.. _sec_object_detection_quick: Object Detection - Quick Start ============================== Object detection is the process of identifying and localizing objects in an image and is an important task in computer vision. Follow this tutorial to learn how to use AutoGluon for object detection. **Tip**: If you are new to AutoGluon, review :ref:`sec_imgquick` first to learn the basics of the AutoGluon API. Our goal is to detect motorbike in images by `YOLOv3 model `__. A tiny dataset is collected from VOC dataset, which only contains the motorbike category. The model pretrained on the COCO dataset is used to fine-tune our small dataset. With the help of AutoGluon, we are able to try many models with different hyperparameters automatically, and return the best one as our final model. To start, import autogluon.vision and ObjectDetector: .. code:: python import autogluon.core as ag from autogluon.vision import ObjectDetector .. parsed-literal:: :class: output /var/lib/jenkins/workspace/workspace/autogluon-tutorial-object-detection-v3/venv/lib/python3.7/site-packages/gluoncv/__init__.py:40: UserWarning: Both `mxnet==1.7.0` and `torch==1.9.0+cu102` are installed. You might encounter increased GPU memory footprint if both framework are used at the same time. warnings.warn(f'Both `mxnet=={mx.__version__}` and `torch=={torch.__version__}` are installed. ' Tiny\_motorbike Dataset ----------------------- We collect a toy dataset for detecting motorbikes in images. From the VOC dataset, images are randomly selected for training, validation, and testing - 120 images for training, 50 images for validation, and 50 for testing. This tiny dataset follows the same format as VOC. Using the commands below, we can download this dataset, which is only 23M. The name of unzipped folder is called ``tiny_motorbike``. Anyway, the task dataset helper can perform the download and extraction automatically, and load the dataset according to the detection formats. .. code:: python url = 'https://autogluon.s3.amazonaws.com/datasets/tiny_motorbike.zip' dataset_train = ObjectDetector.Dataset.from_voc(url, splits='trainval') .. parsed-literal:: :class: output tiny_motorbike/ ├── Annotations/ ├── ImageSets/ └── JPEGImages/ Fit Models by AutoGluon ----------------------- In this section, we demonstrate how to apply AutoGluon to fit our detection models. We use mobilenet as the backbone for the YOLOv3 model. Two different learning rates are used to fine-tune the network. The best model is the one that obtains the best performance on the validation dataset. You can also try using more networks and hyperparameters to create a larger searching space. We ``fit`` a classifier using AutoGluon as follows. In each experiment (one trial in our searching space), we train the model for 5 epochs to avoid bursting our tutorial runtime. .. code:: python time_limit = 60*30 # at most 0.5 hour detector = ObjectDetector() hyperparameters = {'epochs': 5, 'batch_size': 8} hyperparamter_tune_kwargs={'num_trials': 2} detector.fit(dataset_train, time_limit=time_limit, hyperparameters=hyperparameters, hyperparamter_tune_kwargs=hyperparamter_tune_kwargs) .. parsed-literal:: :class: output The number of requested GPUs is greater than the number of available GPUs.Reduce the number to 1 Randomly split train_data into train[155]/validation[15] splits. Starting fit without HPO modified configs( != ): { root.dataset_root ~/.mxnet/datasets/ != auto root.dataset voc_tiny != auto root.valid.batch_size 16 != 8 root.train.seed 233 != 276 root.train.batch_size 16 != 8 root.train.early_stop_baseline 0.0 != -inf root.train.early_stop_max_value 1.0 != inf root.train.epochs 20 != 5 root.train.early_stop_patience -1 != 10 root.num_workers 4 != 8 root.ssd.data_shape 300 != 512 root.ssd.base_network vgg16_atrous != resnet50_v1 root.gpus (0, 1, 2, 3) != (0,) } Saved config to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-object-detection-v3/docs/_build/eval/tutorials/object_detection/0051deab/.trial_0/config.yaml Using transfer learning from ssd_512_resnet50_v1_coco, the other network parameters are ignored. Start training from [Epoch 0] [Epoch 0] Training cost: 9.380765, CrossEntropy=3.792875, SmoothL1=1.062698 [Epoch 0] Validation: cow=nan dog=nan car=1.0000000000000002 pottedplant=nan bicycle=0.0 bus=1.0000000000000002 motorbike=0.8373577402989169 chair=nan boat=nan person=0.8893984228640086 mAP=0.7453512326325852 [Epoch 0] Current best map: 0.745351 vs previous 0.000000, saved to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-object-detection-v3/docs/_build/eval/tutorials/object_detection/0051deab/.trial_0/best_checkpoint.pkl [Epoch 1] Training cost: 8.126496, CrossEntropy=2.456822, SmoothL1=1.042939 [Epoch 1] Validation: cow=nan dog=nan car=1.0000000000000002 pottedplant=nan bicycle=0.0 bus=1.0000000000000002 motorbike=0.8311355311355311 chair=nan boat=nan person=0.8263806900170537 mAP=0.731503244230517 [Epoch 2] Training cost: 8.666463, CrossEntropy=2.298794, SmoothL1=0.996099 [Epoch 2] Validation: cow=nan dog=nan car=0.9090909090909091 pottedplant=nan bicycle=0.0 bus=1.0000000000000002 motorbike=0.8256188256188255 chair=nan boat=nan person=0.7999423097563595 mAP=0.7069304088932189 [Epoch 3] Training cost: 7.961231, CrossEntropy=2.258920, SmoothL1=0.972762 [Epoch 3] Validation: cow=nan dog=nan car=0.9454545454545457 pottedplant=nan bicycle=0.0 bus=1.0000000000000002 motorbike=0.9090909090909093 chair=nan boat=nan person=0.8696033872288463 mAP=0.7448297683548604 [Epoch 4] Training cost: 8.101715, CrossEntropy=2.211277, SmoothL1=0.960873 [Epoch 4] Validation: cow=nan dog=nan car=0.8636363636363636 pottedplant=nan bicycle=0.0 bus=1.0000000000000002 motorbike=0.8430735930735931 chair=nan boat=nan person=0.9142228739002934 mAP=0.7241865661220501 Applying the state from the best checkpoint... Finished, total runtime is 64.07 s { 'best_config': { 'batch_size': 8, 'dist_ip_addrs': None, 'early_stop_baseline': -inf, 'early_stop_max_value': inf, 'early_stop_patience': 10, 'epochs': 5, 'final_fit': False, 'gpus': [0], 'log_dir': '/var/lib/jenkins/workspace/workspace/autogluon-tutorial-object-detection-v3/docs/_build/eval/tutorials/object_detection/0051deab', 'lr': 0.001, 'ngpus_per_trial': 8, 'nthreads_per_trial': 128, 'num_trials': 1, 'num_workers': 8, 'scheduler': 'local', 'search_strategy': 'random', 'seed': 276, 'time_limits': 1800, 'transfer': 'ssd_512_resnet50_v1_coco', 'wall_clock_tick': 1624558116.5470297}, 'total_time': 47.2263126373291, 'train_map': 0.7909779148259738, 'valid_map': 0.7453512326325852} .. parsed-literal:: :class: output Note that ``num_trials=2`` above is only used to speed up the tutorial. In normal practice, it is common to only use ``time_limit`` and drop ``num_trials``. Also note that hyperparameter tuning defaults to random search. Model-based variants, such as ``search_strategy='bayesopt'`` or ``search_strategy='bayesopt_hyperband'`` can be a lot more sample-efficient. After fitting, AutoGluon automatically returns the best model among all models in the searching space. From the output, we know the best model is the one trained with the second learning rate. To see how well the returned model performed on test dataset, call detector.evaluate(). .. code:: python dataset_test = ObjectDetector.Dataset.from_voc(url, splits='test') test_map = detector.evaluate(dataset_test) print("mAP on test dataset: {}".format(test_map[1][-1])) .. parsed-literal:: :class: output tiny_motorbike/ ├── Annotations/ ├── ImageSets/ └── JPEGImages/ mAP on test dataset: 0.03392521561126212 Below, we randomly select an image from test dataset and show the predicted class, box and probability over the origin image, stored in ``predict_class``, ``predict_rois`` and ``predict_score`` columns, respectively. You can interpret ``predict_rois`` as a dict of (``xmin``, ``ymin``, ``xmax``, ``ymax``) proportional to original image size. .. code:: python image_path = dataset_test.iloc[0]['image'] result = detector.predict(image_path) print(result) .. parsed-literal:: :class: output predict_class predict_score \ 0 person 0.966625 1 motorbike 0.897586 2 person 0.236946 3 person 0.161338 4 person 0.142619 .. ... ... 88 person 0.024545 89 chair 0.024537 90 person 0.024441 91 person 0.024277 92 person 0.024225 predict_rois 0 {'xmin': 0.3883911073207855, 'ymin': 0.2853710... 1 {'xmin': 0.32461124658584595, 'ymin': 0.430736... 2 {'xmin': 0.9941485524177551, 'ymin': 0.5980668... 3 {'xmin': 0.3308553695678711, 'ymin': 0.4322595... 4 {'xmin': 0.9954673051834106, 'ymin': 0.8241726... .. ... 88 {'xmin': 0.9907503724098206, 'ymin': 0.6771276... 89 {'xmin': 0.30692780017852783, 'ymin': 0.446932... 90 {'xmin': 0.8992050290107727, 'ymin': 0.0119361... 91 {'xmin': 0.9544028043746948, 'ymin': 0.3871219... 92 {'xmin': 0.9694010019302368, 'ymin': 0.2644164... [93 rows x 3 columns] Prediction with multiple images is permitted: .. code:: python bulk_result = detector.predict(dataset_test) print(bulk_result) .. parsed-literal:: :class: output predict_class predict_score \ 0 person 0.966625 1 motorbike 0.897586 2 person 0.236946 3 person 0.161338 4 person 0.142619 ... ... ... 4489 chair 0.018779 4490 person 0.018736 4491 chair 0.018437 4492 person 0.018423 4493 person 0.018311 predict_rois \ 0 {'xmin': 0.3883911073207855, 'ymin': 0.2853710... 1 {'xmin': 0.32461124658584595, 'ymin': 0.430736... 2 {'xmin': 0.9941485524177551, 'ymin': 0.5980668... 3 {'xmin': 0.3308553695678711, 'ymin': 0.4322595... 4 {'xmin': 0.9954673051834106, 'ymin': 0.8241726... ... ... 4489 {'xmin': 0.6117137670516968, 'ymin': 0.2451491... 4490 {'xmin': 0.14095193147659302, 'ymin': 0.455573... 4491 {'xmin': 0.6955369710922241, 'ymin': 0.2798658... 4492 {'xmin': 0.9132900834083557, 'ymin': 0.3606295... 4493 {'xmin': 0.18184493482112885, 'ymin': 0.501161... image 0 /var/lib/jenkins/.gluoncv/datasets/tiny_motorb... 1 /var/lib/jenkins/.gluoncv/datasets/tiny_motorb... 2 /var/lib/jenkins/.gluoncv/datasets/tiny_motorb... 3 /var/lib/jenkins/.gluoncv/datasets/tiny_motorb... 4 /var/lib/jenkins/.gluoncv/datasets/tiny_motorb... ... ... 4489 /var/lib/jenkins/.gluoncv/datasets/tiny_motorb... 4490 /var/lib/jenkins/.gluoncv/datasets/tiny_motorb... 4491 /var/lib/jenkins/.gluoncv/datasets/tiny_motorb... 4492 /var/lib/jenkins/.gluoncv/datasets/tiny_motorb... 4493 /var/lib/jenkins/.gluoncv/datasets/tiny_motorb... [4494 rows x 4 columns] We can also save the trained model, and use it later. .. code:: python savefile = 'detector.ag' detector.save(savefile) new_detector = ObjectDetector.load(savefile) .. parsed-literal:: :class: output /var/lib/jenkins/workspace/workspace/autogluon-tutorial-object-detection-v3/venv/lib/python3.7/site-packages/mxnet/gluon/block.py:1512: UserWarning: Cannot decide type for the following arguments. Consider providing them as input: data: None input_sym_arg_type = in_param.infer_type()[0]