
93%|█████████▎| 202M/217M [09:37<01:18, 200kiB/s]
93%|█████████▎| 202M/217M [09:38<01:16, 204kiB/s]
93%|█████████▎| 202M/217M [09:38<01:22, 189kiB/s]
93%|█████████▎| 202M/217M [09:38<01:19, 197kiB/s]
93%|█████████▎| 202M/217M [09:38<01:19, 195kiB/s]
93%|█████████▎| 202M/217M [09:38<01:20, 194kiB/s]
93%|█████████▎| 202M/217M [09:38<01:14, 209kiB/s]
93%|█████████▎| 202M/217M [09:38<01:10, 220kiB/s]
93%|█████████▎| 202M/217M [09:39<01:05, 235kiB/s]
93%|█████████▎| 202M/217M [09:39<01:07, 227kiB/s]
93%|█████████▎| 202M/217M [09:39<01:02, 244kiB/s]
93%|█████████▎| 202M/217M [09:39<01:03, 243kiB/s]
93%|█████████▎| 202M/217M [09:39<01:01, 249kiB/s]
93%|█████████▎| 202M/217M [09:39<01:00, 252kiB/s]
93%|█████████▎| 202M/217M [09:39<00:58, 261kiB/s]
93%|█████████▎| 202M/217M [09:39<00:56, 271kiB/s]
93%|█████████▎| 202M/217M [09:40<00:52, 291kiB/s]
93%|█████████▎| 202M/217M [09:40<00:49, 305kiB/s]
93%|█████████▎| 202M/217M [09:40<00:46, 323kiB/s]
93%|█████████▎| 202M/217M [09:40<00:45, 327kiB/s]
93%|█████████▎| 202M/217M [09:40<00:49, 303kiB/s]
93%|█████████▎| 202M/217M [09:40<00:48, 306kiB/s]
93%|█████████▎| 202M/217M [09:40<00:48, 310kiB/s]
93%|█████████▎| 202M/217M [09:40<00:48, 310kiB/s]
93%|█████████▎| 202M/217M [09:41<00:47, 313kiB/s]
93%|█████████▎| 202M/217M [09:41<00:52, 283kiB/s]
93%|█████████▎| 203M/217M [09:41<00:55, 268kiB/s]
93%|█████████▎| 203M/217M [09:41<00:53, 273kiB/s]
93%|█████████▎| 203M/217M [09:41<00:53, 275kiB/s]
93%|█████████▎| 203M/217M [09:41<00:52, 279kiB/s]
93%|█████████▎| 203M/217M [09:41<00:51, 287kiB/s]
93%|█████████▎| 203M/217M [09:41<00:49, 294kiB/s]
93%|█████████▎| 203M/217M [09:42<00:48, 301kiB/s]
93%|█████████▎| 203M/217M [09:42<00:47, 305kiB/s]
93%|█████████▎| 203M/217M [09:42<00:51, 284kiB/s]
93%|█████████▎| 203M/217M [09:42<00:53, 270kiB/s]
93%|█████████▎| 203M/217M [09:42<00:54, 266kiB/s]
93%|█████████▎| 203M/217M [09:42<00:52, 275kiB/s]
93%|█████████▎| 203M/217M [09:42<00:49, 288kiB/s]
93%|█████████▎| 203M/217M [09:42<00:45, 314kiB/s]
93%|█████████▎| 203M/217M [09:42<00:44, 324kiB/s]
93%|█████████▎| 203M/217M [09:43<00:43, 325kiB/s]
93%|█████████▎| 203M/217M [09:43<00:48, 296kiB/s]
93%|█████████▎| 203M/217M [09:43<00:48, 295kiB/s]
93%|█████████▎| 203M/217M [09:43<00:55, 256kiB/s]
94%|█████████▎| 203M/217M [09:43<01:00, 234kiB/s]
94%|█████████▎| 203M/217M [09:43<01:01, 228kiB/s]
94%|█████████▎| 203M/217M [09:43<00:59, 236kiB/s]
94%|█████████▎| 203M/217M [09:44<01:00, 232kiB/s]
94%|█████████▎| 203M/217M [09:44<00:58, 240kiB/s]
94%|█████████▎| 203M/217M [09:44<01:00, 230kiB/s]
94%|█████████▎| 203M/217M [09:44<00:58, 240kiB/s]
94%|█████████▎| 203M/217M [09:44<00:57, 243kiB/s]
94%|█████████▎| 203M/217M [09:44<00:54, 254kiB/s]
94%|█████████▎| 203M/217M [09:44<00:51, 270kiB/s]
94%|█████████▎| 203M/217M [09:44<00:53, 260kiB/s]
94%|█████████▎| 204M/217M [09:45<00:50, 270kiB/s]
94%|█████████▎| 204M/217M [09:45<00:50, 272kiB/s]
94%|█████████▎| 204M/217M [09:45<00:53, 256kiB/s]
94%|█████████▎| 204M/217M [09:45<00:55, 246kiB/s]
94%|█████████▎| 204M/217M [09:45<01:03, 215kiB/s]
94%|█████████▎| 204M/217M [09:45<01:03, 215kiB/s]
94%|█████████▎| 204M/217M [09:45<01:01, 222kiB/s]
94%|█████████▍| 204M/217M [09:46<00:56, 239kiB/s]
94%|█████████▍| 204M/217M [09:46<00:53, 255kiB/s]
94%|█████████▍| 204M/217M [09:46<00:50, 268kiB/s]
94%|█████████▍| 204M/217M [09:46<00:45, 295kiB/s]
94%|█████████▍| 204M/217M [09:46<00:45, 297kiB/s]
94%|█████████▍| 204M/217M [09:46<00:46, 288kiB/s]
94%|█████████▍| 204M/217M [09:46<00:47, 279kiB/s]
94%|█████████▍| 204M/217M [09:46<00:46, 287kiB/s]
94%|█████████▍| 204M/217M [09:47<00:52, 254kiB/s]
94%|█████████▍| 204M/217M [09:47<00:49, 267kiB/s]
94%|█████████▍| 204M/217M [09:47<00:51, 257kiB/s]
94%|█████████▍| 204M/217M [09:47<00:52, 252kiB/s]
94%|█████████▍| 204M/217M [09:47<00:52, 251kiB/s]
94%|█████████▍| 204M/217M [09:47<00:49, 266kiB/s]
94%|█████████▍| 204M/217M [09:47<00:46, 281kiB/s]
94%|█████████▍| 204M/217M [09:47<00:43, 300kiB/s]
94%|█████████▍| 204M/217M [09:48<00:41, 314kiB/s]
94%|█████████▍| 204M/217M [09:48<00:45, 283kiB/s]
94%|█████████▍| 204M/217M [09:48<00:38, 331kiB/s]
94%|█████████▍| 204M/217M [09:48<00:42, 303kiB/s]
94%|█████████▍| 204M/217M [09:48<00:43, 295kiB/s]
94%|█████████▍| 205M/217M [09:48<00:46, 273kiB/s]
94%|█████████▍| 205M/217M [09:48<00:49, 257kiB/s]
94%|█████████▍| 205M/217M [09:49<00:50, 253kiB/s]
94%|█████████▍| 205M/217M [09:49<00:51, 248kiB/s]
94%|█████████▍| 205M/217M [09:49<00:53, 235kiB/s]
94%|█████████▍| 205M/217M [09:49<00:52, 239kiB/s]
94%|█████████▍| 205M/217M [09:49<00:53, 234kiB/s]
94%|█████████▍| 205M/217M [09:49<00:51, 246kiB/s]
94%|█████████▍| 205M/217M [09:49<00:52, 239kiB/s]
94%|█████████▍| 205M/217M [09:50<00:51, 241kiB/s]
94%|█████████▍| 205M/217M [09:50<01:01, 203kiB/s]
94%|█████████▍| 205M/217M [09:50<00:52, 234kiB/s]
94%|█████████▍| 205M/217M [09:50<00:59, 208kiB/s]
94%|█████████▍| 205M/217M [09:50<01:08, 181kiB/s]
94%|█████████▍| 205M/217M [09:50<01:07, 182kiB/s]
94%|█████████▍| 205M/217M [09:50<01:07, 182kiB/s]
94%|█████████▍| 205M/217M [09:51<01:13, 168kiB/s]
94%|█████████▍| 205M/217M [09:51<01:16, 161kiB/s]
94%|█████████▍| 205M/217M [09:51<01:09, 176kiB/s]
94%|█████████▍| 205M/217M [09:51<01:06, 185kiB/s]
94%|█████████▍| 205M/217M [09:51<01:01, 198kiB/s]
94%|█████████▍| 205M/217M [09:51<00:56, 215kiB/s]
94%|█████████▍| 205M/217M [09:51<01:00, 202kiB/s]
94%|█████████▍| 205M/217M [09:52<00:55, 216kiB/s]
94%|█████████▍| 205M/217M [09:52<00:54, 219kiB/s]
94%|█████████▍| 205M/217M [09:52<00:53, 225kiB/s]
94%|█████████▍| 205M/217M [09:52<00:50, 236kiB/s]
95%|█████████▍| 205M/217M [09:52<00:49, 242kiB/s]
95%|█████████▍| 205M/217M [09:52<00:46, 258kiB/s]
95%|█████████▍| 205M/217M [09:52<00:43, 274kiB/s]
95%|█████████▍| 205M/217M [09:52<00:41, 286kiB/s]
95%|█████████▍| 205M/217M [09:53<00:45, 261kiB/s]
95%|█████████▍| 206M/217M [09:53<00:44, 262kiB/s]
95%|█████████▍| 206M/217M [09:53<00:43, 272kiB/s]
95%|█████████▍| 206M/217M [09:53<00:42, 276kiB/s]
95%|█████████▍| 206M/217M [09:53<00:45, 258kiB/s]
95%|█████████▍| 206M/217M [09:53<00:50, 232kiB/s]
95%|█████████▍| 206M/217M [09:53<00:50, 228kiB/s]
95%|█████████▍| 206M/217M [09:54<00:52, 219kiB/s]
95%|█████████▍| 206M/217M [09:54<00:55, 207kiB/s]
95%|█████████▍| 206M/217M [09:54<01:27, 133kiB/s]
95%|█████████▍| 206M/217M [09:54<01:28, 131kiB/s]
95%|█████████▍| 206M/217M [09:54<01:29, 129kiB/s]
95%|█████████▍| 206M/217M [09:54<01:29, 128kiB/s]
95%|█████████▍| 206M/217M [09:55<01:21, 141kiB/s]
95%|█████████▍| 206M/217M [09:55<01:12, 158kiB/s]
95%|█████████▍| 206M/217M [09:55<01:05, 175kiB/s]
95%|█████████▍| 206M/217M [09:55<01:06, 171kiB/s]
95%|█████████▍| 206M/217M [09:55<00:59, 190kiB/s]
95%|█████████▍| 206M/217M [09:55<00:58, 194kiB/s]
95%|█████████▍| 206M/217M [09:55<00:56, 199kiB/s]
95%|█████████▍| 206M/217M [09:56<00:53, 209kiB/s]
95%|█████████▍| 206M/217M [09:56<00:50, 224kiB/s]
95%|█████████▍| 206M/217M [09:56<00:45, 244kiB/s]
95%|█████████▍| 206M/217M [09:56<00:42, 264kiB/s]
95%|█████████▍| 206M/217M [09:56<00:38, 291kiB/s]
95%|█████████▍| 206M/217M [09:56<00:40, 271kiB/s]
95%|█████████▍| 206M/217M [09:56<00:36, 302kiB/s]
95%|█████████▍| 206M/217M [09:56<00:36, 301kiB/s]
95%|█████████▍| 206M/217M [09:57<00:36, 298kiB/s]
95%|█████████▍| 206M/217M [09:57<00:36, 300kiB/s]
95%|█████████▍| 206M/217M [09:57<00:34, 312kiB/s]
95%|█████████▌| 206M/217M [09:57<00:33, 328kiB/s]
95%|█████████▌| 207M/217M [09:57<00:34, 316kiB/s]
95%|█████████▌| 207M/217M [09:57<00:33, 321kiB/s]
95%|█████████▌| 207M/217M [09:57<00:34, 306kiB/s]
95%|█████████▌| 207M/217M [09:58<00:34, 310kiB/s]
95%|█████████▌| 207M/217M [09:58<00:34, 312kiB/s]
95%|█████████▌| 207M/217M [09:58<00:33, 314kiB/s]
95%|█████████▌| 207M/217M [09:58<00:32, 321kiB/s]
95%|█████████▌| 207M/217M [09:58<00:31, 332kiB/s]
95%|█████████▌| 207M/217M [09:58<00:29, 360kiB/s]
95%|█████████▌| 207M/217M [09:58<00:31, 335kiB/s]
95%|█████████▌| 207M/217M [09:58<00:33, 313kiB/s]
95%|█████████▌| 207M/217M [09:59<00:31, 324kiB/s]
95%|█████████▌| 207M/217M [09:59<00:34, 294kiB/s]
95%|█████████▌| 207M/217M [09:59<00:39, 260kiB/s]
95%|█████████▌| 207M/217M [09:59<00:45, 225kiB/s]
95%|█████████▌| 207M/217M [09:59<00:47, 213kiB/s]
95%|█████████▌| 207M/217M [09:59<00:49, 205kiB/s]
95%|█████████▌| 207M/217M [10:00<01:01, 165kiB/s]
95%|█████████▌| 207M/217M [10:00<00:59, 170kiB/s]
95%|█████████▌| 207M/217M [10:00<00:54, 184kiB/s]
95%|█████████▌| 207M/217M [10:00<00:50, 201kiB/s]
95%|█████████▌| 207M/217M [10:00<00:46, 218kiB/s]
95%|█████████▌| 207M/217M [10:00<00:46, 213kiB/s]
95%|█████████▌| 207M/217M [10:00<00:43, 231kiB/s]
95%|█████████▌| 207M/217M [10:01<00:41, 241kiB/s]
95%|█████████▌| 207M/217M [10:01<00:41, 241kiB/s]
95%|█████████▌| 207M/217M [10:01<00:39, 248kiB/s]
95%|█████████▌| 207M/217M [10:01<00:38, 254kiB/s]
95%|█████████▌| 208M/217M [10:01<00:36, 268kiB/s]
96%|█████████▌| 208M/217M [10:01<00:35, 276kiB/s]
96%|█████████▌| 208M/217M [10:01<00:31, 303kiB/s]
96%|█████████▌| 208M/217M [10:01<00:29, 325kiB/s]
96%|█████████▌| 208M/217M [10:02<00:27, 344kiB/s]
96%|█████████▌| 208M/217M [10:02<00:26, 366kiB/s]
96%|█████████▌| 208M/217M [10:02<00:24, 385kiB/s]
96%|█████████▌| 208M/217M [10:02<00:23, 408kiB/s]
96%|█████████▌| 208M/217M [10:02<00:22, 426kiB/s]
96%|█████████▌| 208M/217M [10:02<00:20, 459kiB/s]
96%|█████████▌| 208M/217M [10:02<00:19, 466kiB/s]
96%|█████████▌| 208M/217M [10:02<00:21, 437kiB/s]
96%|█████████▌| 208M/217M [10:03<00:22, 401kiB/s]
96%|█████████▌| 208M/217M [10:03<00:23, 383kiB/s]
96%|█████████▌| 208M/217M [10:03<00:24, 371kiB/s]
96%|█████████▌| 208M/217M [10:03<00:27, 331kiB/s]
96%|█████████▌| 208M/217M [10:03<00:27, 327kiB/s]
96%|█████████▌| 208M/217M [10:03<00:26, 339kiB/s]
96%|█████████▌| 208M/217M [10:03<00:27, 330kiB/s]
96%|█████████▌| 208M/217M [10:03<00:27, 327kiB/s]
96%|█████████▌| 208M/217M [10:04<00:28, 316kiB/s]
96%|█████████▌| 208M/217M [10:04<00:29, 305kiB/s]
96%|█████████▌| 208M/217M [10:04<00:32, 273kiB/s]
96%|█████████▌| 209M/217M [10:04<00:34, 257kiB/s]
96%|█████████▌| 209M/217M [10:04<00:34, 254kiB/s]
96%|█████████▌| 209M/217M [10:04<00:33, 257kiB/s]
96%|█████████▌| 209M/217M [10:04<00:32, 269kiB/s]
96%|█████████▌| 209M/217M [10:04<00:30, 281kiB/s]
96%|█████████▌| 209M/217M [10:05<00:35, 242kiB/s]
96%|█████████▌| 209M/217M [10:05<00:34, 250kiB/s]
96%|█████████▌| 209M/217M [10:05<00:33, 256kiB/s]
96%|█████████▌| 209M/217M [10:05<00:33, 256kiB/s]
96%|█████████▌| 209M/217M [10:05<00:38, 223kiB/s]
96%|█████████▌| 209M/217M [10:05<00:40, 210kiB/s]
96%|█████████▌| 209M/217M [10:05<00:37, 222kiB/s]
96%|█████████▌| 209M/217M [10:06<00:35, 235kiB/s]
96%|█████████▌| 209M/217M [10:06<00:33, 247kiB/s]
96%|█████████▌| 209M/217M [10:06<00:30, 271kiB/s]
96%|█████████▌| 209M/217M [10:06<00:31, 264kiB/s]
96%|█████████▌| 209M/217M [10:06<00:29, 275kiB/s]
96%|█████████▌| 209M/217M [10:06<00:30, 271kiB/s]
96%|█████████▌| 209M/217M [10:06<00:30, 266kiB/s]
96%|█████████▋| 209M/217M [10:07<00:32, 251kiB/s]
96%|█████████▋| 209M/217M [10:07<00:33, 244kiB/s]
96%|█████████▋| 209M/217M [10:07<00:33, 241kiB/s]
96%|█████████▋| 209M/217M [10:07<00:33, 238kiB/s]
96%|█████████▋| 209M/217M [10:07<00:32, 245kiB/s]
96%|█████████▋| 209M/217M [10:07<00:30, 257kiB/s]
96%|█████████▋| 209M/217M [10:07<00:29, 265kiB/s]
96%|█████████▋| 209M/217M [10:07<00:27, 288kiB/s]
96%|█████████▋| 209M/217M [10:08<00:25, 311kiB/s]
96%|█████████▋| 210M/217M [10:08<00:23, 331kiB/s]
96%|█████████▋| 210M/217M [10:08<00:22, 349kiB/s]
96%|█████████▋| 210M/217M [10:08<00:22, 340kiB/s]
96%|█████████▋| 210M/217M [10:08<00:24, 313kiB/s]
96%|█████████▋| 210M/217M [10:08<00:27, 274kiB/s]
96%|█████████▋| 210M/217M [10:09<00:39, 191kiB/s]
97%|█████████▋| 210M/217M [10:09<00:40, 188kiB/s]
97%|█████████▋| 210M/217M [10:09<00:42, 178kiB/s]
97%|█████████▋| 210M/217M [10:09<00:43, 174kiB/s]
97%|█████████▋| 210M/217M [10:09<00:52, 142kiB/s]
97%|█████████▋| 210M/217M [10:09<00:51, 145kiB/s]
97%|█████████▋| 210M/217M [10:09<01:01, 122kiB/s]
97%|█████████▋| 210M/217M [10:10<00:58, 127kiB/s]
97%|█████████▋| 210M/217M [10:10<01:01, 122kiB/s]
97%|█████████▋| 210M/217M [10:10<01:15, 99.1kiB/s]
97%|█████████▋| 210M/217M [10:10<01:04, 115kiB/s]
97%|█████████▋| 210M/217M [10:10<01:06, 112kiB/s]
97%|█████████▋| 210M/217M [10:10<01:05, 113kiB/s]
97%|█████████▋| 210M/217M [10:11<01:00, 122kiB/s]
97%|█████████▋| 210M/217M [10:11<00:51, 141kiB/s]
97%|█████████▋| 210M/217M [10:11<00:44, 163kiB/s]
97%|█████████▋| 210M/217M [10:11<00:48, 150kiB/s]
97%|█████████▋| 210M/217M [10:11<00:44, 164kiB/s]
97%|█████████▋| 210M/217M [10:11<00:44, 161kiB/s]
97%|█████████▋| 210M/217M [10:12<00:43, 166kiB/s]
97%|█████████▋| 210M/217M [10:12<00:39, 182kiB/s]
97%|█████████▋| 210M/217M [10:12<00:36, 195kiB/s]
97%|█████████▋| 210M/217M [10:12<00:36, 193kiB/s]
97%|█████████▋| 210M/217M [10:12<00:36, 193kiB/s]
97%|█████████▋| 210M/217M [10:12<00:34, 202kiB/s]
97%|█████████▋| 210M/217M [10:12<00:33, 211kiB/s]
97%|█████████▋| 210M/217M [10:13<00:31, 225kiB/s]
97%|█████████▋| 210M/217M [10:13<00:28, 243kiB/s]
97%|█████████▋| 210M/217M [10:13<00:29, 233kiB/s]
97%|█████████▋| 210M/217M [10:13<00:29, 234kiB/s]
97%|█████████▋| 210M/217M [10:13<00:29, 234kiB/s]
97%|█████████▋| 210M/217M [10:13<00:27, 248kiB/s]
97%|█████████▋| 211M/217M [10:13<00:30, 225kiB/s]
97%|█████████▋| 211M/217M [10:14<00:29, 230kiB/s]
97%|█████████▋| 211M/217M [10:14<00:29, 224kiB/s]
97%|█████████▋| 211M/217M [10:14<00:31, 212kiB/s]
97%|█████████▋| 211M/217M [10:14<00:33, 198kiB/s]
97%|█████████▋| 211M/217M [10:14<00:33, 197kiB/s]
97%|█████████▋| 211M/217M [10:14<00:32, 202kiB/s]
97%|█████████▋| 211M/217M [10:14<00:31, 207kiB/s]
97%|█████████▋| 211M/217M [10:15<00:28, 226kiB/s]
97%|█████████▋| 211M/217M [10:15<00:27, 237kiB/s]
97%|█████████▋| 211M/217M [10:15<00:25, 255kiB/s]
97%|█████████▋| 211M/217M [10:15<00:23, 269kiB/s]
97%|█████████▋| 211M/217M [10:15<00:21, 295kiB/s]
97%|█████████▋| 211M/217M [10:15<00:19, 322kiB/s]
97%|█████████▋| 211M/217M [10:15<00:20, 307kiB/s]
97%|█████████▋| 211M/217M [10:15<00:18, 338kiB/s]
97%|█████████▋| 211M/217M [10:16<00:18, 334kiB/s]
97%|█████████▋| 211M/217M [10:16<00:18, 334kiB/s]
97%|█████████▋| 211M/217M [10:16<00:18, 326kiB/s]
97%|█████████▋| 211M/217M [10:16<00:18, 337kiB/s]
97%|█████████▋| 211M/217M [10:16<00:17, 346kiB/s]
97%|█████████▋| 211M/217M [10:16<00:18, 316kiB/s]
97%|█████████▋| 211M/217M [10:16<00:17, 347kiB/s]
97%|█████████▋| 211M/217M [10:16<00:17, 332kiB/s]
97%|█████████▋| 211M/217M [10:17<00:18, 323kiB/s]
97%|█████████▋| 211M/217M [10:17<00:18, 309kiB/s]
97%|█████████▋| 212M/217M [10:17<00:20, 284kiB/s]
97%|█████████▋| 212M/217M [10:17<00:20, 275kiB/s]
97%|█████████▋| 212M/217M [10:17<00:21, 272kiB/s]
97%|█████████▋| 212M/217M [10:17<00:20, 283kiB/s]
97%|█████████▋| 212M/217M [10:17<00:19, 294kiB/s]
97%|█████████▋| 212M/217M [10:18<00:21, 265kiB/s]
97%|█████████▋| 212M/217M [10:18<00:17, 312kiB/s]
97%|█████████▋| 212M/217M [10:18<00:17, 312kiB/s]
97%|█████████▋| 212M/217M [10:18<00:17, 311kiB/s]
97%|█████████▋| 212M/217M [10:18<00:20, 262kiB/s]
98%|█████████▊| 212M/217M [10:18<00:18, 285kiB/s]
98%|█████████▊| 212M/217M [10:18<00:20, 265kiB/s]
98%|█████████▊| 212M/217M [10:18<00:20, 257kiB/s]
98%|█████████▊| 212M/217M [10:19<00:21, 247kiB/s]
98%|█████████▊| 212M/217M [10:19<00:21, 242kiB/s]
98%|█████████▊| 212M/217M [10:19<00:23, 223kiB/s]
98%|█████████▊| 212M/217M [10:19<00:27, 192kiB/s]
98%|█████████▊| 212M/217M [10:19<00:27, 186kiB/s]
98%|█████████▊| 212M/217M [10:19<00:30, 171kiB/s]
98%|█████████▊| 212M/217M [10:20<00:27, 189kiB/s]
98%|█████████▊| 212M/217M [10:20<00:27, 187kiB/s]
98%|█████████▊| 212M/217M [10:20<00:26, 192kiB/s]
98%|█████████▊| 212M/217M [10:20<00:25, 198kiB/s]
98%|█████████▊| 212M/217M [10:20<00:23, 215kiB/s]
98%|█████████▊| 212M/217M [10:20<00:22, 226kiB/s]
98%|█████████▊| 212M/217M [10:20<00:19, 253kiB/s]
98%|█████████▊| 212M/217M [10:21<00:20, 237kiB/s]
98%|█████████▊| 212M/217M [10:21<00:17, 273kiB/s]
98%|█████████▊| 212M/217M [10:21<00:18, 267kiB/s]
98%|█████████▊| 213M/217M [10:21<00:17, 266kiB/s]
98%|█████████▊| 213M/217M [10:21<00:19, 238kiB/s]
98%|█████████▊| 213M/217M [10:21<00:21, 222kiB/s]
98%|█████████▊| 213M/217M [10:22<00:19, 236kiB/s]
98%|█████████▊| 213M/217M [10:22<00:19, 239kiB/s]
98%|█████████▊| 213M/217M [10:22<00:22, 203kiB/s]
98%|█████████▊| 213M/217M [10:22<00:22, 204kiB/s]
98%|█████████▊| 213M/217M [10:22<00:21, 211kiB/s]
98%|█████████▊| 213M/217M [10:22<00:20, 224kiB/s]
98%|█████████▊| 213M/217M [10:22<00:18, 238kiB/s]
98%|█████████▊| 213M/217M [10:22<00:17, 256kiB/s]
98%|█████████▊| 213M/217M [10:23<00:16, 273kiB/s]
98%|█████████▊| 213M/217M [10:23<00:14, 300kiB/s]
98%|█████████▊| 213M/217M [10:23<00:13, 323kiB/s]
98%|█████████▊| 213M/217M [10:23<00:13, 323kiB/s]
98%|█████████▊| 213M/217M [10:23<00:13, 314kiB/s]
98%|█████████▊| 213M/217M [10:23<00:13, 312kiB/s]
98%|█████████▊| 213M/217M [10:23<00:12, 321kiB/s]
98%|█████████▊| 213M/217M [10:23<00:13, 314kiB/s]
98%|█████████▊| 213M/217M [10:24<00:13, 293kiB/s]
98%|█████████▊| 213M/217M [10:24<00:14, 275kiB/s]
98%|█████████▊| 213M/217M [10:24<00:14, 272kiB/s]
98%|█████████▊| 213M/217M [10:24<00:15, 251kiB/s]
98%|█████████▊| 213M/217M [10:24<00:15, 255kiB/s]
98%|█████████▊| 213M/217M [10:24<00:15, 248kiB/s]
98%|█████████▊| 213M/217M [10:24<00:15, 253kiB/s]
98%|█████████▊| 213M/217M [10:25<00:15, 253kiB/s]
98%|█████████▊| 213M/217M [10:25<00:16, 232kiB/s]
98%|█████████▊| 213M/217M [10:25<00:17, 212kiB/s]
98%|█████████▊| 214M/217M [10:25<00:16, 227kiB/s]
98%|█████████▊| 214M/217M [10:25<00:17, 209kiB/s]
98%|█████████▊| 214M/217M [10:25<00:17, 212kiB/s]
98%|█████████▊| 214M/217M [10:25<00:16, 221kiB/s]
98%|█████████▊| 214M/217M [10:26<00:16, 226kiB/s]
98%|█████████▊| 214M/217M [10:26<00:14, 241kiB/s]
98%|█████████▊| 214M/217M [10:26<00:14, 248kiB/s]
98%|█████████▊| 214M/217M [10:26<00:13, 266kiB/s]
98%|█████████▊| 214M/217M [10:26<00:12, 288kiB/s]
98%|█████████▊| 214M/217M [10:26<00:11, 311kiB/s]
98%|█████████▊| 214M/217M [10:26<00:10, 334kiB/s]
98%|█████████▊| 214M/217M [10:26<00:09, 355kiB/s]
98%|█████████▊| 214M/217M [10:27<00:08, 384kiB/s]
99%|█████████▊| 214M/217M [10:27<00:08, 400kiB/s]
99%|█████████▊| 214M/217M [10:27<00:07, 424kiB/s]
99%|█████████▊| 214M/217M [10:27<00:06, 450kiB/s]
99%|█████████▊| 214M/217M [10:27<00:06, 475kiB/s]
99%|█████████▊| 214M/217M [10:27<00:06, 500kiB/s]
99%|█████████▊| 214M/217M [10:27<00:06, 484kiB/s]
99%|█████████▊| 214M/217M [10:27<00:06, 479kiB/s]
99%|█████████▊| 214M/217M [10:28<00:06, 465kiB/s]
99%|█████████▊| 215M/217M [10:28<00:06, 406kiB/s]
99%|█████████▊| 215M/217M [10:28<00:06, 396kiB/s]
99%|█████████▉| 215M/217M [10:28<00:06, 395kiB/s]
99%|█████████▉| 215M/217M [10:28<00:08, 312kiB/s]
99%|█████████▉| 215M/217M [10:28<00:08, 304kiB/s]
99%|█████████▉| 215M/217M [10:28<00:08, 294kiB/s]
99%|█████████▉| 215M/217M [10:29<00:09, 273kiB/s]
99%|█████████▉| 215M/217M [10:29<00:10, 252kiB/s]
99%|█████████▉| 215M/217M [10:29<00:09, 253kiB/s]
99%|█████████▉| 215M/217M [10:29<00:09, 255kiB/s]
99%|█████████▉| 215M/217M [10:29<00:10, 237kiB/s]
99%|█████████▉| 215M/217M [10:29<00:10, 222kiB/s]
99%|█████████▉| 215M/217M [10:29<00:10, 223kiB/s]
99%|█████████▉| 215M/217M [10:30<00:10, 228kiB/s]
99%|█████████▉| 215M/217M [10:30<00:09, 243kiB/s]
99%|█████████▉| 215M/217M [10:30<00:09, 243kiB/s]
99%|█████████▉| 215M/217M [10:30<00:09, 229kiB/s]
99%|█████████▉| 215M/217M [10:30<00:09, 226kiB/s]
99%|█████████▉| 215M/217M [10:30<00:09, 219kiB/s]
99%|█████████▉| 215M/217M [10:30<00:10, 200kiB/s]
99%|█████████▉| 215M/217M [10:30<00:10, 202kiB/s]
99%|█████████▉| 215M/217M [10:31<00:11, 185kiB/s]
99%|█████████▉| 215M/217M [10:31<00:12, 163kiB/s]
99%|█████████▉| 215M/217M [10:31<00:12, 164kiB/s]
99%|█████████▉| 215M/217M [10:31<00:12, 156kiB/s]
99%|█████████▉| 215M/217M [10:31<00:13, 151kiB/s]
99%|█████████▉| 215M/217M [10:32<00:14, 136kiB/s]
99%|█████████▉| 215M/217M [10:32<00:18, 108kiB/s]
99%|█████████▉| 215M/217M [10:32<00:18, 107kiB/s]
99%|█████████▉| 215M/217M [10:32<00:17, 112kiB/s]
99%|█████████▉| 215M/217M [10:32<00:16, 116kiB/s]
99%|█████████▉| 215M/217M [10:32<00:15, 125kiB/s]
99%|█████████▉| 215M/217M [10:33<00:12, 147kiB/s]
99%|█████████▉| 215M/217M [10:33<00:11, 156kiB/s]
99%|█████████▉| 215M/217M [10:33<00:10, 165kiB/s]
99%|█████████▉| 216M/217M [10:33<00:10, 166kiB/s]
99%|█████████▉| 216M/217M [10:33<00:09, 175kiB/s]
99%|█████████▉| 216M/217M [10:33<00:09, 185kiB/s]
99%|█████████▉| 216M/217M [10:34<00:08, 203kiB/s]
99%|█████████▉| 216M/217M [10:34<00:07, 224kiB/s]
99%|█████████▉| 216M/217M [10:34<00:06, 239kiB/s]
99%|█████████▉| 216M/217M [10:34<00:05, 273kiB/s]
99%|█████████▉| 216M/217M [10:34<00:05, 294kiB/s]
99%|█████████▉| 216M/217M [10:34<00:04, 300kiB/s]
99%|█████████▉| 216M/217M [10:34<00:05, 282kiB/s]
99%|█████████▉| 216M/217M [10:34<00:04, 289kiB/s]
99%|█████████▉| 216M/217M [10:35<00:04, 293kiB/s]
99%|█████████▉| 216M/217M [10:35<00:04, 302kiB/s]
99%|█████████▉| 216M/217M [10:35<00:04, 305kiB/s]
99%|█████████▉| 216M/217M [10:35<00:03, 325kiB/s]
99%|█████████▉| 216M/217M [10:35<00:03, 345kiB/s]
99%|█████████▉| 216M/217M [10:35<00:03, 329kiB/s]
99%|█████████▉| 216M/217M [10:35<00:03, 318kiB/s]
100%|█████████▉| 216M/217M [10:35<00:03, 313kiB/s]
100%|█████████▉| 216M/217M [10:35<00:03, 308kiB/s]
100%|█████████▉| 216M/217M [10:36<00:03, 289kiB/s]
100%|█████████▉| 216M/217M [10:36<00:03, 262kiB/s]
100%|█████████▉| 216M/217M [10:36<00:03, 265kiB/s]
100%|█████████▉| 216M/217M [10:36<00:04, 225kiB/s]
100%|█████████▉| 216M/217M [10:36<00:03, 227kiB/s]
100%|█████████▉| 216M/217M [10:36<00:05, 172kiB/s]
100%|█████████▉| 216M/217M [10:37<00:05, 168kiB/s]
100%|█████████▉| 216M/217M [10:37<00:04, 173kiB/s]
100%|█████████▉| 216M/217M [10:37<00:04, 165kiB/s]
100%|█████████▉| 217M/217M [10:37<00:04, 182kiB/s]
100%|█████████▉| 217M/217M [10:37<00:03, 183kiB/s]
100%|█████████▉| 217M/217M [10:37<00:03, 187kiB/s]
100%|█████████▉| 217M/217M [10:38<00:03, 201kiB/s]
100%|█████████▉| 217M/217M [10:38<00:03, 183kiB/s]
100%|█████████▉| 217M/217M [10:38<00:02, 207kiB/s]
100%|█████████▉| 217M/217M [10:38<00:02, 193kiB/s]
100%|█████████▉| 217M/217M [10:38<00:02, 195kiB/s]
100%|█████████▉| 217M/217M [10:38<00:02, 192kiB/s]
100%|█████████▉| 217M/217M [10:39<00:02, 194kiB/s]
100%|█████████▉| 217M/217M [10:39<00:02, 203kiB/s]
100%|█████████▉| 217M/217M [10:39<00:01, 213kiB/s]
100%|█████████▉| 217M/217M [10:39<00:01, 231kiB/s]
100%|█████████▉| 217M/217M [10:39<00:01, 249kiB/s]
100%|█████████▉| 217M/217M [10:39<00:01, 264kiB/s]
100%|█████████▉| 217M/217M [10:39<00:00, 291kiB/s]
100%|█████████▉| 217M/217M [10:39<00:00, 315kiB/s]
100%|█████████▉| 217M/217M [10:40<00:00, 290kiB/s]
100%|█████████▉| 217M/217M [10:40<00:00, 322kiB/s]
100%|█████████▉| 217M/217M [10:40<00:00, 302kiB/s]
100%|█████████▉| 217M/217M [10:40<00:00, 276kiB/s]
/home/ci/opt/venv/lib/python3.11/site-packages/mmengine/runner/checkpoint.py:347: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
checkpoint = torch.load(filename, map_location=map_location)
GPU Count: 1
GPU Count to be Used: 1
GPU 0 Name: Tesla T4
GPU 0 Memory: 0.43GB/15.0GB (Used/Total)
Using 16bit Automatic Mixed Precision (AMP)
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs
`Trainer(val_check_interval=1.0)` was configured so validation will run at the end of the training epoch..
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
| Name | Type | Params | Mode
-------------------------------------------------------------------------------
0 | model | MMDetAutoModelForObjectDetection | 54.2 M | train
1 | validation_metric | MeanAveragePrecision | 0 | train
-------------------------------------------------------------------------------
54.2 M Trainable params
0 Non-trainable params
54.2 M Total params
216.620 Total estimated model params size (MB)
592 Modules in train mode
0 Modules in eval mode
/home/ci/opt/venv/lib/python3.11/site-packages/mmdet/models/backbones/csp_darknet.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(enabled=False):
/home/ci/opt/venv/lib/python3.11/site-packages/torch/functional.py:534: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3595.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
/home/ci/opt/venv/lib/python3.11/site-packages/mmdet/models/task_modules/assigners/sim_ota_assigner.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(enabled=False):
Epoch 2, global step 15: 'val_map' reached 0.33114 (best 0.33114), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/tmp/fa4334e064874824b651483d2cb44438-quick_start_tutorial_temp_save/epoch=2-step=15.ckpt' as top 1
Epoch 5, global step 30: 'val_map' reached 0.34902 (best 0.34902), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/tmp/fa4334e064874824b651483d2cb44438-quick_start_tutorial_temp_save/epoch=5-step=30.ckpt' as top 1
Epoch 8, global step 45: 'val_map' reached 0.35936 (best 0.35936), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/tmp/fa4334e064874824b651483d2cb44438-quick_start_tutorial_temp_save/epoch=8-step=45.ckpt' as top 1
Epoch 11, global step 60: 'val_map' reached 0.43478 (best 0.43478), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/tmp/fa4334e064874824b651483d2cb44438-quick_start_tutorial_temp_save/epoch=11-step=60.ckpt' as top 1
Epoch 14, global step 75: 'val_map' was not in top 1
Epoch 17, global step 90: 'val_map' reached 0.44727 (best 0.44727), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/tmp/fa4334e064874824b651483d2cb44438-quick_start_tutorial_temp_save/epoch=17-step=90.ckpt' as top 1
Epoch 20, global step 105: 'val_map' was not in top 1
Epoch 23, global step 120: 'val_map' was not in top 1
Epoch 26, global step 135: 'val_map' reached 0.44859 (best 0.44859), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/tmp/fa4334e064874824b651483d2cb44438-quick_start_tutorial_temp_save/epoch=26-step=135.ckpt' as top 1
Epoch 29, global step 150: 'val_map' reached 0.45323 (best 0.45323), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/tmp/fa4334e064874824b651483d2cb44438-quick_start_tutorial_temp_save/epoch=29-step=150.ckpt' as top 1
Epoch 32, global step 165: 'val_map' was not in top 1
Epoch 35, global step 180: 'val_map' was not in top 1
Epoch 38, global step 195: 'val_map' was not in top 1
Epoch 41, global step 210: 'val_map' reached 0.45324 (best 0.45324), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/tmp/fa4334e064874824b651483d2cb44438-quick_start_tutorial_temp_save/epoch=41-step=210.ckpt' as top 1
Epoch 44, global step 225: 'val_map' reached 0.45510 (best 0.45510), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/tmp/fa4334e064874824b651483d2cb44438-quick_start_tutorial_temp_save/epoch=44-step=225.ckpt' as top 1
Epoch 47, global step 240: 'val_map' reached 0.45563 (best 0.45563), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/tmp/fa4334e064874824b651483d2cb44438-quick_start_tutorial_temp_save/epoch=47-step=240.ckpt' as top 1
`Trainer.fit` stopped: `max_epochs=50` reached.
/home/ci/autogluon/multimodal/src/autogluon/multimodal/utils/checkpoint.py:63: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
avg_state_dict = torch.load(checkpoint_paths[0], map_location=torch.device("cpu"))["state_dict"] # nosec B614
AutoMM has created your model. 🎉🎉🎉
To load the model, use the code below:
```python
from autogluon.multimodal import MultiModalPredictor
predictor = MultiModalPredictor.load("/home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/tmp/fa4334e064874824b651483d2cb44438-quick_start_tutorial_temp_save")
```
If you are not satisfied with the model, try to increase the training time,
adjust the hyperparameters (https://auto.gluon.ai/stable/tutorials/multimodal/advanced_topics/customization.html),
or post issues on GitHub (https://github.com/autogluon/autogluon/issues).
Notice that at the end of each progress bar, if the checkpoint at current stage is saved,
it prints the model’s save path.
In this example, it’s ./quick_start_tutorial_temp_save.
Print out the time and we can see that it’s fast!
print("This finetuning takes %.2f seconds." % (train_end - start))
This finetuning takes 1175.66 seconds.
Evaluation¶
To evaluate the model we just trained, run following code.
And the evaluation results are shown in command line output. The first line is mAP in COCO standard, and the second line is mAP in VOC standard (or mAP50). For more details about these metrics, see COCO’s evaluation guideline. Note that for presenting a fast finetuning we use presets “medium_quality”, you could get better result on this dataset by simply using “high_quality” or “best_quality” presets, or customize your own model and hyperparameter settings: Customization, and some other examples at Fast Fine-tune Coco or High Performance Fine-tune Coco.
predictor.evaluate(test_path)
eval_end = time.time()
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
saving file at /home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/AutogluonModels/ag-20250118_203204/object_detection_result_cache.json
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.09s).
Accumulating evaluation results...
DONE (t=0.04s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.358
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.516
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.379
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.215
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.450
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.751
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.250
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.416
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.440
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.392
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.522
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.812
Using default root folder: ./tiny_motorbike_coco/tiny_motorbike/Annotations/... Specify `model.mmdet_image.coco_root=...` in hyperparameters if you think it is wrong.
/home/ci/opt/venv/lib/python3.11/site-packages/mmdet/models/backbones/csp_darknet.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(enabled=False):
A new predictor save path is created. This is to prevent you to overwrite previous predictor saved here. You could check current save path at predictor._save_path. If you still want to use this path, set resume=True
No path specified. Models will be saved in: "AutogluonModels/ag-20250118_203204"
Print out the evaluation time:
print("The evaluation takes %.2f seconds." % (eval_end - train_end))
The evaluation takes 1.92 seconds.
We can load a new predictor with previous save_path, and we can also reset the number of GPUs to use if not all the devices are available:
# Load and reset num_gpus
new_predictor = MultiModalPredictor.load(model_path)
new_predictor.set_num_gpus(1)
Load pretrained checkpoint: /home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/tmp/fa4334e064874824b651483d2cb44438-quick_start_tutorial_temp_save/model.ckpt
/home/ci/autogluon/multimodal/src/autogluon/multimodal/learners/base.py:2117: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
state_dict = torch.load(path, map_location=torch.device("cpu"))["state_dict"] # nosec B614
Evaluating the new predictor gives us exactly the same result:
# Evaluate new predictor
new_predictor.evaluate(test_path)
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
saving file at /home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/AutogluonModels/ag-20250118_203209/object_detection_result_cache.json
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
Loading and preparing results...
DONE (t=0.01s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.09s).
Accumulating evaluation results...
DONE (t=0.04s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.358
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.516
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.379
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.215
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.450
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.751
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.250
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.416
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.440
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.392
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.522
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.812
Using default root folder: ./tiny_motorbike_coco/tiny_motorbike/Annotations/... Specify `model.mmdet_image.coco_root=...` in hyperparameters if you think it is wrong.
/home/ci/opt/venv/lib/python3.11/site-packages/mmdet/models/backbones/csp_darknet.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(enabled=False):
A new predictor save path is created. This is to prevent you to overwrite previous predictor saved here. You could check current save path at predictor._save_path. If you still want to use this path, set resume=True
No path specified. Models will be saved in: "AutogluonModels/ag-20250118_203209"
{'map': 0.3583638102025215,
'mean_average_precision': 0.3583638102025215,
'map_50': 0.5162189109732803,
'map_75': 0.37926466733124664,
'map_small': 0.21460996477647665,
'map_medium': 0.45018566230019214,
'map_large': 0.7510578004619188,
'mar_1': 0.25046276720695326,
'mar_10': 0.4161428235846841,
'mar_100': 0.4395503875968992,
'mar_small': 0.3920833333333334,
'mar_medium': 0.5222222222222223,
'mar_large': 0.8122986954565902}
For how to set the hyperparameters and finetune the model with higher performance, see AutoMM Detection - High Performance Finetune on COCO Format Dataset.
Inference¶
Now that we have gone through the model setup, finetuning, and evaluation, this section details the inference. Specifically, we layout the steps for using the model to make predictions and visualize the results.
To run inference on the entire test set, perform:
pred = predictor.predict(test_path)
print(pred)
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[<InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9453, 0.9102, 0.6841, 0.0774, 0.0299, 0.0279, 0.0229, 0.0211, 0.0196,
0.0136, 0.0120])
labels: tensor([0, 8, 7, 7, 3, 7, 3, 8, 8, 8, 7])
bboxes: tensor([[ 1.5949e+02, 1.7286e+02, 2.7762e+02, 2.4354e+02],
[ 1.9343e+02, 1.1092e+02, 2.6048e+02, 2.3400e+02],
[ 4.4893e-01, 2.3106e+02, 4.2202e+01, 3.1386e+02],
[ 1.8016e-01, 1.8493e+02, 4.2056e+01, 3.1468e+02],
[-3.4826e-02, 1.4994e+02, 4.2418e+01, 3.1686e+02],
[-4.5239e-02, 2.2318e+02, 1.1654e+01, 3.2018e+02],
[ 9.7014e-02, 2.1312e+02, 1.1957e+01, 3.2477e+02],
[ 3.3659e-01, 1.8794e+02, 1.8828e+01, 3.1518e+02],
[ 9.4490e-02, 2.2240e+02, 1.2106e+01, 3.1979e+02],
[-8.5527e-02, 1.6001e+02, 1.3818e+01, 3.1499e+02],
[ 2.0525e+00, 1.1849e+02, 4.5164e+01, 3.1901e+02]])
) at 0x7f3ee92aaa10>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.8960, 0.8560, 0.8486, 0.7993, 0.7739, 0.7178, 0.6528, 0.5854, 0.3933,
0.2573, 0.1842, 0.1476, 0.1434, 0.1252, 0.1139, 0.0759, 0.0751, 0.0743,
0.0616, 0.0556, 0.0518, 0.0463, 0.0284, 0.0231, 0.0154, 0.0151, 0.0120,
0.0114, 0.0112, 0.0106])
labels: tensor([7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 7, 8, 8, 8])
bboxes: tensor([[ 2.0863e+02, 1.3762e+02, 2.7419e+02, 2.1981e+02],
[ 1.1630e+02, 1.5525e+02, 1.8097e+02, 2.0100e+02],
[ 2.2147e+02, 1.0611e+02, 2.7228e+02, 1.8703e+02],
[ 2.7935e+02, 3.1456e+02, 2.9526e+02, 3.3268e+02],
[ 4.6394e+02, 2.7135e+02, 4.8606e+02, 3.2391e+02],
[ 3.5933e+02, 3.1732e+02, 3.7700e+02, 3.3266e+02],
[ 4.3572e+02, 2.7772e+02, 4.5021e+02, 3.2887e+02],
[ 4.2122e+02, 2.6843e+02, 4.3816e+02, 3.2214e+02],
[ 4.5423e+02, 2.7653e+02, 4.6609e+02, 3.2420e+02],
[ 2.6752e+02, 3.1721e+02, 2.8209e+02, 3.3277e+02],
[ 3.9852e+02, 2.7731e+02, 4.1867e+02, 3.0935e+02],
[ 4.8926e+02, 2.7318e+02, 4.9980e+02, 3.3067e+02],
[ 3.8125e+02, 2.7142e+02, 3.9844e+02, 2.9843e+02],
[ 2.3892e+02, 7.0352e+01, 2.4506e+02, 8.9113e+01],
[ 5.7401e-01, 1.6516e+02, 1.1389e+01, 2.1571e+02],
[ 8.9887e-01, 3.3685e+01, 4.8759e+01, 3.3078e+02],
[ 8.2498e+00, 1.7394e+02, 1.9485e+01, 2.0909e+02],
[ 4.0183e+02, 2.7243e+02, 4.2161e+02, 3.1345e+02],
[ 4.9303e+02, 2.7300e+02, 4.9994e+02, 3.2734e+02],
[ 4.0397e+02, 2.6489e+02, 4.1869e+02, 2.8503e+02],
[ 1.7020e+02, 3.2578e+02, 1.8312e+02, 3.3241e+02],
[ 4.4656e+02, 2.7641e+02, 4.5735e+02, 3.2706e+02],
[ 4.1075e+02, 2.7888e+02, 4.2597e+02, 3.1638e+02],
[-7.8756e-01, -8.0091e-01, 6.5143e+01, 3.3849e+02],
[ 4.7877e+02, 2.6404e+02, 4.9310e+02, 3.2770e+02],
[ 3.8189e+02, 1.4386e+02, 3.9818e+02, 1.7624e+02],
[ 3.8505e+01, 2.3121e+00, 5.0564e+02, 2.9825e+02],
[ 4.5629e-02, 1.3304e+02, 2.0877e+01, 2.7618e+02],
[ 3.3328e+00, 1.6729e+02, 1.6931e+01, 2.1359e+02],
[ 3.5683e+02, 3.1758e+02, 3.8497e+02, 3.3318e+02]])
) at 0x7f3ee841b850>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9282, 0.9111, 0.8633, 0.8408, 0.8369, 0.3669, 0.3191, 0.2158, 0.1727,
0.1481, 0.1069, 0.0688, 0.0546, 0.0379, 0.0350, 0.0183, 0.0174, 0.0167,
0.0154, 0.0142, 0.0125, 0.0119, 0.0111, 0.0106, 0.0101])
labels: tensor([8, 8, 3, 8, 7, 7, 7, 8, 9, 8, 7, 8, 8, 3, 3, 8, 7, 7, 7, 7, 3, 3, 8, 8,
3])
bboxes: tensor([[ 1.9805e+02, 7.8047e+01, 3.1367e+02, 3.2234e+02],
[ 3.3665e+02, 3.6290e+00, 4.3640e+02, 3.7606e+02],
[ 4.5794e+02, 1.0647e+02, 4.9909e+02, 2.3826e+02],
[ 2.8340e+02, 5.3094e+01, 3.3496e+02, 3.2776e+02],
[ 1.4237e+01, 1.1797e+02, 3.7170e+02, 3.7344e+02],
[ 1.0898e-01, 5.2452e-03, 4.7645e+02, 3.6738e+02],
[ 7.9954e-01, -1.6418e+00, 2.7811e+02, 3.4891e+02],
[ 1.1006e+00, 1.0145e+02, 2.0359e+02, 3.5246e+02],
[ 4.5175e+02, 1.8025e+01, 4.8418e+02, 5.5169e+01],
[ 1.3107e-01, 1.0285e+02, 1.0378e+02, 3.2879e+02],
[-6.9836e-01, 1.0352e+02, 2.6339e+02, 3.4257e+02],
[ 1.8642e+02, 5.4641e-01, 4.4795e+02, 3.7152e+02],
[-1.3424e+00, 2.4999e-01, 2.7869e+02, 3.4604e+02],
[ 2.1678e+02, 4.0046e+00, 4.9845e+02, 2.5947e+02],
[ 4.7124e+02, 1.0832e+02, 4.9986e+02, 1.9227e+02],
[-8.4257e-01, -6.7982e+00, 8.5315e+01, 3.5914e+02],
[ 1.9784e+02, 3.9407e+00, 5.0841e+02, 3.2125e+02],
[-2.3549e-01, 1.0322e+02, 1.0160e+02, 3.2960e+02],
[-2.6002e+00, 3.1924e+00, 1.7574e+02, 3.5306e+02],
[ 9.3662e+00, -3.8147e-02, 2.7657e+02, 2.0766e+02],
[ 2.8052e+01, 1.2898e-02, 2.7566e+02, 1.8241e+02],
[ 3.2046e+02, 2.0945e+00, 4.9673e+02, 2.1060e+02],
[-1.2353e+00, -2.7762e+00, 5.4556e+01, 3.3500e+02],
[-2.5234e+00, 1.2048e+00, 1.7254e+02, 3.4430e+02],
[ 4.7910e+02, 1.0712e+02, 4.9981e+02, 2.2999e+02]])
) at 0x7f3ee6e6ee10>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9609, 0.3206, 0.1643, 0.1229, 0.0677, 0.0414, 0.0322, 0.0316, 0.0272,
0.0270, 0.0198, 0.0196, 0.0177, 0.0166, 0.0152, 0.0140, 0.0135, 0.0120,
0.0115, 0.0111, 0.0108, 0.0108])
labels: tensor([7, 7, 7, 8, 7, 7, 8, 8, 8, 7, 7, 7, 7, 8, 7, 8, 8, 7, 8, 7, 7, 7])
bboxes: tensor([[ 1.0799e+00, 3.3202e+01, 4.9931e+02, 3.3497e+02],
[ 1.8740e+02, 1.0514e+00, 3.9307e+02, 1.2734e+02],
[-1.8904e-01, 2.5856e+00, 4.2597e+02, 1.5522e+02],
[ 3.3489e+02, -2.7676e+00, 4.9871e+02, 3.3323e+02],
[ 1.9764e+02, 2.4755e+00, 4.9572e+02, 1.6559e+02],
[-1.1692e+00, 5.8388e+01, 2.1777e+02, 1.7807e+02],
[ 3.8644e+02, 4.8455e-01, 5.0027e+02, 1.1218e+02],
[ 2.2351e+02, 5.7292e+00, 4.9759e+02, 3.3587e+02],
[-2.1287e+00, 1.5164e+00, 1.9451e+02, 1.6479e+02],
[-7.0671e-01, 5.6985e+01, 2.9680e+02, 3.3015e+02],
[ 4.7823e+02, 2.2347e+01, 4.9989e+02, 1.7894e+02],
[ 1.0020e-01, 5.7097e+01, 9.3552e+01, 1.6901e+02],
[ 3.4127e+00, 7.2258e+01, 2.1319e+02, 1.5131e+02],
[ 1.9919e+02, -2.4398e+00, 5.0316e+02, 1.7324e+02],
[-1.9259e-01, 5.6670e+01, 5.7663e+01, 1.6924e+02],
[ 4.1314e-01, 4.5480e+01, 2.2986e+02, 3.2621e+02],
[ 3.7464e+02, -5.9886e-01, 5.0036e+02, 1.7286e+02],
[ 4.7872e+02, 2.0141e+01, 5.0019e+02, 1.2819e+02],
[ 4.6208e+02, 7.3278e+00, 4.9964e+02, 3.2606e+02],
[ 2.9321e+00, 1.2378e+02, 5.0605e+02, 3.1710e+02],
[ 4.6963e+02, 5.7216e+00, 4.9990e+02, 2.7100e+02],
[ 3.1725e+02, 1.0222e+01, 4.9525e+02, 3.3020e+02]])
) at 0x7f40078d0490>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9385, 0.9028, 0.8857, 0.8838, 0.8760, 0.8687, 0.8638, 0.8145, 0.7988,
0.7939, 0.7900, 0.7837, 0.7827, 0.7817, 0.5972, 0.4429, 0.4255, 0.2386,
0.2218, 0.1663, 0.1172, 0.1146, 0.0994, 0.0679, 0.0392, 0.0355, 0.0341,
0.0329, 0.0247, 0.0218, 0.0195, 0.0187, 0.0152, 0.0142, 0.0117])
labels: tensor([7, 7, 7, 8, 7, 7, 8, 8, 8, 8, 7, 8, 8, 8, 7, 7, 8, 7, 8, 8, 7, 8, 7, 7,
7, 8, 7, 7, 7, 7, 7, 7, 4, 7, 8])
bboxes: tensor([[ 95.1110, 199.3266, 139.2640, 225.2828],
[331.7704, 188.2250, 360.4171, 209.0406],
[273.0836, 191.3272, 305.0414, 216.0947],
[111.2057, 186.1958, 133.9115, 221.2261],
[218.8869, 189.5133, 258.4569, 217.5179],
[177.2577, 190.9004, 212.3908, 220.4277],
[419.2517, 271.1425, 499.4983, 373.7794],
[284.6994, 182.0486, 300.8475, 210.9201],
[228.2201, 179.0968, 248.7330, 215.2391],
[147.4925, 186.4819, 169.4997, 218.5962],
[139.7806, 201.8128, 175.2585, 221.2341],
[291.2434, 177.8954, 308.3659, 209.6046],
[338.5934, 182.1462, 356.3286, 204.9632],
[177.4970, 177.7211, 200.6280, 214.0758],
[259.5348, 344.3858, 347.4965, 374.7549],
[280.5911, 187.7161, 315.1120, 209.7448],
[225.5203, 184.6283, 245.5734, 212.4421],
[255.0446, 316.0262, 501.2054, 376.9425],
[281.6905, 184.2923, 297.9970, 211.0202],
[185.1431, 178.8218, 202.5522, 215.3188],
[231.3303, 241.9787, 437.8103, 306.0682],
[233.8000, 179.0285, 252.5281, 213.7449],
[430.5262, 303.1158, 500.7238, 376.1811],
[259.6618, 336.2666, 497.3694, 375.4521],
[391.1815, 298.2749, 500.2247, 374.7720],
[229.8827, 183.3845, 243.5548, 209.5843],
[419.5640, 272.4094, 499.1860, 375.2469],
[248.6142, 249.9593, 498.6515, 378.1657],
[292.9993, 187.4700, 315.5945, 208.4285],
[464.0344, 299.8455, 500.0281, 376.7170],
[261.2922, 344.7435, 313.3171, 375.1784],
[260.3998, 345.2148, 421.2408, 374.7071],
[346.2735, 230.7792, 387.7109, 264.5334],
[388.1076, 327.9276, 500.1736, 374.4161],
[343.4374, 184.5252, 383.9063, 264.3029]])
) at 0x7f3ee6e59b10>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9468, 0.9258, 0.8696, 0.8540, 0.8457, 0.8330, 0.8252, 0.8125, 0.7754,
0.7612, 0.7227, 0.6284, 0.5981, 0.5596, 0.5522, 0.5425, 0.5400, 0.4678,
0.4377, 0.2830, 0.2576, 0.2452, 0.2433, 0.2069, 0.1964, 0.1963, 0.1777,
0.1582, 0.1444, 0.1217, 0.1013, 0.0853, 0.0833, 0.0598, 0.0491, 0.0444,
0.0433, 0.0431, 0.0431, 0.0356, 0.0344, 0.0343, 0.0224, 0.0208, 0.0195,
0.0165, 0.0164, 0.0164, 0.0162, 0.0154, 0.0148, 0.0140, 0.0132, 0.0118,
0.0113, 0.0111, 0.0109, 0.0105])
labels: tensor([7, 8, 8, 8, 8, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
7, 8, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 7, 8, 7, 3, 3, 8, 7,
8, 8, 3, 8, 8, 8, 7, 8, 3, 8])
bboxes: tensor([[252.9008, 136.8273, 382.6461, 311.5455],
[271.7205, 80.0086, 381.4045, 246.4005],
[435.8845, 41.6593, 449.2717, 87.8294],
[410.1651, 42.4456, 423.4287, 89.3885],
[425.3668, 44.0746, 436.3520, 88.7368],
[326.1855, 62.6358, 389.0489, 134.4801],
[391.4235, 40.3942, 405.8421, 90.5603],
[324.8606, 29.1994, 372.4051, 114.8506],
[180.2437, 69.6167, 226.7875, 116.6515],
[463.9769, 40.6230, 478.9919, 94.3384],
[277.3724, 40.4621, 292.5495, 63.5197],
[261.8165, 43.8454, 276.0741, 62.7751],
[291.4538, 41.2763, 304.6400, 62.7055],
[304.7170, 39.4350, 315.5955, 63.7650],
[383.6921, 37.9736, 395.6048, 90.3423],
[371.5536, 46.8245, 383.5245, 76.6050],
[456.4921, 43.1998, 467.7267, 90.8820],
[230.7163, 46.1030, 241.9400, 71.9515],
[215.6832, 46.3213, 230.8012, 78.9650],
[278.1380, 40.5227, 292.1745, 88.6728],
[157.4412, 41.7595, 169.5119, 61.8314],
[236.0452, 38.1713, 251.0642, 63.4651],
[262.3376, 43.8400, 276.7250, 85.7464],
[314.6213, 43.9939, 329.9100, 80.8039],
[165.3644, 36.0461, 495.9637, 315.1857],
[453.4666, 44.8437, 466.0647, 88.8472],
[ 3.4909, 46.7752, 412.5247, 325.3702],
[237.5403, 45.1541, 251.1316, 84.5300],
[315.2922, 43.0372, 331.5828, 68.8605],
[363.5735, 42.0324, 376.2703, 63.4153],
[405.3694, 45.0383, 415.7243, 92.0731],
[478.7159, 43.6131, 491.5966, 94.7687],
[459.0081, 43.6638, 468.3356, 69.2112],
[215.0722, 45.6015, 222.8185, 60.8235],
[321.5871, 29.3248, 365.9129, 65.8127],
[304.9916, 39.3440, 316.4928, 87.2128],
[224.2730, 29.8893, 492.5239, 309.0289],
[176.9397, 69.4358, 226.1854, 159.6369],
[ 1.3257, 26.7514, 355.3149, 323.8940],
[372.4684, 45.4404, 384.5628, 69.4869],
[477.7222, 41.5251, 490.2466, 67.9294],
[ 5.6064, 106.6938, 326.0342, 324.0880],
[384.8130, 43.9747, 394.8745, 82.8753],
[483.1195, 42.5475, 497.3493, 66.7116],
[ 16.0648, 52.6215, 36.7184, 65.2376],
[172.4033, 32.8547, 492.8311, 310.3635],
[320.9645, 41.9129, 339.9730, 64.4144],
[490.8883, 46.7338, 499.7367, 66.7275],
[130.2133, 27.2491, 462.7555, 312.4508],
[292.7245, 42.8300, 306.8849, 88.4177],
[315.1185, 33.9293, 495.0378, 316.9116],
[410.1078, 44.5338, 418.7984, 79.6776],
[481.6203, 41.6503, 494.9423, 66.9247],
[324.1836, 45.2961, 345.3477, 89.9584],
[176.5927, 88.4452, 219.8917, 165.8411],
[324.4941, 35.8866, 354.0215, 71.8088],
[ 6.9772, 33.4952, 300.4446, 322.6230],
[315.0451, 43.3499, 341.2049, 84.5751]])
) at 0x7f4007869c90>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.8921, 0.8550, 0.7310, 0.6924, 0.6353, 0.6279, 0.0678, 0.0650, 0.0326,
0.0304, 0.0271, 0.0230, 0.0133])
labels: tensor([8, 7, 3, 8, 7, 7, 3, 7, 8, 8, 3, 3, 7])
bboxes: tensor([[1.0368e+02, 4.4978e+01, 3.0827e+02, 4.6948e+02],
[2.3648e+00, 7.9438e+01, 3.2360e+02, 4.9087e+02],
[2.2936e+02, 2.3924e+02, 2.7131e+02, 2.7599e+02],
[9.8529e+01, 1.4062e+02, 1.3832e+02, 2.0782e+02],
[2.7003e+02, 1.7353e+02, 3.3226e+02, 4.2608e+02],
[2.6146e+02, 1.8104e+02, 3.3927e+02, 4.8029e+02],
[2.3989e-01, 1.5214e+02, 8.2366e+00, 1.6466e+02],
[7.0752e+00, 5.5161e+01, 3.4703e+02, 3.7062e+02],
[7.4041e+01, 1.3568e+02, 1.3799e+02, 3.1901e+02],
[9.2785e+01, 1.3729e+02, 1.3840e+02, 3.0099e+02],
[2.5754e+02, 2.3883e+02, 2.7636e+02, 2.5414e+02],
[1.0363e+01, 4.0658e+01, 3.4374e+02, 3.4821e+02],
[2.7210e+02, 8.8759e+01, 3.3762e+02, 3.4523e+02]])
) at 0x7f3ee5a48c10>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9390, 0.9043, 0.8770, 0.7764, 0.6641, 0.4568, 0.4185, 0.0911, 0.0858,
0.0529, 0.0468, 0.0411, 0.0309, 0.0199, 0.0128])
labels: tensor([7, 8, 8, 9, 8, 9, 9, 3, 9, 7, 9, 9, 9, 3, 9])
bboxes: tensor([[9.8267e+01, 1.4466e+02, 3.7830e+02, 3.0718e+02],
[2.1105e+02, 1.0470e+02, 2.9169e+02, 3.1671e+02],
[2.7617e+02, 1.1065e+02, 3.3047e+02, 2.6276e+02],
[2.0350e+02, 1.3048e+02, 2.1642e+02, 1.5085e+02],
[3.1125e+02, 1.2967e+02, 3.3172e+02, 1.8014e+02],
[1.8105e+02, 1.2695e+02, 1.9864e+02, 1.4618e+02],
[2.0420e+02, 1.1298e+02, 2.1689e+02, 1.5137e+02],
[6.8150e+01, 2.0840e+02, 1.2462e+02, 2.3017e+02],
[1.8374e+02, 1.3160e+02, 2.0181e+02, 1.4816e+02],
[8.8566e+01, 5.8522e+00, 5.0323e+02, 3.2405e+02],
[2.0226e+02, 1.0399e+02, 2.1844e+02, 1.5177e+02],
[1.8929e+02, 1.3421e+02, 2.0271e+02, 1.4965e+02],
[2.0035e+02, 1.2548e+02, 2.1332e+02, 1.5039e+02],
[1.6054e+02, 2.7083e-01, 4.9766e+02, 3.1110e+02],
[1.8319e+02, 1.3154e+02, 1.9611e+02, 1.4588e+02]])
) at 0x7f3ee41d4450>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9087, 0.6587, 0.5493, 0.5332, 0.5166, 0.4238, 0.3997, 0.3262, 0.3044,
0.2610, 0.2544, 0.2267, 0.2267, 0.1737, 0.1281, 0.1127, 0.1022, 0.0969,
0.0965, 0.0789, 0.0684, 0.0614, 0.0338, 0.0234, 0.0181, 0.0110])
labels: tensor([7, 8, 7, 7, 7, 7, 8, 7, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 7, 7, 7,
7, 7])
bboxes: tensor([[6.9821e+01, 1.0027e+02, 4.3291e+02, 3.5129e+02],
[1.6210e+02, 3.8147e+01, 1.7208e+02, 5.3162e+01],
[1.0525e+02, 5.3472e+01, 1.1956e+02, 7.8852e+01],
[1.4444e+02, 4.9035e+01, 1.8584e+02, 8.3387e+01],
[1.1636e+02, 4.8113e+01, 1.4751e+02, 8.3625e+01],
[1.2000e+00, 2.3959e+01, 4.6833e+02, 3.6706e+02],
[1.5011e+02, 3.7938e+01, 1.6082e+02, 5.2541e+01],
[1.1593e+02, 5.0582e+01, 1.3681e+02, 8.3012e+01],
[1.3097e+02, 3.8835e+01, 1.4579e+02, 5.3938e+01],
[1.3948e+02, 5.0758e+01, 1.6071e+02, 8.5960e+01],
[5.4784e+01, 5.1130e+01, 9.5899e+01, 7.6116e+01],
[3.4188e+01, 4.5811e+01, 6.0734e+01, 7.6846e+01],
[7.2877e+01, 5.3141e+01, 9.6557e+01, 7.5473e+01],
[4.1508e+01, 4.9494e+01, 8.9156e+01, 7.6776e+01],
[9.0056e+01, 4.4005e+01, 1.1287e+02, 7.0448e+01],
[1.8556e+02, 3.9457e+01, 2.2069e+02, 9.3649e+01],
[1.8507e+02, 3.1106e+01, 2.4071e+02, 9.4773e+01],
[4.8129e+01, 5.1044e+01, 7.8922e+01, 7.6104e+01],
[3.2952e+01, 4.5540e+01, 4.7810e+01, 6.7350e+01],
[3.9677e+01, 5.1522e+01, 6.2179e+01, 7.6212e+01],
[3.3057e+01, 3.6998e+01, 4.6826e+01, 6.5444e+01],
[3.4447e+01, 4.6192e+01, 5.1002e+01, 7.0410e+01],
[4.1438e+01, 4.9431e+01, 7.1843e+01, 7.7034e+01],
[2.7180e+02, 4.0618e-01, 5.0125e+02, 9.8227e+01],
[1.3331e+02, 4.5638e+01, 1.5263e+02, 8.6393e+01],
[1.1657e+02, 6.3054e+01, 1.5179e+02, 8.4993e+01]])
) at 0x7f3f0f705bd0>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9355, 0.9292, 0.9224, 0.3291, 0.1145, 0.1139, 0.0505, 0.0311, 0.0279,
0.0253, 0.0244, 0.0224, 0.0195, 0.0172, 0.0149, 0.0105, 0.0100])
labels: tensor([7, 7, 8, 7, 7, 7, 3, 3, 7, 7, 2, 7, 7, 7, 3, 3, 3])
bboxes: tensor([[ 4.9048e+01, 2.5264e+01, 4.4783e+02, 3.2161e+02],
[ 3.1588e+02, 4.6516e+01, 4.8412e+02, 1.6012e+02],
[ 3.5119e+02, 5.6649e-01, 4.5585e+02, 1.3107e+02],
[ 2.2746e+01, 1.3700e+02, 5.9774e+01, 1.6827e+02],
[ 2.1707e-01, 1.1838e+02, 1.7691e+01, 2.0643e+02],
[ 1.4899e-02, 1.2599e+02, 1.2381e+01, 2.0838e+02],
[ 8.6492e-02, 1.3381e+02, 1.1248e+01, 2.0994e+02],
[-4.9552e-02, 1.4043e+02, 7.6606e+00, 2.1113e+02],
[ 4.7986e-02, 1.3644e+02, 8.5824e+00, 2.1024e+02],
[ 2.8458e-01, 1.3772e+02, 1.3509e+01, 1.8162e+02],
[ 1.1998e+00, 3.2825e+01, 1.7009e+02, 1.5907e+02],
[-3.5938e-01, 1.3756e+02, 2.4725e+01, 1.7181e+02],
[ 9.7826e-01, 6.5402e+01, 4.4090e+01, 2.0843e+02],
[ 8.8185e-02, 7.5439e+01, 1.9467e+01, 2.0620e+02],
[-2.2707e-01, 1.3841e+02, 2.4324e+01, 1.7213e+02],
[-6.0158e-01, 3.5221e+01, 1.7502e+02, 2.1712e+02],
[ 2.4664e+01, 1.4045e+02, 5.9320e+01, 1.6854e+02]])
) at 0x7f4007868510>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9512, 0.9497, 0.9429, 0.9277, 0.9141, 0.8652, 0.8564, 0.8516, 0.8091,
0.2192, 0.0117])
labels: tensor([7, 8, 8, 7, 7, 8, 7, 8, 7, 8, 8])
bboxes: tensor([[369.5950, 115.1669, 468.6862, 177.1904],
[ 86.2699, 80.9000, 178.1832, 235.4626],
[292.3818, 82.4506, 362.3058, 187.8531],
[291.6922, 129.1415, 362.9953, 204.3957],
[ 85.1895, 143.4100, 175.9433, 263.3141],
[401.8036, 73.3165, 453.6652, 162.2478],
[486.6723, 111.2668, 500.0464, 159.8175],
[182.4350, 80.1961, 287.0963, 203.1836],
[196.6247, 137.8179, 285.7972, 230.6538],
[199.1438, 80.4697, 247.7312, 132.4554],
[488.5123, 72.8782, 499.7690, 167.5652]])
) at 0x7f3ee6e59990>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9448, 0.9346, 0.8916, 0.8892, 0.6870, 0.1355, 0.1219, 0.0283, 0.0260,
0.0194, 0.0193, 0.0135, 0.0111])
labels: tensor([8, 7, 8, 8, 8, 8, 7, 3, 7, 8, 3, 7, 8])
bboxes: tensor([[ 1.7243e+02, 1.7730e+02, 2.9671e+02, 4.6684e+02],
[ 5.8155e+00, 2.0046e+02, 4.1645e+02, 4.7454e+02],
[ 4.0024e+02, 2.3898e+02, 4.2242e+02, 3.0398e+02],
[ 4.3841e+02, 2.1665e+02, 4.6628e+02, 3.0093e+02],
[ 3.3773e+02, 1.7839e+02, 3.8805e+02, 3.1731e+02],
[ 3.3694e+02, 1.7726e+02, 3.8806e+02, 2.5048e+02],
[ 3.8216e-01, -6.0158e+00, 4.4962e+02, 4.8258e+02],
[ 3.4070e-01, 5.6722e+01, 4.8634e+01, 2.1261e+02],
[ 1.4704e+02, -2.3098e+00, 4.1898e+02, 4.7497e+02],
[ 3.7034e+02, 2.0512e+02, 4.0466e+02, 3.2652e+02],
[ 2.0313e-01, 1.0729e+02, 4.6965e+01, 2.0873e+02],
[ 4.6216e+00, -1.0417e+01, 3.9264e+02, 3.5339e+02],
[ 1.8937e+02, -7.5917e+00, 3.9305e+02, 4.5681e+02]])
) at 0x7f3ee6312a50>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.8774, 0.8174, 0.7881, 0.7192, 0.0555, 0.0156])
labels: tensor([8, 8, 7, 7, 7, 8])
bboxes: tensor([[187.4893, 94.3077, 351.1826, 317.8810],
[254.4585, 56.5442, 416.2447, 288.1173],
[191.8931, 154.2749, 362.4038, 327.0023],
[274.1012, 165.7805, 426.6801, 294.4188],
[201.7774, 139.4754, 426.3476, 326.9692],
[304.9045, 107.7410, 416.9705, 268.1470]])
) at 0x7f3ee6714c10>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9351, 0.0746, 0.0447, 0.0347, 0.0301, 0.0246, 0.0213, 0.0179, 0.0160,
0.0123, 0.0121, 0.0110, 0.0104, 0.0103, 0.0102])
labels: tensor([7, 7, 7, 7, 7, 7, 7, 7, 8, 7, 7, 8, 7, 8, 7])
bboxes: tensor([[ 51.3628, 63.8355, 425.9809, 348.2739],
[372.5372, 121.6008, 500.1191, 368.2430],
[274.9606, 118.1414, 496.9145, 370.1398],
[ 56.2354, 48.8488, 345.7178, 210.9169],
[387.1140, 191.1213, 501.1673, 367.0818],
[ 65.6768, 43.2195, 273.7763, 193.6946],
[339.5045, 2.2160, 496.4330, 371.0262],
[447.2321, 98.4732, 499.6429, 369.8862],
[279.6960, -14.1533, 496.0853, 372.3564],
[150.9262, 128.5654, 237.3551, 250.9268],
[ 30.9630, 31.9545, 444.8183, 250.4673],
[ 16.3388, 76.7248, 339.9112, 372.4939],
[397.5184, 160.0190, 499.3566, 335.2935],
[465.1181, -2.9264, 499.7256, 356.6373],
[461.2032, 1.5593, 498.9531, 364.6516]])
) at 0x7f3ee79e4cd0>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9521, 0.9111, 0.3076, 0.0841, 0.0478, 0.0417, 0.0184, 0.0174, 0.0169,
0.0131, 0.0106])
labels: tensor([7, 8, 8, 8, 7, 8, 8, 8, 7, 7, 7])
bboxes: tensor([[ 1.4176e+02, 1.0473e+02, 4.6723e+02, 3.2496e+02],
[ 2.5648e+02, 4.1302e+01, 4.0407e+02, 2.6768e+02],
[ 1.7256e+02, 1.1143e+02, 2.3408e+02, 1.6396e+02],
[ 1.7252e+02, 1.1005e+02, 2.3763e+02, 2.0206e+02],
[ 3.9451e+00, -2.6713e+00, 4.9254e+02, 5.9507e+01],
[ 1.7306e+02, 1.1060e+02, 2.0546e+02, 1.5678e+02],
[ 1.7304e+02, 1.0978e+02, 2.7852e+02, 2.0545e+02],
[ 3.2438e+02, -5.6137e-02, 4.4046e+02, 2.3921e+01],
[ 3.3324e+02, -3.6887e-01, 4.7770e+02, 2.4612e+01],
[ 2.4106e+02, -6.8224e-01, 5.0464e+02, 6.2352e+01],
[ 3.2332e+02, 2.5612e-01, 4.2942e+02, 2.4280e+01]])
) at 0x7f3ee6714a90>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9575, 0.9111, 0.0158, 0.0128, 0.0109])
labels: tensor([7, 8, 7, 8, 7])
bboxes: tensor([[190.0638, 156.2086, 476.7330, 373.8695],
[279.6879, 157.0183, 407.4215, 305.4817],
[ -3.8097, 154.3061, 500.2940, 382.4127],
[369.1621, 277.8802, 479.2754, 375.2448],
[189.7010, 158.6341, 325.1427, 321.0535]])
) at 0x7f3ee7a20950>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9590, 0.9458, 0.1489, 0.0877, 0.0444, 0.0346, 0.0212, 0.0177, 0.0128])
labels: tensor([7, 8, 7, 7, 7, 7, 3, 7, 8])
bboxes: tensor([[ 1.0298e+02, 1.1576e+02, 4.3101e+02, 3.1588e+02],
[ 2.0767e+02, 7.8989e+01, 3.6265e+02, 2.8918e+02],
[ 4.7691e+02, 2.2197e+02, 4.9965e+02, 3.6671e+02],
[ 4.7026e+02, 1.1819e+02, 5.0083e+02, 3.6970e+02],
[ 3.5584e+02, -4.4198e-01, 5.0197e+02, 7.7395e+01],
[ 2.2170e+02, -1.7525e-01, 5.0173e+02, 6.6337e+01],
[ 4.7701e+02, 2.5071e+02, 4.9956e+02, 3.6726e+02],
[ 3.6216e+02, 1.0261e-02, 5.0190e+02, 4.8256e+01],
[ 3.5350e+02, -4.6781e-02, 5.0197e+02, 4.8435e+01]])
) at 0x7f3ee7b54f50>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9292, 0.1604, 0.0288, 0.0184, 0.0175, 0.0131, 0.0130, 0.0103])
labels: tensor([7, 3, 7, 3, 3, 3, 3, 3])
bboxes: tensor([[ 7.5044e+01, 2.6525e+02, 4.5113e+02, 4.6273e+02],
[ 4.6762e-01, 2.5770e+02, 3.2638e+01, 3.7337e+02],
[ 4.6972e+02, 2.5928e+00, 4.9981e+02, 9.7734e+01],
[ 4.3102e+01, 2.4062e+02, 1.8014e+02, 3.5880e+02],
[ 9.3057e+01, 2.6306e+02, 2.0733e+02, 3.5746e+02],
[-1.4960e+00, 6.0879e+01, 1.8577e+02, 4.1155e+02],
[ 9.4234e+01, 2.3261e+02, 1.8194e+02, 3.5743e+02],
[ 7.0111e-02, 2.8773e+02, 1.0501e+01, 3.7304e+02]])
) at 0x7f3ee7a21f10>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9521, 0.0839, 0.0359, 0.0267, 0.0215, 0.0212, 0.0158, 0.0145, 0.0133,
0.0117, 0.0105])
labels: tensor([7, 8, 3, 3, 7, 7, 3, 7, 3, 8, 8])
bboxes: tensor([[ 9.4630e+01, 3.8269e+01, 3.3876e+02, 4.9103e+02],
[ 1.8375e+02, 1.0650e+01, 3.3593e+02, 3.4384e+02],
[ 6.8871e-03, 3.3930e+02, 1.9775e+02, 4.8805e+02],
[ 1.7638e+02, 3.7974e+00, 3.3354e+02, 3.1437e+02],
[-4.8194e+00, -9.8881e+00, 3.5388e+02, 4.9817e+02],
[ 8.5438e+01, 1.9861e+02, 3.3390e+02, 5.0022e+02],
[ 5.7304e+00, -1.7797e+00, 3.3747e+02, 3.0940e+02],
[-7.6565e+00, -2.2549e+00, 1.7555e+02, 4.9210e+02],
[ 8.7763e-01, 3.3927e+02, 2.0410e+02, 4.3182e+02],
[-2.5258e+00, 2.0853e+01, 5.9140e+01, 4.9165e+02],
[ 1.1773e+02, -5.7279e+00, 3.3402e+02, 3.7331e+02]])
) at 0x7f3ee79e5990>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9336, 0.8164, 0.7749, 0.2371, 0.2057, 0.0704, 0.0597, 0.0572, 0.0379,
0.0267, 0.0173, 0.0146, 0.0123, 0.0115, 0.0106])
labels: tensor([7, 3, 3, 8, 8, 3, 3, 8, 3, 3, 3, 3, 3, 7, 8])
bboxes: tensor([[ 83.9244, 81.2432, 267.8230, 410.5536],
[ 4.2655, 89.7942, 33.2614, 105.1277],
[ 58.1326, 89.0264, 76.0663, 103.8447],
[154.5910, 92.3937, 160.8495, 104.8719],
[150.2587, 92.1892, 156.0075, 104.6858],
[160.9006, 89.5535, 176.2070, 97.7512],
[151.1016, 92.2227, 159.6542, 104.4570],
[151.8348, 91.2136, 159.1161, 105.2707],
[ 78.4743, 91.3791, 95.2522, 100.4178],
[204.2995, 94.0529, 218.5001, 102.5291],
[215.7458, 90.3772, 229.3065, 97.5134],
[201.5837, 92.3821, 219.6543, 101.3679],
[140.4630, 91.2711, 153.1153, 99.6469],
[203.9678, 93.6486, 218.4414, 102.2499],
[110.5402, 80.9819, 268.1446, 320.9713]])
) at 0x7f3ed0e37b50>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9385, 0.9336, 0.8774, 0.0490, 0.0265, 0.0205, 0.0164, 0.0141, 0.0131,
0.0113])
labels: tensor([8, 8, 7, 7, 8, 7, 8, 3, 2, 8])
bboxes: tensor([[ 4.2394e-01, 1.3914e+02, 7.3062e+01, 4.0226e+02],
[ 9.6425e+01, 1.7062e+02, 1.8951e+02, 3.5359e+02],
[ 8.5373e+01, 2.5419e+02, 1.9744e+02, 3.9503e+02],
[ 7.7716e+01, 2.0436e+02, 3.7697e+02, 4.4017e+02],
[-2.5264e-01, 1.3978e+02, 4.7665e+01, 2.9968e+02],
[-1.0571e+01, 6.3291e+01, 1.9631e+02, 4.0976e+02],
[ 8.0925e-02, 1.3613e+02, 6.4372e+01, 2.6387e+02],
[ 3.6438e+02, 3.6075e+02, 3.7468e+02, 3.9394e+02],
[ 1.4174e+02, 2.0681e+02, 3.7389e+02, 4.3851e+02],
[-2.5013e-01, 1.4433e+02, 4.2535e+01, 3.6895e+02]])
) at 0x7f3ee92a8190>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9512, 0.9360, 0.0446])
labels: tensor([8, 7, 7])
bboxes: tensor([[ 42.1876, 47.1714, 224.5100, 209.2739],
[ 66.1101, 176.2430, 313.3235, 450.3195],
[ 74.2302, 177.9138, 232.3256, 352.1643]])
) at 0x7f4024234cd0>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9253, 0.8945, 0.5410, 0.3525, 0.2566, 0.0337, 0.0172, 0.0127, 0.0104])
labels: tensor([7, 7, 3, 7, 3, 3, 7, 8, 7])
bboxes: tensor([[174.2735, 186.1996, 305.8047, 275.8782],
[ 57.4156, 185.5003, 141.0219, 299.6423],
[344.5625, 131.4622, 496.8437, 332.1793],
[332.0187, 32.3267, 499.2314, 354.8882],
[330.3344, 41.8920, 499.3530, 346.6912],
[367.0510, 120.3737, 499.3552, 292.0561],
[ 24.5257, 95.9487, 146.8611, 307.4897],
[ 62.0639, 177.8405, 136.8619, 277.5915],
[ 25.2252, 97.1456, 275.7514, 302.5790]])
) at 0x7f3ee4159950>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9521, 0.9380, 0.8848, 0.2323, 0.1110, 0.0806, 0.0511, 0.0490, 0.0313,
0.0271, 0.0196, 0.0195, 0.0139, 0.0133, 0.0120, 0.0119, 0.0119, 0.0106,
0.0101])
labels: tensor([7, 8, 8, 7, 8, 7, 8, 8, 7, 8, 7, 7, 8, 7, 8, 7, 7, 5, 8])
bboxes: tensor([[ 1.9429e+01, 2.8555e+01, 4.4893e+02, 2.9487e+02],
[ 4.6321e+02, 3.4348e-01, 4.9851e+02, 8.8183e+01],
[ 4.2772e+02, 3.2632e-01, 4.6291e+02, 8.0383e+01],
[ 4.3255e+02, 2.4381e+02, 5.0104e+02, 3.3425e+02],
[ 4.3865e+02, 2.4424e+02, 4.9964e+02, 3.3225e+02],
[ 4.4384e+02, 2.9956e+02, 4.9991e+02, 3.3322e+02],
[ 4.9367e+02, 1.9524e+00, 5.0008e+02, 9.1020e+01],
[ 4.5033e+02, 2.4709e+02, 4.8405e+02, 3.3175e+02],
[ 1.9632e+02, 2.9031e+01, 4.3962e+02, 2.8365e+02],
[ 4.9223e+02, 6.4210e-01, 4.9996e+02, 6.3554e+01],
[ 1.3515e+01, 1.2680e+02, 4.6539e+02, 2.9492e+02],
[ 4.4789e+02, 3.0231e+02, 4.8648e+02, 3.3282e+02],
[ 4.4799e+02, 2.9623e+02, 4.8561e+02, 3.3303e+02],
[ 2.5429e+02, 1.5627e+02, 3.5977e+02, 2.6389e+02],
[ 3.1993e+02, 2.1065e-01, 3.5507e+02, 2.5854e+01],
[ 4.6168e+02, 3.0735e+02, 5.0004e+02, 3.3286e+02],
[ 1.8844e+02, 1.5666e+02, 3.6235e+02, 2.6662e+02],
[ 1.7483e+01, 1.2708e+02, 4.5205e+02, 2.9504e+02],
[ 4.7965e+02, -1.0487e+00, 5.0004e+02, 8.6448e+01]])
) at 0x7f3ee6311250>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9556, 0.0600, 0.0183, 0.0163, 0.0145, 0.0115])
labels: tensor([7, 8, 8, 7, 8, 5])
bboxes: tensor([[ 67.3527, 24.1280, 436.1629, 348.3329],
[130.8438, 131.0230, 158.6093, 205.1098],
[126.5263, 124.8587, 161.7550, 252.0944],
[143.2430, 176.5141, 444.2571, 350.0485],
[130.5925, 126.6008, 156.3215, 185.8992],
[ 63.6166, 117.6950, 423.8834, 360.0394]])
) at 0x7f3ee7a217d0>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9546, 0.8740, 0.0169, 0.0145])
labels: tensor([7, 7, 5, 7])
bboxes: tensor([[203.4420, 121.2869, 455.9330, 292.2272],
[ 79.1059, 126.7023, 318.1597, 264.7290],
[418.8734, 173.6405, 456.1266, 210.9511],
[ 62.1651, 123.2019, 486.6631, 279.7594]])
) at 0x7f3ee7a61510>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9619, 0.9321, 0.1240, 0.0707, 0.0252, 0.0239, 0.0155, 0.0145, 0.0127,
0.0108])
labels: tensor([7, 8, 3, 7, 3, 7, 8, 3, 7, 7])
bboxes: tensor([[ 6.7262e+01, 9.5022e+01, 4.0969e+02, 3.3037e+02],
[ 1.7492e+02, 4.3459e+01, 3.3172e+02, 2.9209e+02],
[ 4.8216e+02, -7.6169e-02, 4.9987e+02, 2.2830e+01],
[ 4.2217e+02, 6.5388e-01, 5.0048e+02, 2.8741e+01],
[ 4.3687e+02, 7.9100e-02, 4.9829e+02, 2.8754e+01],
[ 2.8673e+01, 4.3755e+01, 4.9672e+02, 3.3437e+02],
[ 1.3050e+02, 6.5928e-01, 5.0075e+02, 3.0344e+02],
[ 4.7718e+02, 1.7190e-02, 5.0017e+02, 2.7400e+01],
[ 2.9880e+02, -6.5401e-01, 4.6605e+02, 1.9795e+01],
[ 3.6354e+02, -2.0199e-01, 4.6380e+02, 1.7634e+01]])
) at 0x7f3ee79e7490>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.8833, 0.7148, 0.2402, 0.2240, 0.0759, 0.0525, 0.0415, 0.0409, 0.0380,
0.0326, 0.0307, 0.0274, 0.0202, 0.0202, 0.0180, 0.0145, 0.0126, 0.0117,
0.0103])
labels: tensor([7, 8, 8, 8, 8, 8, 7, 8, 8, 7, 8, 7, 8, 7, 7, 8, 8, 8, 8])
bboxes: tensor([[ 2.2677e+01, 3.7042e+01, 4.1990e+02, 3.7350e+02],
[ 3.0712e+02, 1.4007e+01, 5.0147e+02, 3.7427e+02],
[ 9.2354e+01, 1.3782e+00, 3.1780e+02, 8.8807e+01],
[ 1.8119e+02, 1.5311e+01, 5.0162e+02, 3.7277e+02],
[ 2.6469e+01, 1.2096e+00, 4.4931e+02, 3.6715e+02],
[ 3.7200e+02, 1.4806e+00, 4.9909e+02, 3.7391e+02],
[ 2.8920e+01, 1.3390e+00, 4.2460e+02, 1.1683e+02],
[-2.3795e+00, 4.3836e-01, 3.2757e+02, 9.2872e+01],
[ 2.1930e+02, 5.3554e-01, 3.1820e+02, 8.6964e+01],
[ 1.1492e+02, 2.4964e+01, 4.9992e+02, 3.7211e+02],
[ 3.8530e+01, -1.5835e+00, 3.6147e+02, 2.9651e+02],
[ 3.5818e+01, 1.0741e+02, 3.4699e+02, 3.7618e+02],
[ 2.5404e+02, 1.6672e-01, 3.6588e+02, 7.3661e+01],
[ 2.7592e-01, 1.7647e+00, 3.2511e+02, 1.5195e+02],
[ 1.7267e+01, 1.2518e+02, 4.8898e+02, 3.7521e+02],
[ 3.0736e+02, -1.0844e-01, 3.6998e+02, 6.9884e+01],
[ 3.8149e+02, 1.4737e+00, 5.0055e+02, 1.9970e+02],
[-1.4627e+00, 1.6931e+00, 3.2978e+02, 2.0260e+02],
[ 2.5322e+02, 4.6732e-01, 3.2100e+02, 7.5558e+01]])
) at 0x7f3ee7a61050>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9561, 0.9531, 0.0471, 0.0187, 0.0106])
labels: tensor([7, 8, 9, 8, 7])
bboxes: tensor([[ 28.2147, 95.1965, 442.8791, 355.1942],
[118.5745, 25.3001, 226.3474, 227.4343],
[ 17.1296, 1.3847, 61.2395, 59.9922],
[293.4143, 22.6264, 314.0076, 53.6919],
[246.9791, 158.2423, 338.1772, 197.8124]])
) at 0x7f3ee41d4f10>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.8208, 0.7759, 0.6655, 0.3789, 0.2856, 0.2137, 0.1160, 0.1092, 0.1036,
0.0629, 0.0573, 0.0456, 0.0415, 0.0404, 0.0381, 0.0374, 0.0256, 0.0254,
0.0243, 0.0241, 0.0232, 0.0227, 0.0189, 0.0164, 0.0164, 0.0152, 0.0148,
0.0145, 0.0129, 0.0125, 0.0118, 0.0106, 0.0105, 0.0102])
labels: tensor([7, 8, 7, 7, 7, 8, 8, 7, 8, 8, 7, 7, 7, 8, 8, 8, 7, 7, 7, 7, 8, 7, 8, 8,
8, 7, 7, 7, 8, 8, 3, 7, 7, 7])
bboxes: tensor([[107.6121, 222.6573, 285.5639, 388.2802],
[103.5395, 186.8555, 256.6441, 336.1913],
[ 2.9812, 5.7032, 325.7717, 446.2499],
[260.1371, 131.3580, 327.0890, 212.3920],
[ 10.2574, 172.0762, 319.4716, 406.0488],
[145.2424, 218.5026, 257.6946, 333.4505],
[163.5020, 244.3372, 258.5668, 331.4440],
[183.5779, 111.1966, 277.5352, 210.2878],
[178.2922, 81.8450, 276.1833, 208.5847],
[196.2923, 253.9941, 255.8405, 328.4278],
[ 77.0447, 3.9104, 327.4541, 369.1365],
[181.7765, 259.5471, 263.3284, 335.3748],
[ 8.6866, 244.1530, 145.9288, 387.4876],
[301.4442, 21.2958, 313.5034, 46.4288],
[105.7764, 186.8969, 206.5779, 313.8843],
[ 94.0984, 2.4837, 325.2373, 375.6413],
[180.1312, 111.9816, 330.5681, 228.0575],
[ 7.1989, 208.9951, 224.3338, 389.4424],
[105.6959, 216.9335, 261.5157, 342.4415],
[ 6.8825, 274.1206, 300.7866, 487.9888],
[ 98.0376, 3.8735, 325.5930, 239.6812],
[ 93.6460, 6.7383, 325.6896, 152.4414],
[ 98.2623, 3.7374, 291.0093, 89.4755],
[105.2998, 186.4017, 199.4408, 276.0983],
[ 9.3683, 240.4074, 294.7867, 488.4989],
[187.5747, 283.7428, 286.8134, 377.1947],
[219.3730, 120.8627, 326.4662, 331.8717],
[107.1435, 223.1085, 206.3822, 321.8134],
[ 5.3824, 8.4913, 319.4661, 437.9931],
[165.9957, 8.0260, 328.3050, 357.2084],
[220.7658, 138.5865, 327.0256, 337.9760],
[ 10.0260, 250.3859, 103.9833, 383.2079],
[183.1734, 101.3395, 328.6973, 287.9183],
[133.5665, 91.0965, 324.8135, 376.0910]])
) at 0x7f4007869010>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9624, 0.9463])
labels: tensor([7, 8])
bboxes: tensor([[141.7842, 121.3854, 410.5596, 316.1146],
[162.0757, 35.4768, 323.8618, 290.4998]])
) at 0x7f3ee796fad0>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.8877, 0.8862, 0.8774, 0.8477, 0.7700, 0.7676, 0.7368, 0.7251, 0.7095,
0.6724, 0.6685, 0.6572, 0.6543, 0.6411, 0.6123, 0.6025, 0.5327, 0.4507,
0.4333, 0.4229, 0.4221, 0.4185, 0.3948, 0.3186, 0.3052, 0.2803, 0.2603,
0.2329, 0.2141, 0.1658, 0.1628, 0.1547, 0.1514, 0.1379, 0.1345, 0.1204,
0.1166, 0.1058, 0.1054, 0.0854, 0.0795, 0.0689, 0.0677, 0.0657, 0.0620,
0.0586, 0.0453, 0.0449, 0.0446, 0.0417, 0.0399, 0.0369, 0.0355, 0.0349,
0.0280, 0.0275, 0.0267, 0.0263, 0.0260, 0.0248, 0.0242, 0.0226, 0.0222,
0.0167, 0.0141, 0.0136, 0.0126, 0.0109, 0.0107, 0.0102, 0.0101])
labels: tensor([8, 7, 8, 7, 8, 7, 7, 8, 3, 7, 7, 7, 7, 7, 8, 7, 3, 8, 7, 8, 7, 7, 7, 7,
8, 8, 7, 2, 7, 7, 7, 7, 8, 3, 7, 7, 7, 7, 8, 7, 7, 8, 3, 3, 8, 7, 7, 8,
7, 7, 2, 7, 3, 7, 7, 8, 3, 7, 8, 8, 8, 8, 8, 3, 3, 7, 7, 8, 8, 8, 3])
bboxes: tensor([[2.9167e+01, 9.7510e+01, 4.5442e+01, 1.4137e+02],
[2.0136e+02, 1.3167e+02, 3.1973e+02, 2.7076e+02],
[1.9974e+02, 5.8775e+01, 3.0964e+02, 2.3807e+02],
[3.8088e+02, 1.2098e+02, 4.0897e+02, 1.4230e+02],
[4.5882e+02, 1.0819e+02, 4.7946e+02, 1.5977e+02],
[1.3112e+02, 1.1186e+02, 1.5306e+02, 1.4010e+02],
[1.5186e+02, 1.1247e+02, 1.6787e+02, 1.4046e+02],
[3.1250e+02, 1.1843e+02, 3.2226e+02, 1.3899e+02],
[3.2613e+02, 1.0327e+02, 3.9614e+02, 1.2722e+02],
[3.0808e+02, 1.2322e+02, 3.2942e+02, 1.4065e+02],
[3.2508e+02, 1.2069e+02, 3.5266e+02, 1.4083e+02],
[6.4860e+01, 1.1756e+02, 9.9788e+01, 1.4005e+02],
[1.6611e+02, 1.1354e+02, 1.8233e+02, 1.4134e+02],
[1.9305e+02, 1.1452e+02, 2.1359e+02, 1.3763e+02],
[7.3996e+01, 9.6701e+01, 8.8211e+01, 1.3691e+02],
[8.7585e+01, 1.1616e+02, 1.1671e+02, 1.3931e+02],
[4.3005e+02, 1.1471e+02, 4.4808e+02, 1.2905e+02],
[6.4193e+01, 9.8755e+01, 7.6335e+01, 1.3720e+02],
[1.8245e+02, 1.1660e+02, 1.9880e+02, 1.3829e+02],
[4.2007e+02, 1.1165e+02, 4.2681e+02, 1.2938e+02],
[4.7867e+02, 1.2238e+02, 4.9945e+02, 1.4227e+02],
[4.7772e+02, 1.2339e+02, 4.9024e+02, 1.4203e+02],
[1.2232e+02, 1.1164e+02, 1.3881e+02, 1.4012e+02],
[4.0659e+02, 1.1793e+02, 4.2076e+02, 1.4086e+02],
[3.9408e+02, 1.1447e+02, 4.0748e+02, 1.3924e+02],
[4.7931e+02, 1.1428e+02, 4.9178e+02, 1.4178e+02],
[4.3269e+02, 1.2006e+02, 4.5402e+02, 1.3932e+02],
[2.7019e-01, 4.4724e+01, 8.3226e+01, 1.3756e+02],
[4.2965e+02, 1.1466e+02, 4.4770e+02, 1.2911e+02],
[1.1279e+02, 1.1818e+02, 1.2784e+02, 1.4060e+02],
[2.3148e+02, 1.2040e+02, 2.4274e+02, 1.3956e+02],
[1.1408e+02, 1.1367e+02, 1.3592e+02, 1.4161e+02],
[3.8899e+02, 1.1440e+02, 4.0554e+02, 1.3971e+02],
[4.3035e+02, 1.1317e+02, 4.5793e+02, 1.2961e+02],
[4.3070e+02, 1.1619e+02, 4.4977e+02, 1.3558e+02],
[4.8599e+02, 1.1731e+02, 4.9994e+02, 1.4206e+02],
[3.1023e+02, 1.1992e+02, 3.2493e+02, 1.4063e+02],
[1.7918e+02, 1.1745e+02, 1.9211e+02, 1.3880e+02],
[2.0136e+02, 5.9152e+01, 2.9591e+02, 1.8071e+02],
[3.8817e+02, 1.1767e+02, 4.1026e+02, 1.4092e+02],
[4.4471e+02, 1.1742e+02, 4.5998e+02, 1.3980e+02],
[2.0212e+02, 1.0178e+02, 3.0218e+02, 2.2942e+02],
[4.8680e+02, 1.1551e+02, 4.9992e+02, 1.4016e+02],
[4.8964e+02, 1.1386e+02, 5.0021e+02, 1.2346e+02],
[2.3174e+02, 1.2203e+02, 2.4404e+02, 1.4027e+02],
[1.5368e+02, 1.1616e+02, 1.7034e+02, 1.4204e+02],
[2.0190e+02, 1.2399e+02, 2.9888e+02, 2.3531e+02],
[3.1020e+02, 1.1706e+02, 3.2183e+02, 1.3314e+02],
[6.5246e+01, 1.1197e+02, 8.1141e+01, 1.3901e+02],
[4.4829e+02, 1.1482e+02, 4.6108e+02, 1.4006e+02],
[3.2350e+02, 1.0309e+02, 4.0501e+02, 1.2721e+02],
[1.2316e+02, 1.1063e+02, 1.3563e+02, 1.3606e+02],
[2.9570e+02, 1.0867e+02, 3.2344e+02, 1.2045e+02],
[2.0813e+02, 1.1283e+02, 2.2312e+02, 1.3737e+02],
[1.2781e+02, 1.1656e+02, 1.4661e+02, 1.4106e+02],
[4.0661e+02, 1.1373e+02, 4.1918e+02, 1.3882e+02],
[2.9113e+02, 1.0101e+02, 3.3934e+02, 1.1933e+02],
[3.2903e+02, 1.2226e+02, 3.4753e+02, 1.4141e+02],
[4.0648e+02, 1.1270e+02, 4.1539e+02, 1.3731e+02],
[3.2796e+02, 1.1441e+02, 3.4314e+02, 1.3892e+02],
[2.1497e+02, 7.1558e+01, 2.9050e+02, 1.5600e+02],
[4.1444e+02, 1.1236e+02, 4.2619e+02, 1.2984e+02],
[4.9011e+02, 1.0855e+02, 4.9973e+02, 1.2214e+02],
[4.4282e+02, 1.1186e+02, 4.6031e+02, 1.3307e+02],
[4.4732e+02, 1.1208e+02, 4.5893e+02, 1.2133e+02],
[4.7218e+02, 1.1803e+02, 4.9970e+02, 1.4934e+02],
[2.2328e+02, 1.0452e+02, 2.4312e+02, 1.3983e+02],
[1.2407e+02, 1.1132e+02, 1.3628e+02, 1.3986e+02],
[2.4888e+02, 1.0528e+02, 2.6714e+02, 1.2345e+02],
[2.2206e+02, 6.9534e+01, 2.5645e+02, 1.3949e+02],
[2.1643e+02, 9.7828e+01, 5.0310e+02, 3.8150e+02]])
) at 0x7f3ee7a61390>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9595, 0.8433])
labels: tensor([7, 8])
bboxes: tensor([[105.5714, 161.1331, 385.0536, 372.5418],
[172.4518, 65.6327, 322.0795, 284.2252]])
) at 0x7f3ee7a22dd0>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9492, 0.9282, 0.9028, 0.8994, 0.8120, 0.7759, 0.7632, 0.7085, 0.6904,
0.2520, 0.1516, 0.1471, 0.1083, 0.0917, 0.0880, 0.0878, 0.0634, 0.0632,
0.0452, 0.0429, 0.0394, 0.0385, 0.0354, 0.0353, 0.0307, 0.0303, 0.0301,
0.0217, 0.0199, 0.0178, 0.0173, 0.0161, 0.0150, 0.0146, 0.0143, 0.0131,
0.0130, 0.0127, 0.0124, 0.0122, 0.0121, 0.0115, 0.0112, 0.0111, 0.0102])
labels: tensor([7, 8, 8, 8, 8, 7, 8, 8, 8, 8, 8, 8, 7, 7, 7, 8, 7, 7, 7, 8, 7, 8, 7, 8,
8, 8, 7, 8, 8, 8, 8, 7, 7, 8, 8, 8, 7, 8, 5, 8, 7, 8, 8, 7, 8])
bboxes: tensor([[ 3.3193e+01, 3.6645e+01, 4.5274e+02, 3.0281e+02],
[ 2.9582e+02, 1.0286e+01, 3.6824e+02, 1.2819e+02],
[ 4.0209e+02, 2.5292e+01, 4.9400e+02, 2.2197e+02],
[ 5.3843e-01, 1.3934e+01, 5.5907e+01, 1.9798e+02],
[ 3.4993e+02, 2.8327e+01, 3.7663e+02, 9.9700e+01],
[ 3.3636e+02, 1.2873e+00, 3.9880e+02, 2.4640e+01],
[ 3.7746e+02, 4.1489e+01, 3.9675e+02, 9.6011e+01],
[ 4.1153e+02, 1.7465e+01, 4.4628e+02, 1.2111e+02],
[ 3.8733e+02, 4.8811e+01, 4.1657e+02, 9.3572e+01],
[ 4.8272e+02, 3.9872e+01, 5.0009e+02, 1.7321e+02],
[ 4.6932e+02, 3.4692e+01, 4.9943e+02, 1.7254e+02],
[ 4.0858e+02, 1.6441e+01, 4.4689e+02, 1.8239e+02],
[ 3.8124e+02, 6.7645e+01, 4.1447e+02, 9.7784e+01],
[ 3.1282e+02, 2.7435e+01, 5.0046e+02, 1.3331e+02],
[ 3.8187e+02, 6.0095e+01, 4.1345e+02, 1.2741e+02],
[ 4.8531e+02, 7.7922e+01, 4.9985e+02, 1.7012e+02],
[ 2.9882e+02, 6.4717e+01, 4.3165e+02, 1.3079e+02],
[ 2.9023e+01, 1.3953e+02, 4.6785e+02, 3.1477e+02],
[ 4.6990e+02, 2.7266e+01, 5.0041e+02, 1.9383e+02],
[ 4.7019e+02, 3.5446e+01, 4.9856e+02, 8.8187e+01],
[ 4.2405e+02, 2.6574e+01, 5.0173e+02, 1.6835e+02],
[ 4.3357e+02, 2.9882e+01, 4.7268e+02, 1.2481e+02],
[ 2.9806e+02, 8.1847e+01, 3.7538e+02, 1.2870e+02],
[ 3.8218e+02, 6.3257e+01, 4.1157e+02, 1.3381e+02],
[ 4.5939e+02, 2.7896e+01, 4.9998e+02, 1.1742e+02],
[ 3.7924e+02, 4.3251e+01, 4.0747e+02, 9.7667e+01],
[ 2.9592e+02, 5.5141e+01, 3.7205e+02, 1.2943e+02],
[ 3.8884e+02, 4.8932e+01, 4.1585e+02, 7.6752e+01],
[ 3.9232e+02, 3.6931e+00, 4.9986e+02, 1.1974e+02],
[ 3.2494e+01, 9.5904e+01, 3.2942e+02, 3.2988e+02],
[ 4.1319e+02, 2.2822e+01, 4.4306e+02, 9.8076e+01],
[ 3.4861e+02, -1.3695e-01, 4.6155e+02, 2.4820e+01],
[ 3.1542e+01, 8.6452e+01, 3.0615e+02, 2.8894e+02],
[ 4.8596e+02, 5.5080e+01, 4.9998e+02, 2.5215e+02],
[ 4.8862e+02, 3.8495e+01, 4.9967e+02, 1.5369e+02],
[ 3.7919e+02, 4.2307e+01, 3.9698e+02, 7.1462e+01],
[ 4.8527e+02, 8.0364e+01, 4.9989e+02, 1.7257e+02],
[ 4.4580e+02, 2.9578e+01, 4.9951e+02, 1.6085e+02],
[ 3.2478e+01, 1.0653e+02, 4.5698e+02, 3.2082e+02],
[ 4.9229e+02, 4.0129e+01, 4.9990e+02, 9.6492e+01],
[ 3.3570e+02, 8.8397e+01, 4.3813e+02, 1.3211e+02],
[ 3.2719e+02, 2.4958e+00, 4.9937e+02, 1.2270e+02],
[ 4.9273e+02, 2.3355e+01, 5.0024e+02, 1.5389e+02],
[ 3.5717e+02, 1.5272e-01, 4.2017e+02, 2.3602e+01],
[ 4.3235e+02, 4.2495e+01, 4.5437e+02, 1.0711e+02]])
) at 0x7f3ee6dde3d0>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9409, 0.8770, 0.3721, 0.2307, 0.0948])
labels: tensor([7, 8, 3, 7, 8])
bboxes: tensor([[-8.8537e-01, 2.5943e+01, 2.2901e+02, 2.3321e+02],
[-3.9432e-01, 2.4241e+00, 1.7764e+02, 1.9756e+02],
[ 1.7359e+02, 1.7634e+00, 4.9907e+02, 1.6287e+02],
[ 1.6556e+02, 3.4396e+00, 4.9616e+02, 1.6158e+02],
[ 6.3987e+01, 4.3885e+00, 1.8445e+02, 1.5966e+02]])
) at 0x7f4007868dd0>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9595, 0.9395, 0.9302, 0.8193, 0.8037, 0.7153, 0.7109, 0.6982, 0.6807,
0.3882, 0.3198, 0.2360, 0.2240, 0.2170, 0.2153, 0.1539, 0.1348, 0.0883,
0.0670, 0.0592, 0.0547, 0.0338, 0.0329, 0.0323, 0.0308, 0.0305, 0.0302,
0.0223, 0.0216, 0.0187, 0.0173, 0.0160, 0.0150, 0.0143, 0.0110, 0.0109,
0.0102])
labels: tensor([3, 7, 3, 3, 3, 3, 3, 3, 7, 8, 3, 3, 3, 3, 3, 8, 3, 3, 7, 8, 8, 3, 3, 3,
8, 7, 7, 3, 7, 3, 3, 8, 8, 3, 3, 3, 8])
bboxes: tensor([[ 3.7895e+02, 4.1991e+01, 4.9918e+02, 1.7930e+02],
[ 3.1101e+02, 3.5166e+01, 3.9719e+02, 9.6573e+01],
[ 1.1747e+02, 5.9787e+01, 2.0734e+02, 1.4178e+02],
[ 8.6965e+01, 3.1939e+01, 1.0473e+02, 4.5405e+01],
[ 2.0702e+02, 1.5591e+01, 3.9142e+02, 7.6938e+01],
[ 1.7551e+01, 2.5087e+01, 3.5867e+01, 3.8390e+01],
[ 6.6929e-02, 2.4374e+01, 1.8854e+01, 4.2179e+01],
[ 1.1890e+02, 2.9259e+01, 1.3911e+02, 4.3300e+01],
[ 1.2854e+02, 6.9622e+01, 4.4959e+02, 3.4210e+02],
[ 2.0479e+02, 2.9726e+01, 2.1201e+02, 4.9473e+01],
[-5.9384e-02, 2.8679e+01, 5.2657e+00, 4.6711e+01],
[-4.6429e-02, 2.8496e+01, 1.1246e+01, 4.5918e+01],
[ 9.9281e+01, 3.0093e+01, 1.3490e+02, 4.5152e+01],
[ 1.2857e+02, 7.0877e+01, 4.5151e+02, 3.4123e+02],
[ 1.2366e+02, 3.0635e+01, 1.4665e+02, 4.3681e+01],
[ 1.7021e+02, 2.7580e+01, 1.7471e+02, 3.6971e+01],
[ 1.6246e+01, 2.6205e+01, 2.8188e+01, 4.1568e+01],
[ 1.3313e+02, 3.2020e+01, 1.5164e+02, 4.4347e+01],
[ 1.7913e+02, 3.3306e+01, 1.9489e+02, 4.3158e+01],
[ 2.0628e+02, 2.9066e+01, 2.1443e+02, 4.9352e+01],
[ 1.9986e+02, 2.4428e+01, 2.0912e+02, 4.4761e+01],
[-2.2386e+00, -1.4319e+00, 2.6864e+02, 3.4167e+02],
[ 1.8194e+02, 3.3927e+01, 1.9403e+02, 4.3124e+01],
[ 1.1809e+02, 6.6725e+01, 1.5046e+02, 1.4382e+02],
[-5.1401e+00, 4.8028e+01, 2.6627e+02, 5.9416e+02],
[ 6.6190e-01, 1.8252e+00, 4.9309e+02, 3.5169e+02],
[ 1.9261e+02, 3.3259e+01, 2.0017e+02, 4.2864e+01],
[ 3.5854e+01, 2.5058e+01, 4.9254e+01, 3.4268e+01],
[-6.6665e+00, 1.0536e+00, 2.9026e+02, 3.5852e+02],
[-4.5555e-01, 1.9579e+01, 1.4005e+01, 4.2823e+01],
[ 2.1514e+02, 2.0342e+01, 2.7432e+02, 3.3515e+01],
[ 2.0552e+02, 2.8192e+01, 2.1753e+02, 5.0372e+01],
[-1.2922e+00, 3.2763e+00, 2.5071e+02, 3.9282e+02],
[ 9.4567e+01, 2.9459e+01, 1.2262e+02, 4.5443e+01],
[-3.2721e+00, 2.1793e+00, 1.7788e+02, 3.2829e+02],
[ 1.3932e+02, 1.6868e+02, 4.5794e+02, 3.4225e+02],
[ 1.7011e+02, 2.7326e+01, 1.7560e+02, 4.0057e+01]])
) at 0x7f3ed0e37590>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9434, 0.1816, 0.0680, 0.0392, 0.0258, 0.0155])
labels: tensor([7, 7, 7, 7, 7, 7])
bboxes: tensor([[ 7.5134e+00, 2.3324e+02, 3.9330e+02, 4.9528e+02],
[ 1.9711e+02, 2.3290e+02, 3.0918e+02, 3.0421e+02],
[ 5.2555e+00, 3.1241e+02, 3.0453e+02, 4.9462e+02],
[-1.2620e-01, 3.5826e+02, 1.1345e+01, 4.0775e+02],
[ 2.7551e-03, 3.2995e+02, 1.2382e+01, 4.0911e+02],
[ 1.8250e+02, 2.3186e+02, 3.9021e+02, 4.5252e+02]])
) at 0x7f3ee4158390>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9473, 0.9146, 0.8794, 0.2395, 0.0888, 0.0298, 0.0214, 0.0208, 0.0160,
0.0141, 0.0120])
labels: tensor([7, 8, 8, 7, 7, 7, 7, 7, 8, 7, 3])
bboxes: tensor([[ 1.3882e+00, 5.9346e+01, 4.0408e+02, 3.3091e+02],
[ 3.6467e+02, 1.3273e+02, 4.2674e+02, 2.9055e+02],
[ 2.2948e+00, -3.8393e-01, 1.8774e+02, 3.2400e+02],
[ 5.3200e+00, 2.3990e+02, 3.1167e+02, 3.3230e+02],
[-1.7622e-01, 2.5288e+02, 2.3748e+02, 3.3300e+02],
[-2.5967e+00, 1.9070e+02, 3.1783e+02, 3.3381e+02],
[ 2.0076e+02, 2.3163e+02, 5.0002e+02, 3.3470e+02],
[ 4.7224e-01, 3.3985e+01, 2.1984e+02, 3.3517e+02],
[ 1.2938e+00, 1.7187e+02, 2.1726e+02, 3.3466e+02],
[ 8.1891e+01, 2.5283e+02, 3.1049e+02, 3.3149e+02],
[-9.2107e-02, 4.1866e-01, 1.7226e+02, 1.7575e+02]])
) at 0x7f3ed0e37510>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9468, 0.1285, 0.0669, 0.0366, 0.0363, 0.0225, 0.0223, 0.0218, 0.0129,
0.0128, 0.0125, 0.0104, 0.0103])
labels: tensor([7, 7, 7, 7, 7, 7, 8, 7, 7, 7, 8, 7, 3])
bboxes: tensor([[ 3.0056, 26.4785, 495.4319, 317.6621],
[257.7522, 279.4475, 489.9040, 376.4119],
[382.1909, 331.1739, 483.4341, 374.2949],
[383.3167, 335.5938, 441.6833, 374.1718],
[251.3298, 221.0505, 493.2014, 376.6057],
[248.3202, 121.5623, 499.3361, 372.5783],
[154.2496, 216.1385, 166.8442, 246.7522],
[357.1429, 127.8096, 499.8884, 370.6279],
[409.7569, 351.3511, 484.7744, 375.2114],
[ 35.3058, 160.3600, 497.8973, 376.7493],
[180.1309, 100.7995, 492.5253, 378.1067],
[183.3872, 170.5746, 493.9565, 377.8629],
[398.1004, 166.0860, 500.3371, 371.8047]])
) at 0x7f3ed0e37f10>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9536])
labels: tensor([7])
bboxes: tensor([[154.3900, 25.9610, 350.6882, 148.4746]])
) at 0x7f3ee433a910>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9131, 0.0285, 0.0257, 0.0187, 0.0140, 0.0101])
labels: tensor([7, 7, 3, 7, 7, 8])
bboxes: tensor([[ 5.4654e+01, 9.2944e+01, 4.6449e+02, 2.6956e+02],
[-3.2652e+00, 3.7368e-01, 5.0678e+02, 8.4783e+01],
[ 2.2523e+01, 1.4226e+02, 4.4811e+01, 2.5305e+02],
[ 1.8369e+01, 1.1087e+02, 1.5067e+02, 2.6315e+02],
[ 2.5029e+02, 1.4985e+02, 4.5361e+02, 2.5953e+02],
[ 3.5046e+02, 2.3655e+02, 3.6868e+02, 2.6774e+02]])
) at 0x7f3ee6d7a450>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9468, 0.9038, 0.7368, 0.5620, 0.4287, 0.2666, 0.2074, 0.0999, 0.0742,
0.0725, 0.0696, 0.0692, 0.0688, 0.0671, 0.0655, 0.0482, 0.0403, 0.0397,
0.0396, 0.0339, 0.0314, 0.0302, 0.0292, 0.0283, 0.0282, 0.0258, 0.0246,
0.0243, 0.0230, 0.0230, 0.0225, 0.0194, 0.0190, 0.0184, 0.0183, 0.0182,
0.0181, 0.0175, 0.0171, 0.0147, 0.0142, 0.0139, 0.0138, 0.0129, 0.0124,
0.0123, 0.0105])
labels: tensor([7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 7, 7, 8, 8, 7, 7, 8, 7, 8, 8, 3, 8, 8,
7, 7, 8, 7, 8, 7, 8, 7, 8, 8, 3, 8, 8, 8, 7, 7, 7, 7, 8, 8, 8, 7, 7])
bboxes: tensor([[ 1.9803e+01, 5.8279e+01, 4.9895e+02, 3.1396e+02],
[ 1.0493e-01, 8.7125e-01, 2.1025e+02, 1.9500e+02],
[ 2.4995e+02, 8.5840e-01, 4.9419e+02, 1.2917e+02],
[ 2.4889e+02, 2.4603e+00, 4.6322e+02, 9.8208e+01],
[ 1.7215e+02, -3.1539e-01, 2.9309e+02, 8.2547e+01],
[ 4.4673e+02, 2.0614e+02, 4.9936e+02, 3.3348e+02],
[-2.4425e+00, -7.8632e-01, 5.0166e+02, 8.9846e+01],
[ 4.5056e+02, 4.9713e+01, 5.0023e+02, 3.3774e+02],
[-1.7203e+00, 1.4086e+00, 2.8551e+02, 8.2969e+01],
[ 4.4741e+02, 1.4337e-01, 5.0024e+02, 2.9950e+01],
[ 4.0200e+02, 6.1402e+00, 4.9956e+02, 2.1568e+02],
[ 3.5307e+02, 2.8277e+00, 4.9693e+02, 3.2415e+02],
[ 4.0144e+02, 3.9824e+00, 5.0090e+02, 3.2846e+02],
[-1.6784e+00, -1.5956e-03, 1.6701e+02, 8.2038e+01],
[ 2.0164e+02, 5.8368e-01, 5.0266e+02, 8.2624e+01],
[ 3.5900e+02, 8.5336e-02, 4.8475e+02, 8.1903e+01],
[ 1.2807e+02, -9.8327e-01, 2.7857e+02, 8.3020e+01],
[ 4.2604e+02, -9.9776e-02, 4.9975e+02, 9.0818e+01],
[ 1.7857e+02, 8.5948e-01, 5.0151e+02, 8.1470e+01],
[-2.7031e+00, -2.9007e-01, 2.5427e+02, 8.3205e+01],
[ 4.7214e+02, -7.6454e-02, 4.9974e+02, 3.0413e+01],
[ 8.3232e+01, -2.3424e-01, 2.6872e+02, 8.3442e+01],
[ 1.1495e+00, 2.2466e+00, 2.4397e+02, 3.3702e+02],
[ 3.7860e+02, -3.4894e-01, 4.3312e+02, 1.6456e+01],
[ 4.2085e+02, 1.2984e+01, 5.0024e+02, 2.1137e+02],
[ 4.6266e+02, 2.0919e+01, 4.9984e+02, 2.0949e+02],
[ 3.7105e+02, 8.0152e-01, 4.8052e+02, 7.9870e+01],
[ 1.4561e+00, 2.1918e+00, 4.9581e+02, 1.4959e+02],
[ 3.5793e+02, 2.3168e+00, 4.9832e+02, 3.0476e+02],
[ 1.9371e+02, 6.5695e-01, 3.3676e+02, 8.4989e+01],
[ 4.3327e+02, 1.7567e-01, 4.9954e+02, 4.4184e+01],
[ 2.4250e+02, -3.8620e+00, 4.9500e+02, 3.3552e+02],
[ 3.8112e+02, -5.2157e-02, 4.6185e+02, 1.5952e+01],
[ 4.5413e+02, -1.9793e-01, 5.0056e+02, 2.1012e+02],
[ 4.7178e+02, 3.4098e+01, 5.0009e+02, 2.1055e+02],
[ 3.6835e+02, -3.6664e-01, 4.9962e+02, 2.1144e+01],
[ 2.7726e+02, 1.9989e+00, 5.0008e+02, 1.1837e+02],
[ 3.3928e+02, -4.5346e-01, 5.0056e+02, 8.3368e+01],
[ 4.2460e+02, 3.3615e-01, 4.9962e+02, 9.1699e+01],
[ 3.7647e+02, 5.3618e+01, 4.9931e+02, 2.4741e+02],
[ 2.5176e+02, 1.5027e+00, 4.1738e+02, 7.5998e+01],
[ 2.4939e+02, 1.4294e+00, 3.3186e+02, 1.2606e+02],
[ 3.3635e+02, -5.2170e-01, 4.5311e+02, 1.9568e+01],
[ 4.6423e+02, -1.0355e-01, 5.0061e+02, 7.8043e+01],
[ 2.9347e+02, 4.0242e+00, 4.9755e+02, 2.8374e+02],
[ 3.1029e+02, -3.2571e-01, 4.5846e+02, 2.2640e+01],
[ 2.4965e+02, 5.0484e+01, 4.4215e+02, 1.2081e+02]])
) at 0x7f3ee7a29f50>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9399, 0.9390, 0.9292, 0.9253, 0.9028, 0.8257, 0.7153, 0.5400, 0.4783,
0.4478, 0.3853, 0.3362, 0.2441, 0.2246, 0.1694, 0.1160, 0.0627, 0.0532,
0.0508, 0.0414, 0.0413, 0.0379, 0.0304, 0.0221, 0.0214, 0.0202, 0.0194,
0.0189, 0.0175, 0.0160, 0.0152, 0.0144, 0.0140, 0.0132, 0.0126, 0.0114,
0.0109, 0.0109, 0.0104, 0.0104, 0.0103])
labels: tensor([8, 7, 7, 8, 7, 8, 7, 8, 8, 8, 8, 7, 7, 8, 8, 7, 8, 7, 3, 8, 3, 8, 3, 8,
8, 3, 3, 7, 7, 8, 7, 3, 3, 3, 3, 3, 8, 7, 3, 7, 3])
bboxes: tensor([[ 1.2379e+02, 8.1221e+01, 1.7406e+02, 2.4144e+02],
[ 4.4981e+01, 1.2856e+02, 1.4330e+02, 2.8629e+02],
[ 2.0937e+02, 1.0577e+02, 3.2774e+02, 3.2821e+02],
[ 3.5726e+02, 8.8662e+01, 4.3766e+02, 2.4825e+02],
[ 3.5132e+02, 1.2587e+02, 4.3110e+02, 2.6398e+02],
[ 3.2574e+02, 9.4665e+01, 3.7075e+02, 2.3912e+02],
[ 4.9315e+02, 1.1870e+02, 4.9982e+02, 1.5435e+02],
[ 2.5181e+02, 1.8157e+02, 3.4975e+02, 2.8562e+02],
[ 2.1974e+02, 9.9419e+01, 3.3455e+02, 2.8632e+02],
[ 4.9224e+02, 1.1590e+02, 4.9995e+02, 1.5148e+02],
[ 4.5999e+01, 1.1264e+02, 1.1201e+02, 2.2759e+02],
[ 2.3421e+02, 1.0631e+02, 2.6266e+02, 1.3022e+02],
[ 2.3929e+02, 1.1170e+02, 2.6110e+02, 1.3088e+02],
[ 2.3070e+02, 1.3744e+02, 3.4976e+02, 2.8561e+02],
[ 2.3852e+02, 1.0186e+02, 2.5952e+02, 1.3017e+02],
[-9.2340e-02, 2.2341e+02, 5.6374e+00, 2.6995e+02],
[ 2.8599e+02, 1.9022e+02, 3.5034e+02, 2.8478e+02],
[-4.5790e-02, 1.8465e+02, 6.2225e+00, 2.7043e+02],
[-2.7738e-01, 1.0856e+02, 9.3228e+00, 1.1722e+02],
[-1.9509e-01, 1.2347e+02, 7.1836e+00, 2.8278e+02],
[ 3.8259e+01, 1.0713e+02, 6.0569e+01, 1.1631e+02],
[-8.3132e-02, 1.6601e+02, 6.4796e+00, 2.7423e+02],
[ 6.9977e+01, 1.0376e+02, 8.7640e+01, 1.1049e+02],
[ 4.9196e+02, 9.7746e+01, 5.0022e+02, 1.5421e+02],
[ 5.2653e+01, 1.3144e+02, 9.7152e+01, 2.0938e+02],
[ 5.7320e+01, 1.0548e+02, 6.9145e+01, 1.1112e+02],
[ 8.2361e+00, 1.0907e+02, 2.6822e+01, 1.1769e+02],
[ 4.9461e+02, 1.1904e+02, 4.9992e+02, 1.8545e+02],
[ 4.8988e+02, 1.1671e+02, 4.9997e+02, 1.5458e+02],
[-7.2592e-02, 1.0653e+02, 1.0528e+01, 2.8565e+02],
[ 4.0275e+01, 7.5532e+01, 3.5328e+02, 3.3150e+02],
[ 4.0119e+01, 1.0612e+02, 6.0369e+01, 1.1263e+02],
[ 5.6759e+01, 1.0496e+02, 7.3222e+01, 1.1047e+02],
[ 3.0645e+00, 1.0929e+02, 1.2878e+01, 1.1785e+02],
[ 5.9713e+00, 1.1271e+02, 2.1739e+01, 1.1932e+02],
[ 1.4968e+01, 1.0967e+02, 2.6829e+01, 1.1787e+02],
[ 2.2421e+02, 1.0021e+02, 3.1603e+02, 2.4608e+02],
[ 2.2065e+02, 1.1679e+02, 3.2779e+02, 2.4961e+02],
[-6.4029e-02, 1.8859e+02, 6.0638e+00, 2.7352e+02],
[ 3.2198e+02, 1.0331e+02, 4.3623e+02, 2.5645e+02],
[ 3.5460e+01, 1.1407e+02, 7.3329e+01, 1.3319e+02]])
) at 0x7f3ee841bc50>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9502, 0.9482, 0.9316, 0.9224, 0.9224, 0.9087, 0.9087, 0.9077, 0.9053,
0.8750, 0.8716, 0.8521, 0.6274, 0.5679, 0.5449, 0.3599, 0.3503, 0.2969,
0.2764, 0.1637, 0.1234, 0.1182, 0.1019, 0.0770, 0.0597, 0.0447, 0.0445,
0.0365, 0.0319, 0.0280, 0.0279, 0.0219, 0.0181, 0.0173, 0.0156, 0.0152,
0.0152, 0.0146, 0.0127, 0.0109, 0.0109, 0.0109, 0.0107, 0.0106])
labels: tensor([7, 7, 8, 8, 7, 7, 2, 8, 8, 8, 8, 8, 8, 8, 8, 7, 2, 3, 7, 8, 8, 7, 8, 7,
2, 8, 8, 7, 8, 8, 7, 8, 8, 7, 8, 2, 7, 7, 3, 8, 8, 8, 7, 8])
bboxes: tensor([[ 2.9656e+02, 1.1233e+02, 3.8000e+02, 1.9661e+02],
[ 3.8488e+02, 1.0397e+02, 4.6434e+02, 1.9600e+02],
[ 3.9520e+02, 5.8980e+01, 4.6574e+02, 1.7795e+02],
[ 3.0874e+02, 6.9711e+01, 3.8032e+02, 1.7307e+02],
[ 2.5653e+02, 1.0001e+02, 3.1964e+02, 1.8298e+02],
[ 1.0255e+02, 1.0287e+02, 2.0741e+02, 2.0745e+02],
[ 2.5706e+02, 1.9562e+00, 5.0036e+02, 1.0734e+02],
[ 2.6532e+02, 7.0248e+01, 3.2101e+02, 1.6473e+02],
[ 1.2569e+02, 7.2699e+01, 2.0946e+02, 2.0209e+02],
[ 4.4206e+02, 4.1958e+01, 4.7200e+02, 1.0832e+02],
[ 4.2475e+02, 3.9378e+01, 4.4869e+02, 6.5621e+01],
[ 1.5271e+02, 4.1371e+01, 1.7913e+02, 8.0997e+01],
[ 6.8293e+01, 3.5225e+01, 8.1609e+01, 6.5578e+01],
[ 1.2576e+02, 7.4306e+01, 1.7521e+02, 1.1305e+02],
[ 2.0539e+01, 3.4729e+01, 3.2440e+01, 6.3049e+01],
[ 5.9072e+01, 4.1150e+01, 8.2334e+01, 6.5605e+01],
[ 9.7895e-01, -2.3598e-01, 1.9306e+02, 1.1304e+02],
[ 2.5952e+02, 8.7785e+00, 4.7681e+02, 1.0198e+02],
[ 4.2634e+02, 6.6099e+01, 4.7444e+02, 1.1677e+02],
[ 4.3958e+02, 3.8276e+01, 4.5261e+02, 5.7550e+01],
[ 4.9522e+02, 4.8791e+01, 5.0010e+02, 1.2012e+02],
[ 2.0457e+01, 3.8811e+01, 3.2668e+01, 6.3651e+01],
[ 7.6018e-02, 3.9516e+01, 1.1637e+01, 6.4995e+01],
[ 4.0479e+02, 6.3172e+01, 4.7568e+02, 1.1638e+02],
[ 3.5120e+02, 7.0058e-01, 4.9880e+02, 9.6296e+01],
[ 4.9604e+02, 6.3604e+01, 5.0006e+02, 1.2971e+02],
[ 4.9377e+02, 2.6104e+01, 4.9998e+02, 1.2456e+02],
[ 6.4635e+01, 4.6866e+01, 8.1459e+01, 6.6330e+01],
[ 4.2120e+02, 4.4436e+01, 4.4990e+02, 1.0379e+02],
[ 2.6786e+02, 5.7681e+01, 2.8995e+02, 1.0011e+02],
[ 1.0089e+02, 1.0181e+02, 1.5926e+02, 1.8996e+02],
[ 2.7060e+02, 5.7314e+01, 2.8721e+02, 8.7108e+01],
[ 4.9142e+02, 8.9753e+00, 4.9998e+02, 1.2139e+02],
[ 4.3980e+02, 7.5850e+01, 4.7426e+02, 1.1697e+02],
[ 4.8965e+02, 2.9831e+01, 4.9941e+02, 1.2601e+02],
[-7.2174e-01, -1.3527e+00, 2.0697e+02, 1.7037e+02],
[ 2.0753e+01, 4.2578e+01, 3.7108e+01, 6.4763e+01],
[ 7.0222e+01, 4.8029e+01, 8.1927e+01, 6.6728e+01],
[ 3.5311e+02, 6.8717e-01, 4.9533e+02, 9.8359e+01],
[ 2.7267e+02, 5.7682e+01, 2.8593e+02, 7.8934e+01],
[ 2.6448e+02, 6.4779e+01, 3.1326e+02, 1.1497e+02],
[ 4.9589e+02, 5.9461e+01, 5.0021e+02, 1.7103e+02],
[-2.6337e-01, 4.5570e+01, 1.5327e+01, 6.8504e+01],
[ 1.3369e+02, 7.4889e+01, 1.7413e+02, 9.9784e+01]])
) at 0x7f3ee433aad0>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9526, 0.9434, 0.2542, 0.0171, 0.0164, 0.0153, 0.0141, 0.0118])
labels: tensor([8, 7, 7, 8, 8, 7, 7, 8])
bboxes: tensor([[273.7454, 62.8087, 391.8797, 275.4726],
[ 56.6131, 137.2383, 309.5978, 286.9805],
[243.2493, 126.1492, 409.4851, 263.6946],
[153.1183, 138.8060, 159.9677, 151.2330],
[329.7613, 64.1554, 361.2544, 148.9306],
[364.7667, 176.9132, 410.6239, 215.0790],
[167.0702, 127.0471, 418.0861, 279.5936],
[327.6038, 63.3811, 375.5212, 182.9080]])
) at 0x7f3ee433a610>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9600, 0.8965, 0.0139, 0.0128, 0.0118])
labels: tensor([7, 8, 7, 7, 7])
bboxes: tensor([[168.2290, 113.8739, 404.0366, 263.4699],
[240.3175, 95.6240, 356.1669, 242.0714],
[ 4.6860, 279.6691, 279.2983, 385.9559],
[243.3743, 159.9499, 295.2976, 207.0423],
[189.6792, 325.7678, 277.8989, 375.0135]])
) at 0x7f3ed0e36f10>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9282, 0.1169, 0.0335, 0.0265, 0.0209, 0.0161, 0.0154, 0.0122, 0.0111])
labels: tensor([7, 8, 7, 7, 7, 8, 7, 7, 7])
bboxes: tensor([[ 65.7897, 97.3768, 381.8665, 301.4513],
[222.5854, 103.4998, 230.9302, 122.0861],
[173.3919, 152.6381, 218.9909, 184.4713],
[ 21.4184, 203.7533, 409.4409, 381.4030],
[208.2638, 186.7466, 309.7049, 261.6909],
[222.0536, 49.4321, 503.7277, 452.9117],
[ 43.3022, 175.4740, 63.6314, 191.7135],
[230.1973, 86.3625, 386.2090, 271.4500],
[188.7839, 133.8487, 253.4036, 181.7763]])
) at 0x7f3ee796c250>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9312, 0.8984, 0.8848, 0.8828, 0.8496, 0.8433, 0.8403, 0.8267, 0.7563,
0.7153, 0.6997, 0.6372, 0.6196, 0.5654, 0.5420, 0.5352, 0.4941, 0.4124,
0.3896, 0.3320, 0.3093, 0.2952, 0.2883, 0.2871, 0.2583, 0.2035, 0.1708,
0.1691, 0.1440, 0.1396, 0.1342, 0.1257, 0.0809, 0.0758, 0.0641, 0.0609,
0.0517, 0.0509, 0.0494, 0.0442, 0.0402, 0.0345, 0.0341, 0.0297, 0.0292,
0.0263, 0.0259, 0.0256, 0.0239, 0.0235, 0.0229, 0.0219, 0.0218, 0.0210,
0.0190, 0.0189, 0.0180, 0.0178, 0.0168, 0.0155, 0.0153, 0.0148, 0.0139,
0.0128, 0.0127, 0.0124, 0.0110, 0.0104])
labels: tensor([7, 7, 7, 8, 8, 3, 8, 8, 8, 7, 7, 8, 7, 7, 8, 7, 8, 7, 7, 8, 8, 7, 8, 7,
7, 7, 8, 8, 3, 8, 7, 8, 3, 8, 7, 7, 8, 8, 8, 8, 7, 8, 8, 8, 7, 8, 7, 7,
7, 7, 7, 8, 3, 7, 8, 7, 3, 7, 8, 8, 8, 8, 8, 7, 7, 3, 7, 7])
bboxes: tensor([[ 42.1663, 99.3477, 312.5212, 277.9064],
[ 0.5352, 55.6602, 59.4746, 105.5462],
[170.6105, 123.0403, 497.3582, 328.9620],
[117.7825, 40.8042, 292.3737, 225.7913],
[306.4886, 34.3128, 498.1989, 322.4490],
[296.9205, 18.5393, 394.0952, 72.1637],
[194.4128, 18.4194, 250.1185, 73.1131],
[272.4044, 20.7031, 299.0800, 96.7863],
[336.0901, 28.3117, 390.0818, 76.2968],
[122.3423, 53.2957, 163.0093, 107.1300],
[134.5225, 73.2941, 252.1963, 157.0007],
[435.9511, 22.9213, 451.5489, 36.2137],
[290.7020, 52.3369, 399.5324, 132.6796],
[ 59.4583, 54.6140, 95.4245, 99.9567],
[130.0523, 27.3868, 162.1352, 61.4133],
[ 92.1215, 49.4632, 127.8003, 101.6922],
[379.5861, 19.7893, 398.5390, 66.5713],
[291.5353, 38.3869, 490.1053, 142.3360],
[268.6132, 40.6600, 303.2619, 100.2492],
[451.4218, 25.7971, 463.4220, 38.2659],
[322.3743, 65.6900, 468.2507, 304.1478],
[334.3202, 50.3775, 396.1486, 98.7286],
[125.5087, 25.2893, 163.9444, 94.8349],
[ -0.5915, 44.3139, 504.4978, 330.2079],
[161.9870, 54.1434, 204.6146, 87.0586],
[288.5361, 74.3392, 496.2296, 324.1879],
[ 72.0308, 24.8707, 91.7387, 59.9773],
[436.3911, 21.5577, 451.8902, 61.7290],
[481.7053, 33.1701, 499.5447, 45.8230],
[273.6105, 20.8099, 300.9989, 53.5968],
[129.9732, 65.9688, 274.7144, 182.8667],
[394.3688, 33.0199, 498.5999, 325.6936],
[175.5467, 19.2355, 296.7189, 58.3914],
[376.8923, 19.2975, 400.4515, 93.3127],
[ 73.0463, 34.6426, 127.9303, 100.2165],
[292.9576, 60.2070, 363.2924, 132.2258],
[448.9414, 25.6731, 465.1211, 60.8827],
[163.2302, 29.3887, 193.6057, 70.6334],
[319.7281, 34.8265, 487.3032, 183.7584],
[163.4376, 37.8426, 504.5312, 336.2888],
[327.9939, 33.9939, 496.2249, 185.5669],
[403.1187, 35.8638, 486.7251, 127.9772],
[401.7171, 33.4986, 499.8454, 156.0067],
[163.4580, 34.4280, 205.8780, 76.8161],
[163.7186, 35.5413, 208.7424, 77.0689],
[188.1347, 44.7581, 290.7716, 145.1376],
[156.6527, 56.6615, 201.9411, 108.5457],
[366.9370, 30.5246, 500.2505, 331.3115],
[289.3442, 56.6106, 450.4996, 136.0173],
[324.6936, 48.3307, 394.8376, 114.0467],
[440.1209, 64.5564, 500.5041, 329.4819],
[417.1564, 114.4800, 500.0312, 332.4481],
[427.8382, 34.0693, 499.5055, 75.5159],
[422.6665, 115.2606, 499.9897, 333.2288],
[164.4417, 28.5181, 186.7301, 61.2091],
[453.9222, 111.1492, 500.7653, 329.9239],
[175.0922, 18.8977, 267.0953, 51.2641],
[ 40.2116, 52.0201, 75.4134, 98.2570],
[399.6084, 38.1551, 444.1416, 114.0737],
[163.0535, 45.0103, 204.3293, 84.4817],
[435.2440, 24.9276, 463.9748, 64.3605],
[451.5647, 25.5696, 467.1854, 40.8841],
[ 91.4061, 33.1843, 126.9533, 103.1386],
[294.4671, 32.3255, 493.8142, 245.1992],
[361.0066, 49.1593, 396.4153, 97.9952],
[481.9555, 32.7376, 500.0758, 51.7689],
[388.6934, 33.3856, 498.8067, 176.6120],
[ 38.1555, 52.1281, 74.0516, 83.9995]])
) at 0x7f3ee7a61110>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9067, 0.7593, 0.7407, 0.7021, 0.7021, 0.6509, 0.5425, 0.4441, 0.3022,
0.2227, 0.1725, 0.1693, 0.1407, 0.1155, 0.0975, 0.0951, 0.0775, 0.0689,
0.0602, 0.0454, 0.0439, 0.0418, 0.0410, 0.0390, 0.0374, 0.0356, 0.0346,
0.0308, 0.0297, 0.0265, 0.0258, 0.0257, 0.0255, 0.0254, 0.0235, 0.0222,
0.0215, 0.0214, 0.0197, 0.0193, 0.0184, 0.0180, 0.0177, 0.0167, 0.0164,
0.0154, 0.0147, 0.0146, 0.0144, 0.0144, 0.0133, 0.0113, 0.0110, 0.0106,
0.0106, 0.0104, 0.0102])
labels: tensor([7, 8, 8, 8, 7, 8, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 7, 8, 8,
8, 8, 8, 8, 8, 8, 8, 7, 8, 8, 8, 8, 8, 8, 8, 3, 8, 7, 7, 8, 8, 8, 8, 8,
8, 8, 8, 8, 7, 7, 8, 7, 8])
bboxes: tensor([[ 4.1983e+01, 2.6130e+01, 4.3458e+02, 3.5160e+02],
[ 3.1558e+02, 2.5677e-01, 3.6021e+02, 7.1228e+01],
[ 4.0353e+02, 9.4569e-01, 4.9881e+02, 3.7777e+02],
[ 8.7226e+01, -9.5624e-02, 2.0398e+02, 8.3348e+01],
[ 5.9931e+01, 1.2541e+02, 4.2444e+02, 3.4998e+02],
[ 2.9589e+02, 9.2185e-02, 3.2013e+02, 7.5640e+01],
[ 1.8113e+00, 4.0539e+00, 4.5991e+02, 1.8032e+02],
[ 2.4462e+00, 1.1596e+00, 2.9306e+02, 1.7950e+02],
[ 2.9496e+02, -2.1425e-02, 3.4059e+02, 7.5363e+01],
[ 8.8643e+01, 4.7193e-02, 1.3460e+02, 2.3634e+01],
[ 4.4904e+02, -2.8754e-01, 4.9706e+02, 2.7778e+01],
[ 4.2366e+02, -1.2599e+00, 4.9744e+02, 1.1991e+02],
[-3.4711e-01, 9.4712e-02, 2.8716e+01, 4.1971e+01],
[ 2.1067e+02, 1.1748e-01, 2.4948e+02, 1.1162e+01],
[ 2.5099e+02, -7.1129e-02, 2.9237e+02, 7.4290e+01],
[ 2.0931e+02, 1.0396e-01, 2.6334e+02, 6.9281e+01],
[ 2.8394e+02, 5.6250e-01, 2.9575e+02, 3.8402e+01],
[ 2.4477e+01, -4.5836e-01, 2.4427e+02, 1.8229e+02],
[ 2.5427e+02, -8.7392e-02, 3.1995e+02, 7.7236e+01],
[ 4.3871e+02, 3.6997e+00, 4.9957e+02, 3.1427e+02],
[ 4.2446e+02, 8.8704e-02, 4.6617e+02, 1.1915e+02],
[-8.7855e-01, 2.1717e+00, 4.9541e+02, 1.0349e+02],
[ 2.2611e+02, -1.7437e-01, 2.8522e+02, 7.1268e+01],
[-9.4012e-01, 2.9906e-01, 6.5198e+01, 4.0179e+01],
[-1.0145e-01, -1.0079e+00, 2.7201e+01, 9.4660e+01],
[-3.7624e-01, -3.9503e-01, 4.0220e+01, 3.7187e+01],
[ 2.1643e+02, -1.7022e-01, 2.5427e+02, 9.2950e+00],
[ 6.9228e-01, 5.4580e-01, 9.5987e+01, 9.5841e+01],
[ 6.6397e+01, 2.0858e-01, 1.3165e+02, 2.6989e+01],
[-1.9445e+00, 2.3147e-01, 9.9894e+01, 3.9417e+01],
[ 3.6784e+02, -1.1191e-02, 4.4076e+02, 1.7479e+01],
[-8.4253e-01, 4.1618e-01, 9.5764e+01, 3.9525e+01],
[ 4.0830e-01, 6.9750e-01, 2.3835e+01, 5.9996e+01],
[ 5.6368e+01, 1.3004e+00, 2.2293e+02, 9.5282e+01],
[ 2.6979e+02, 1.9488e+00, 5.0560e+02, 3.7657e+02],
[ 4.5994e+02, 1.3848e-01, 4.9943e+02, 7.7010e+01],
[ 8.0538e+01, -2.6877e-01, 1.7454e+02, 2.5733e+01],
[-5.2306e-02, -2.5321e+00, 5.5521e+01, 9.4622e+01],
[ 4.4381e+02, 1.2242e+00, 4.9994e+02, 1.6333e+02],
[-4.6668e-01, -3.3558e-01, 2.5662e+01, 4.2450e+01],
[ 8.0389e+01, -1.1820e+00, 2.1141e+02, 2.8745e+01],
[-2.1565e+00, 3.9288e-01, 2.2462e+02, 1.0361e+02],
[ 1.9015e+01, -1.1009e-01, 1.3831e+02, 2.8699e+01],
[ 4.3490e-01, 1.7543e-01, 2.2636e+01, 3.4029e+01],
[ 1.4312e-01, 2.6076e+00, 9.8422e+00, 7.7861e+01],
[ 8.0777e+01, -3.4921e+00, 2.0555e+02, 1.7420e+02],
[ 4.2075e+01, -2.9984e-02, 1.1388e+02, 2.7252e+01],
[ 3.7132e+02, 4.8638e-01, 4.3962e+02, 4.7707e+01],
[ 7.1799e-02, -3.6557e-01, 1.1165e+01, 4.3188e+01],
[ 1.2882e-01, 6.7884e-01, 2.0147e+01, 8.0474e+01],
[ 7.3508e+01, 3.3287e+00, 2.5716e+02, 7.9288e+01],
[ 2.9094e+02, 9.0368e-01, 5.3093e+02, 2.8757e+02],
[ 2.9163e+02, 2.1386e-01, 5.0095e+02, 9.8321e+01],
[-1.4827e-01, -7.3805e-01, 2.6418e+01, 1.0230e+02],
[-4.1297e-02, 9.8677e-01, 1.0344e+01, 3.1630e+01],
[ 1.2278e-02, -5.9870e-01, 1.0748e+01, 4.5179e+01],
[ 2.0121e+02, 2.2101e-01, 2.3785e+02, 1.3426e+01]])
) at 0x7f3ee6d78f10>, <InstanceData(
META INFORMATION
DATA FIELDS
scores: tensor([0.9160, 0.8369, 0.0663, 0.0124])
labels: tensor([7, 8, 8, 7])
bboxes: tensor([[ 10.1531, 144.9379, 373.2454, 496.8590],
[109.7389, 75.1407, 293.3861, 463.9218],
[108.1947, 82.8591, 288.6803, 315.9690],
[ 6.3213, 251.7752, 239.3818, 442.3655]])
) at 0x7f3ee4339a10>]
Using default root folder: ./tiny_motorbike_coco/tiny_motorbike/Annotations/... Specify `model.mmdet_image.coco_root=...` in hyperparameters if you think it is wrong.
/home/ci/opt/venv/lib/python3.11/site-packages/mmdet/models/backbones/csp_darknet.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(enabled=False):
A new predictor save path is created. This is to prevent you to overwrite previous predictor saved here. You could check current save path at predictor._save_path. If you still want to use this path, set resume=True
No path specified. Models will be saved in: "AutogluonModels/ag-20250118_203210"
Saved detection results to /home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/AutogluonModels/ag-20250118_203210/result.txt
The output pred is a pandas DataFrame that has two columns, image and bboxes.
In image, each row contains the image path
In bboxes, each row is a list of dictionaries, each one representing a bounding box: {"class": <predicted_class_name>, "bbox": [x1, y1, x2, y2], "score": <confidence_score>}
Note that, by default, the predictor.predict does not save the detection results into a file.
To run inference and save results, run the following:
pred = predictor.predict(test_path, save_results=True)
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
saving file at /home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/AutogluonModels/ag-20250118_203212-001/result.json
Using default root folder: ./tiny_motorbike_coco/tiny_motorbike/Annotations/... Specify `model.mmdet_image.coco_root=...` in hyperparameters if you think it is wrong.
/home/ci/opt/venv/lib/python3.11/site-packages/mmdet/models/backbones/csp_darknet.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(enabled=False):
A new predictor save path is created. This is to prevent you to overwrite previous predictor saved here. You could check current save path at predictor._save_path. If you still want to use this path, set resume=True
No path specified. Models will be saved in: "AutogluonModels/ag-20250118_203212"
Saved detection results to /home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/AutogluonModels/ag-20250118_203212/result.txt
A new predictor save path is created. This is to prevent you to overwrite previous predictor saved here. You could check current save path at predictor._save_path. If you still want to use this path, set resume=True
No path specified. Models will be saved in: "AutogluonModels/ag-20250118_203212-001"
Using default root folder: ./tiny_motorbike_coco/tiny_motorbike/Annotations/... Specify `model.mmdet_image.coco_root=...` in hyperparameters if you think it is wrong.
--- Logging error ---
Traceback (most recent call last):
File "/opt/conda/lib/python3.11/logging/__init__.py", line 1110, in emit
msg = self.format(record)
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/logging/__init__.py", line 953, in format
return fmt.format(record)
^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/logging/__init__.py", line 687, in format
record.message = record.getMessage()
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/logging/__init__.py", line 377, in getMessage
msg = msg % self.args
~~~~^~~~~~~~~~~
TypeError: not all arguments converted during string formatting
Call stack:
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "/home/ci/opt/venv/lib/python3.11/site-packages/ipykernel_launcher.py", line 18, in <module>
app.launch_new_instance()
File "/home/ci/opt/venv/lib/python3.11/site-packages/traitlets/config/application.py", line 1075, in launch_instance
app.start()
File "/home/ci/opt/venv/lib/python3.11/site-packages/ipykernel/kernelapp.py", line 739, in start
self.io_loop.start()
File "/home/ci/opt/venv/lib/python3.11/site-packages/tornado/platform/asyncio.py", line 205, in start
self.asyncio_loop.run_forever()
File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 608, in run_forever
self._run_once()
File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 1936, in _run_once
handle._run()
File "/opt/conda/lib/python3.11/asyncio/events.py", line 84, in _run
self._context.run(self._callback, *self._args)
File "/home/ci/opt/venv/lib/python3.11/site-packages/ipykernel/kernelbase.py", line 545, in dispatch_queue
await self.process_one()
File "/home/ci/opt/venv/lib/python3.11/site-packages/ipykernel/kernelbase.py", line 534, in process_one
await dispatch(*args)
File "/home/ci/opt/venv/lib/python3.11/site-packages/ipykernel/kernelbase.py", line 437, in dispatch_shell
await result
File "/home/ci/opt/venv/lib/python3.11/site-packages/ipykernel/ipkernel.py", line 362, in execute_request
await super().execute_request(stream, ident, parent)
File "/home/ci/opt/venv/lib/python3.11/site-packages/ipykernel/kernelbase.py", line 778, in execute_request
reply_content = await reply_content
File "/home/ci/opt/venv/lib/python3.11/site-packages/ipykernel/ipkernel.py", line 449, in do_execute
res = shell.run_cell(
File "/home/ci/opt/venv/lib/python3.11/site-packages/ipykernel/zmqshell.py", line 549, in run_cell
return super().run_cell(*args, **kwargs)
File "/home/ci/opt/venv/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3009, in run_cell
result = self._run_cell(
File "/home/ci/opt/venv/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3064, in _run_cell
result = runner(coro)
File "/home/ci/opt/venv/lib/python3.11/site-packages/IPython/core/async_helpers.py", line 129, in _pseudo_sync_runner
coro.send(None)
File "/home/ci/opt/venv/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3269, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "/home/ci/opt/venv/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3448, in run_ast_nodes
if await self.run_code(code, result, async_=asy):
File "/home/ci/opt/venv/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3508, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "/tmp/ipykernel_4465/4018775541.py", line 1, in <module>
pred = predictor.predict(test_path, save_results=True)
File "/home/ci/autogluon/multimodal/src/autogluon/multimodal/predictor.py", line 640, in predict
return self._learner.predict(
File "/home/ci/autogluon/multimodal/src/autogluon/multimodal/learners/object_detection.py", line 755, in predict
save_result_coco_format(
File "/home/ci/autogluon/multimodal/src/autogluon/multimodal/utils/object_detection.py", line 1610, in save_result_coco_format
logger.info(25, f"Saved detection result to {result_path}")
Message: 25
Arguments: ('Saved detection result to /home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/AutogluonModels/ag-20250118_203212-001/result.json',)
Saved detection results as coco to /home/ci/autogluon/docs/tutorials/multimodal/object_detection/quick_start/AutogluonModels/ag-20250118_203212-001/result.json
Here, we save pred into a .txt file, which exactly follows the same layout as in pred.
You can use a predictor initialized in any way (i.e. finetuned predictor, predictor with pretrained model, etc.).
Visualizing Results¶
To run visualizations, ensure that you have opencv installed. If you haven’t already, install opencv by running
!pip install opencv-python
Requirement already satisfied: opencv-python in /home/ci/opt/venv/lib/python3.11/site-packages (4.11.0.86)
Requirement already satisfied: numpy>=1.21.2 in /home/ci/opt/venv/lib/python3.11/site-packages (from opencv-python) (1.26.4)
To visualize the detection bounding boxes, run the following:
from autogluon.multimodal.utils import ObjectDetectionVisualizer
conf_threshold = 0.4 # Specify a confidence threshold to filter out unwanted boxes
image_result = pred.iloc[30]
img_path = image_result.image # Select an image to visualize
visualizer = ObjectDetectionVisualizer(img_path) # Initialize the Visualizer
out = visualizer.draw_instance_predictions(image_result, conf_threshold=conf_threshold) # Draw detections
visualized = out.get_image() # Get the visualized image
from PIL import Image
from IPython.display import display
img = Image.fromarray(visualized, 'RGB')
display(img)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[17], line 4
1 from autogluon.multimodal.utils import ObjectDetectionVisualizer
3 conf_threshold = 0.4 # Specify a confidence threshold to filter out unwanted boxes
----> 4 image_result = pred.iloc[30]
6 img_path = image_result.image # Select an image to visualize
8 visualizer = ObjectDetectionVisualizer(img_path) # Initialize the Visualizer
AttributeError: 'list' object has no attribute 'iloc'
Testing on Your Own Data¶
You can also predict on your own images with various input format. The follow is an example:
Download the example image:
from autogluon.multimodal import download
image_url = "https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/detection/street_small.jpg"
test_image = download(image_url)
Run inference on data in a json file of COCO format (See Convert Data to COCO Format for more details about COCO format). Note that since the root is by default the parent folder of the annotation file, here we put the annotation file in a folder:
import json
# create a input file for demo
data = {"images": [{"id": 0, "width": -1, "height": -1, "file_name": test_image}], "categories": []}
os.mkdir("input_data_for_demo")
input_file = "input_data_for_demo/demo_annotation.json"
with open(input_file, "w+") as f:
json.dump(data, f)
pred_test_image = predictor.predict(input_file)
print(pred_test_image)
Run inference on data in a list of image file names:
pred_test_image = predictor.predict([test_image])
print(pred_test_image)
Other Examples¶
You may go to AutoMM Examples to explore other examples about AutoMM.
Customization¶
To learn how to customize AutoMM, please refer to Customize AutoMM.
Citation¶
@article{DBLP:journals/corr/abs-2107-08430,
author = {Zheng Ge and
Songtao Liu and
Feng Wang and
Zeming Li and
Jian Sun},
title = {{YOLOX:} Exceeding {YOLO} Series in 2021},
journal = {CoRR},
volume = {abs/2107.08430},
year = {2021},
url = {https://arxiv.org/abs/2107.08430},
eprinttype = {arXiv},
eprint = {2107.08430},
timestamp = {Tue, 05 Apr 2022 14:09:44 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2107-08430.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
}