.. _sec_automm_detection_fast_ft_coco:

AutoMM Detection - Fast Finetune on COCO Format Dataset
=======================================================


.. figure:: https://automl-mm-bench.s3.amazonaws.com/object_detection/example_image/pothole144_gt.jpg
   :width: 500px

   Pothole Dataset


In this section, our goal is to fast finetune and evaluate a pretrained
model on `Pothole
dataset <https://www.kaggle.com/datasets/andrewmvd/pothole-detection>`__
in COCO format. Pothole is a single object, i.e. \ ``pothole``,
detection dataset, containing 665 images with bounding box annotations
for the creation of detection models and can work as POC/POV for the
maintenance of roads. See :ref:`sec_automm_detection_prepare_voc` for
how to prepare Pothole dataset.

To start, let’s import MultiModalPredictor:

.. code:: python

    from autogluon.multimodal import MultiModalPredictor


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    The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.


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    0it [00:00, ?it/s]


Make sure ``mmcv-full`` and ``mmdet`` are installed:

.. code:: python

    !mim install mmcv-full
    !pip install mmdet


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And also import some other packages that will be used in this tutorial:

.. code:: python

    import os
    import time
    
    from autogluon.core.utils.loaders import load_zip

We have the sample dataset ready in the cloud. Let’s download it:

.. code:: python

    zip_file = "https://automl-mm-bench.s3.amazonaws.com/object_detection/dataset/pothole.zip"
    download_dir = "./pothole"
    
    load_zip.unzip(zip_file, unzip_dir=download_dir)
    data_dir = os.path.join(download_dir, "pothole")
    train_path = os.path.join(data_dir, "Annotations", "usersplit_train_cocoformat.json")
    val_path = os.path.join(data_dir, "Annotations", "usersplit_val_cocoformat.json")
    test_path = os.path.join(data_dir, "Annotations", "usersplit_test_cocoformat.json")


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    Downloading ./pothole/file.zip from https://automl-mm-bench.s3.amazonaws.com/object_detection/dataset/pothole.zip...


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    100%|██████████| 351M/351M [00:06<00:00, 51.3MiB/s]


While using COCO format dataset, the input is the json annotation file
of the dataset split. In this example,
``usersplit_train_cocoformat.json`` is the annotation file of the train
split. ``usersplit_val_cocoformat.json`` is the annotation file of the
validation split. And ``usersplit_test_cocoformat.json`` is the
annotation file of the test split.

We select the YOLOv3 with MobileNetV2 as backbone, and input resolution
is 320x320, pretrained on COCO dataset. With this setting, it is fast to
finetune or inference, and easy to deploy. And we use all the GPUs (if
any):

.. code:: python

    checkpoint_name = "yolov3_mobilenetv2_320_300e_coco"
    num_gpus = -1  # use all GPUs

We create the MultiModalPredictor with selected checkpoint name and
number of GPUs. 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.

.. code:: python

    predictor = MultiModalPredictor(
        hyperparameters={
            "model.mmdet_image.checkpoint_name": checkpoint_name,
            "env.num_gpus": num_gpus,
        },
        problem_type="object_detection",
        sample_data_path=train_path,
    )


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    processing yolov3_mobilenetv2_320_300e_coco...



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    Output()



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    Successfully downloaded yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth to /home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune
    Successfully dumped yolov3_mobilenetv2_320_300e_coco.py to /home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune
    processing yolov3_mobilenetv2_320_300e_coco...
    yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth exists in /home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune
    Successfully dumped yolov3_mobilenetv2_320_300e_coco.py to /home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune
    load checkpoint from local path: yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth
    The model and loaded state dict do not match exactly
    
    size mismatch for bbox_head.convs_pred.0.weight: copying a param with shape torch.Size([255, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([18, 96, 1, 1]).
    size mismatch for bbox_head.convs_pred.0.bias: copying a param with shape torch.Size([255]) from checkpoint, the shape in current model is torch.Size([18]).
    size mismatch for bbox_head.convs_pred.1.weight: copying a param with shape torch.Size([255, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([18, 96, 1, 1]).
    size mismatch for bbox_head.convs_pred.1.bias: copying a param with shape torch.Size([255]) from checkpoint, the shape in current model is torch.Size([18]).
    size mismatch for bbox_head.convs_pred.2.weight: copying a param with shape torch.Size([255, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([18, 96, 1, 1]).
    size mismatch for bbox_head.convs_pred.2.bias: copying a param with shape torch.Size([255]) from checkpoint, the shape in current model is torch.Size([18]).


We set the learning rate to be ``2e-4``. 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 set the
epoch to be 30 for fast finetuning and batch_size to be 32. We also
compute the time of the fit process here for better understanding the
speed.

.. code:: python

    import time
    start = time.time()
    predictor.fit(
        train_path,
        hyperparameters={
            "optimization.learning_rate": 2e-4, # we use two stage and detection head has 100x lr
            "optimization.max_epochs": 30,
            "env.per_gpu_batch_size": 32,  # decrease it when model is large
        },
    )
    end = time.time()


.. parsed-literal::
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    loading annotations into memory...
    Done (t=0.00s)
    creating index...
    index created!


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    :class: output

    Global seed set to 123
    GPU available: True (cuda), used: True
    TPU available: False, using: 0 TPU cores
    IPU available: False, using: 0 IPUs
    HPU available: False, using: 0 HPUs
    LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
    
      | Name              | Type                             | Params
    -----------------------------------------------------------------------
    0 | model             | MMDetAutoModelForObjectDetection | 3.7 M 
    1 | validation_metric | MeanMetric                       | 0     
    -----------------------------------------------------------------------
    3.7 M     Trainable params
    0         Non-trainable params
    3.7 M     Total params
    14.675    Total estimated model params size (MB)
    Epoch 0, global step 1: 'val_direct_loss' reached 52603.91797 (best 52603.91797), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune/AutogluonModels/ag-20230206_231255/epoch=0-step=1.ckpt' as top 1
    /home/ci/opt/venv/lib/python3.8/site-packages/pytorch_lightning/utilities/cloud_io.py:33: LightningDeprecationWarning: `pytorch_lightning.utilities.cloud_io.get_filesystem` has been deprecated in v1.8.0 and will be removed in v1.10.0. Please use `lightning_lite.utilities.cloud_io.get_filesystem` instead.
      rank_zero_deprecation(
    /home/ci/opt/venv/lib/python3.8/site-packages/pytorch_lightning/utilities/cloud_io.py:25: LightningDeprecationWarning: `pytorch_lightning.utilities.cloud_io.atomic_save` has been deprecated in v1.8.0 and will be removed in v1.10.0. This function is internal but you can copy over its implementation.
      rank_zero_deprecation(
    Epoch 0, global step 3: 'val_direct_loss' reached 7221.30127 (best 7221.30127), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune/AutogluonModels/ag-20230206_231255/epoch=0-step=3.ckpt' as top 1
    Epoch 1, global step 4: 'val_direct_loss' reached 2628.90527 (best 2628.90527), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune/AutogluonModels/ag-20230206_231255/epoch=1-step=4.ckpt' as top 1
    Epoch 1, global step 6: 'val_direct_loss' reached 911.72858 (best 911.72858), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune/AutogluonModels/ag-20230206_231255/epoch=1-step=6.ckpt' as top 1
    Epoch 2, global step 7: 'val_direct_loss' reached 763.92450 (best 763.92450), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune/AutogluonModels/ag-20230206_231255/epoch=2-step=7.ckpt' as top 1
    Epoch 2, global step 9: 'val_direct_loss' reached 714.85297 (best 714.85297), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune/AutogluonModels/ag-20230206_231255/epoch=2-step=9.ckpt' as top 1
    Epoch 3, global step 10: 'val_direct_loss' was not in top 1
    Epoch 3, global step 12: 'val_direct_loss' reached 681.82672 (best 681.82672), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune/AutogluonModels/ag-20230206_231255/epoch=3-step=12.ckpt' as top 1
    Epoch 4, global step 13: 'val_direct_loss' reached 596.27637 (best 596.27637), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune/AutogluonModels/ag-20230206_231255/epoch=4-step=13.ckpt' as top 1
    Epoch 4, global step 15: 'val_direct_loss' was not in top 1
    Epoch 5, global step 16: 'val_direct_loss' reached 591.26923 (best 591.26923), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune/AutogluonModels/ag-20230206_231255/epoch=5-step=16.ckpt' as top 1
    Epoch 5, global step 18: 'val_direct_loss' reached 563.00592 (best 563.00592), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune/AutogluonModels/ag-20230206_231255/epoch=5-step=18.ckpt' as top 1
    Epoch 6, global step 19: 'val_direct_loss' was not in top 1
    Epoch 6, global step 21: 'val_direct_loss' was not in top 1
    Epoch 7, global step 22: 'val_direct_loss' reached 545.98834 (best 545.98834), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune/AutogluonModels/ag-20230206_231255/epoch=7-step=22.ckpt' as top 1
    Epoch 7, global step 24: 'val_direct_loss' was not in top 1
    Epoch 8, global step 25: 'val_direct_loss' reached 474.08963 (best 474.08963), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune/AutogluonModels/ag-20230206_231255/epoch=8-step=25.ckpt' as top 1
    Epoch 8, global step 27: 'val_direct_loss' was not in top 1
    Epoch 9, global step 28: 'val_direct_loss' was not in top 1
    Epoch 9, global step 30: 'val_direct_loss' was not in top 1
    Epoch 10, global step 31: 'val_direct_loss' was not in top 1
    Epoch 10, global step 33: 'val_direct_loss' was not in top 1
    Epoch 11, global step 34: 'val_direct_loss' was not in top 1
    Epoch 11, global step 36: 'val_direct_loss' reached 465.77646 (best 465.77646), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune/AutogluonModels/ag-20230206_231255/epoch=11-step=36.ckpt' as top 1
    Epoch 12, global step 37: 'val_direct_loss' reached 413.23276 (best 413.23276), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune/AutogluonModels/ag-20230206_231255/epoch=12-step=37.ckpt' as top 1
    Epoch 12, global step 39: 'val_direct_loss' was not in top 1
    Epoch 13, global step 40: 'val_direct_loss' was not in top 1
    Epoch 13, global step 42: 'val_direct_loss' was not in top 1
    Epoch 14, global step 43: 'val_direct_loss' was not in top 1
    Epoch 14, global step 45: 'val_direct_loss' was not in top 1
    Epoch 15, global step 46: 'val_direct_loss' was not in top 1
    Epoch 15, global step 48: 'val_direct_loss' was not in top 1
    Epoch 16, global step 49: 'val_direct_loss' was not in top 1
    Epoch 16, global step 51: 'val_direct_loss' was not in top 1
    Epoch 17, global step 52: 'val_direct_loss' was not in top 1


Print out the time and we can see that it’s fast!

.. code:: python

    print("This finetuning takes %.2f seconds." % (end - start))


.. parsed-literal::
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    This finetuning takes 145.01 seconds.


To evaluate the model we just trained, run:

.. code:: python

    predictor.evaluate(test_path)


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    Global seed set to 123


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    loading annotations into memory...
    Done (t=0.00s)
    creating index...
    index created!


.. parsed-literal::
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    WARNING:automm: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


.. parsed-literal::
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    saving file at /home/ci/autogluon/docs/_build/eval/tutorials/multimodal/object_detection/finetune/AutogluonModels/ag-20230206_231522/object_detection_result_cache.json
    loading annotations into memory...
    Done (t=0.00s)
    creating index...
    index created!
    Loading and preparing results...
    DONE (t=0.02s)
    creating index...
    index created!
    Running per image evaluation...
    Evaluate annotation type *bbox*
    DONE (t=0.42s).
    Accumulating evaluation results...
    DONE (t=0.05s).
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.162
     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.426
     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.088
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.031
     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.159
     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.288
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.140
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.280
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.326
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.196
     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.327
     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.431




.. parsed-literal::
    :class: output

    {'map': 0.16155008768665033}



And the evaluation results are shown in command line output. The first
value is mAP in COCO standard, and the second one is mAP in VOC standard
(or mAP50). For more details about these metrics, see `COCO’s evaluation
guideline <https://cocodataset.org/#detection-eval>`__.

We can get the prediction on test set:

.. code:: python

    pred = predictor.predict(test_path)


.. parsed-literal::
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    Global seed set to 123


.. parsed-literal::
    :class: output

    loading annotations into memory...
    Done (t=0.00s)
    creating index...
    index created!


Let’s also visualize the prediction result:

.. code:: python

    !pip install opencv-python


.. parsed-literal::
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    Requirement already satisfied: opencv-python in /home/ci/opt/venv/lib/python3.8/site-packages (4.7.0.68)
    Requirement already satisfied: numpy>=1.17.3 in /home/ci/opt/venv/lib/python3.8/site-packages (from opencv-python) (1.22.4)


.. code:: python

    from autogluon.multimodal.utils import visualize_detection
    conf_threshold = 0.25  # Specify a confidence threshold to filter out unwanted boxes
    visualization_result_dir = "./"  # Use the pwd as result dir to save the visualized image
    visualized = visualize_detection(
        pred=pred[12:13],
        detection_classes=predictor.get_predictor_classes(),
        conf_threshold=conf_threshold,
        visualization_result_dir=visualization_result_dir,
    )
    from PIL import Image
    from IPython.display import display
    img = Image.fromarray(visualized[0][:, :, ::-1], 'RGB')
    display(img)



.. figure:: output_detection_fast_finetune_coco_631583_22_0.png


Under this fast finetune setting, we reached a good mAP number on a new
dataset with a few hundred seconds! For how to finetune with higher
performance, see :ref:`sec_automm_detection_high_ft_coco`, where we
finetuned a VFNet model with 5 hours and reached
``mAP = 0.450, mAP50 = 0.718`` on this dataset.

Other Examples
~~~~~~~~~~~~~~

You may go to `AutoMM
Examples <https://github.com/autogluon/autogluon/tree/master/examples/automm>`__
to explore other examples about AutoMM.

Customization
~~~~~~~~~~~~~

To learn how to customize AutoMM, please refer to
:ref:`sec_automm_customization`.

Citation
~~~~~~~~

::

   @misc{redmon2018yolov3,
       title={YOLOv3: An Incremental Improvement},
       author={Joseph Redmon and Ali Farhadi},
       year={2018},
       eprint={1804.02767},
       archivePrefix={arXiv},
       primaryClass={cs.CV}
   }