.. _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 ObjectDetector: .. code:: python 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} hyperparameter_tune_kwargs={'num_trials': 2} detector.fit(dataset_train, time_limit=time_limit, hyperparameters=hyperparameters, hyperparameter_tune_kwargs=hyperparameter_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[152]/validation[18] splits. Starting HPO experiments .. parsed-literal:: :class: output 0%| | 0/2 [00:00 != ): { root.train.seed 233 != 188 root.train.batch_size 16 != 8 root.train.early_stop_patience -1 != 10 root.train.epochs 20 != 5 root.train.early_stop_baseline 0.0 != -inf root.train.early_stop_max_value 1.0 != inf root.dataset voc_tiny != auto root.valid.batch_size 16 != 8 root.ssd.data_shape 300 != 512 root.ssd.base_network vgg16_atrous != resnet50_v1 root.gpus (0, 1, 2, 3) != (0,) root.dataset_root ~/.mxnet/datasets/ != auto root.num_workers 4 != 8 } Saved config to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-object-detection-v3/docs/_build/eval/tutorials/object_detection/7110f2aa/.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: 9.372814, CrossEntropy=3.422843, SmoothL1=0.970789 [Epoch 0] Validation: person=0.6336037361653125 motorbike=0.7388429752066115 cow=nan chair=nan pottedplant=0.0 bicycle=0.0 bus=1.0000000000000002 car=1.0000000000000002 boat=nan dog=0.0 mAP=0.48177810162456064 [Epoch 0] Current best map: 0.481778 vs previous 0.000000, saved to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-object-detection-v3/docs/_build/eval/tutorials/object_detection/7110f2aa/.trial_0/best_checkpoint.pkl [Epoch 1] Training cost: 8.013312, CrossEntropy=2.728228, SmoothL1=1.148340 [Epoch 1] Validation: person=0.8179817081730957 motorbike=0.8139361707430133 cow=nan chair=nan pottedplant=0.0 bicycle=0.0 bus=1.0000000000000002 car=1.0000000000000002 boat=nan dog=0.33333333333333326 mAP=0.5664644588927775 [Epoch 1] Current best map: 0.566464 vs previous 0.481778, saved to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-object-detection-v3/docs/_build/eval/tutorials/object_detection/7110f2aa/.trial_0/best_checkpoint.pkl [Epoch 2] Training cost: 8.277046, CrossEntropy=2.254046, SmoothL1=0.981910 [Epoch 2] Validation: person=0.700187969924812 motorbike=0.912092957547503 cow=nan chair=nan pottedplant=0.0 bicycle=0.0 bus=1.0000000000000002 car=1.0000000000000002 boat=nan dog=0.0 mAP=0.5160401324960451 [Epoch 3] Training cost: 8.101155, CrossEntropy=2.234331, SmoothL1=0.996270 [Epoch 3] Validation: person=0.7145325078816583 motorbike=0.8005809979494191 cow=nan chair=nan pottedplant=0.0 bicycle=0.0 bus=1.0000000000000002 car=1.0000000000000002 boat=nan dog=0.0 mAP=0.5021590722615825 [Epoch 4] Training cost: 8.140993, CrossEntropy=2.261475, SmoothL1=0.949625 [Epoch 4] Validation: person=0.7183485157793459 motorbike=0.8556343837650554 cow=nan chair=nan pottedplant=0.0 bicycle=0.0 bus=1.0000000000000002 car=1.0000000000000002 boat=nan dog=0.0 mAP=0.5105689856492003 Applying the state from the best checkpoint... modified configs( != ): { root.train.seed 233 != 188 root.train.early_stop_patience -1 != 10 root.train.epochs 20 != 5 root.train.early_stop_baseline 0.0 != -inf root.train.early_stop_max_value 1.0 != inf root.train.batch_size 16 != 8 root.dataset voc_tiny != auto root.valid.batch_size 16 != 8 root.gpus (0, 1, 2, 3) != (0,) root.dataset_root ~/.mxnet/datasets/ != auto root.num_workers 4 != 8 } Saved config to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-object-detection-v3/docs/_build/eval/tutorials/object_detection/7110f2aa/.trial_1/config.yaml Using transfer learning from yolo3_darknet53_coco, the other network parameters are ignored. Start training from [Epoch 0] [Epoch 0] Training cost: 15.599, ObjLoss=9.696, BoxCenterLoss=8.126, BoxScaleLoss=2.626, ClassLoss=4.826 [Epoch 0] Validation: person=0.643974227310219 motorbike=0.7011628893981835 cow=nan chair=nan pottedplant=0.0 bicycle=0.5000000000000001 bus=1.0000000000000002 car=0.32057416267942584 boat=nan dog=0.5000000000000001 mAP=0.5236730399125469 [Epoch 0] Current best map: 0.523673 vs previous 0.000000, saved to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-object-detection-v3/docs/_build/eval/tutorials/object_detection/7110f2aa/.trial_1/best_checkpoint.pkl [Epoch 1] Training cost: 12.816, ObjLoss=9.781, BoxCenterLoss=7.802, BoxScaleLoss=2.691, ClassLoss=3.930 [Epoch 1] Validation: person=0.740512972865914 motorbike=0.6893028024606972 cow=nan chair=nan pottedplant=0.0 bicycle=1.0000000000000002 bus=1.0000000000000002 car=1.0000000000000002 boat=nan dog=0.11111111111111108 mAP=0.6487038409196747 [Epoch 1] Current best map: 0.648704 vs previous 0.523673, saved to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-object-detection-v3/docs/_build/eval/tutorials/object_detection/7110f2aa/.trial_1/best_checkpoint.pkl [Epoch 2] Training cost: 13.141, ObjLoss=9.983, BoxCenterLoss=7.779, BoxScaleLoss=2.864, ClassLoss=3.571 [Epoch 2] Validation: person=0.7642860422405876 motorbike=0.5028801701976575 cow=nan chair=nan pottedplant=0.0 bicycle=0.33333333333333326 bus=0.25000000000000006 car=1.0000000000000002 boat=nan dog=0.5000000000000001 mAP=0.47864279225308265 [Epoch 3] Training cost: 10.478, ObjLoss=9.781, BoxCenterLoss=7.755, BoxScaleLoss=2.866, ClassLoss=3.347 [Epoch 3] Validation: person=0.7978907352480293 motorbike=0.8500494071146245 cow=nan chair=nan pottedplant=0.025000000000000005 bicycle=0.5000000000000001 bus=0.0 car=0.7727272727272726 boat=nan dog=0.0 mAP=0.4208096307271324 [Epoch 4] Training cost: 13.389, ObjLoss=9.742, BoxCenterLoss=7.780, BoxScaleLoss=2.925, ClassLoss=3.208 [Epoch 4] Validation: person=0.8209430919957236 motorbike=0.8627934661371194 cow=nan chair=nan pottedplant=0.0 bicycle=0.14285714285714288 bus=1.0000000000000002 car=1.0000000000000002 boat=nan dog=0.0 mAP=0.5466562429985694 Applying the state from the best checkpoint... Finished, total runtime is 163.77 s { 'best_config': { 'dataset': 'auto', 'dataset_root': 'auto', 'estimator': , 'gpus': [0], 'horovod': False, 'num_workers': 8, 'resume': '', 'save_interval': 10, 'save_prefix': '', 'train': { 'batch_size': 8, 'early_stop_baseline': -inf, 'early_stop_max_value': inf, 'early_stop_min_delta': 0.001, 'early_stop_patience': 10, 'epochs': 5, 'label_smooth': False, 'log_interval': 100, 'lr': 0.001, 'lr_decay': 0.1, 'lr_decay_epoch': (160, 180), 'lr_decay_period': 0, 'lr_mode': 'step', 'mixup': False, 'momentum': 0.9, 'no_mixup_epochs': 20, 'no_wd': False, 'num_samples': -1, 'seed': 188, 'start_epoch': 0, 'warmup_epochs': 0, 'warmup_lr': 0.0, 'wd': 0.0005}, 'valid': { 'batch_size': 8, 'iou_thresh': 0.5, 'metric': 'voc07', 'val_interval': 1}, 'yolo3': { 'amp': False, 'anchors': ( [10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]), 'base_network': 'darknet53', 'data_shape': 416, 'filters': (512, 256, 128), 'nms_thresh': 0.45, 'nms_topk': 400, 'no_random_shape': False, 'strides': (8, 16, 32), 'syncbn': False, 'transfer': 'yolo3_darknet53_coco'}}, 'total_time': 163.76751351356506, 'train_map': 0.7782267804566688, 'valid_map': 0.6487038409196747} .. 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 ``searcher='bayesopt'`` in ``hyperparameter_tune_kwargs`` 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: 0.3416276980042973 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 motorbike 0.682645 1 person 0.577913 2 car 0.363752 3 motorbike 0.352576 4 person 0.258981 5 person 0.256710 6 motorbike 0.225929 7 person 0.200261 8 motorbike 0.120902 9 bicycle 0.109221 10 pottedplant 0.099113 11 person 0.099011 12 person 0.090721 13 person 0.088232 14 person 0.070633 15 person 0.062771 16 person 0.046365 17 person 0.043824 18 bicycle 0.042624 19 person 0.041354 20 cow 0.040686 21 boat 0.039857 22 pottedplant 0.039258 23 bus 0.038404 24 motorbike 0.038231 25 chair 0.036855 26 motorbike 0.032093 27 pottedplant 0.031982 28 person 0.031942 29 person 0.029937 30 car 0.028760 31 dog 0.028587 32 motorbike 0.024838 33 dog 0.023639 34 motorbike 0.023555 35 chair 0.023419 36 person 0.022975 37 motorbike 0.021419 38 person 0.020317 39 person 0.016776 40 pottedplant 0.015502 41 dog 0.015088 42 pottedplant 0.015062 43 person 0.013744 44 motorbike 0.011344 45 motorbike 0.011245 46 person 0.011236 47 motorbike 0.011236 48 person 0.011068 49 person 0.010974 50 person 0.010575 predict_rois 0 {'xmin': 0.3310595154762268, 'ymin': 0.4464629... 1 {'xmin': 0.34560394287109375, 'ymin': 0.347209... 2 {'xmin': 0.0, 'ymin': 0.6688785552978516, 'xma... 3 {'xmin': 0.0, 'ymin': 0.6157286763191223, 'xma... 4 {'xmin': 0.6616300940513611, 'ymin': 0.0, 'xma... 5 {'xmin': 0.4548812210559845, 'ymin': 0.0031030... 6 {'xmin': 0.007165733724832535, 'ymin': 0.67869... 7 {'xmin': 0.057544589042663574, 'ymin': 0.02677... 8 {'xmin': 0.35936659574508667, 'ymin': 0.247161... 9 {'xmin': 0.3310595154762268, 'ymin': 0.4464629... 10 {'xmin': 0.0, 'ymin': 0.6688785552978516, 'xma... 11 {'xmin': 0.7704325914382935, 'ymin': 0.0, 'xma... 12 {'xmin': 0.6943906545639038, 'ymin': 0.0, 'xma... 13 {'xmin': 0.4034964144229889, 'ymin': 0.2719404... 14 {'xmin': 0.5255002975463867, 'ymin': 0.0012342... 15 {'xmin': 0.7239393591880798, 'ymin': 0.3926926... 16 {'xmin': 0.9029600620269775, 'ymin': 0.0302012... 17 {'xmin': 0.6395756602287292, 'ymin': 0.0419282... 18 {'xmin': 0.0, 'ymin': 0.6688785552978516, 'xma... 19 {'xmin': 0.5328963398933411, 'ymin': 0.0, 'xma... 20 {'xmin': 0.0, 'ymin': 0.6688785552978516, 'xma... 21 {'xmin': 0.0, 'ymin': 0.6688785552978516, 'xma... 22 {'xmin': 0.35936659574508667, 'ymin': 0.247161... 23 {'xmin': 0.0, 'ymin': 0.6688785552978516, 'xma... 24 {'xmin': 0.7239393591880798, 'ymin': 0.3926926... 25 {'xmin': 0.0, 'ymin': 0.6157286763191223, 'xma... 26 {'xmin': 0.4548812210559845, 'ymin': 0.0031030... 27 {'xmin': 0.3310595154762268, 'ymin': 0.4464629... 28 {'xmin': 0.6135271787643433, 'ymin': 0.0339585... 29 {'xmin': 0.8174579739570618, 'ymin': 0.0, 'xma... 30 {'xmin': 0.7729672193527222, 'ymin': 0.0, 'xma... 31 {'xmin': 0.3310595154762268, 'ymin': 0.4464629... 32 {'xmin': 0.7729672193527222, 'ymin': 0.0, 'xma... 33 {'xmin': 0.0, 'ymin': 0.6157286763191223, 'xma... 34 {'xmin': 0.056816305965185165, 'ymin': 0.03956... 35 {'xmin': 0.007165733724832535, 'ymin': 0.67869... 36 {'xmin': 0.9146621227264404, 'ymin': 0.0, 'xma... 37 {'xmin': 0.6616300940513611, 'ymin': 0.0, 'xma... 38 {'xmin': 0.0, 'ymin': 0.6688785552978516, 'xma... 39 {'xmin': 0.5964206457138062, 'ymin': 0.0, 'xma... 40 {'xmin': 0.7239393591880798, 'ymin': 0.3926926... 41 {'xmin': 0.007165733724832535, 'ymin': 0.67869... 42 {'xmin': 0.4548812210559845, 'ymin': 0.0031030... 43 {'xmin': 0.2948954403400421, 'ymin': 0.2013196... 44 {'xmin': 0.6943906545639038, 'ymin': 0.0, 'xma... 45 {'xmin': 0.8174579739570618, 'ymin': 0.0, 'xma... 46 {'xmin': 0.03064700961112976, 'ymin': 0.0, 'xm... 47 {'xmin': 0.797978937625885, 'ymin': 0.08672408... 48 {'xmin': 0.9054626822471619, 'ymin': 0.0, 'xma... 49 {'xmin': 0.6799211502075195, 'ymin': 0.0312307... 50 {'xmin': 0.9095916748046875, 'ymin': 0.0010530... 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 motorbike 0.682645 1 person 0.577913 2 car 0.363752 3 motorbike 0.352576 4 person 0.258981 ... ... ... 1857 motorbike 0.011603 1858 person 0.011169 1859 motorbike 0.010710 1860 motorbike 0.010163 1861 pottedplant 0.010114 predict_rois \ 0 {'xmin': 0.3310595154762268, 'ymin': 0.4464629... 1 {'xmin': 0.34560394287109375, 'ymin': 0.347209... 2 {'xmin': 0.0, 'ymin': 0.6688785552978516, 'xma... 3 {'xmin': 0.0, 'ymin': 0.6157286763191223, 'xma... 4 {'xmin': 0.6616300940513611, 'ymin': 0.0, 'xma... ... ... 1857 {'xmin': 0.10874426364898682, 'ymin': 0.025177... 1858 {'xmin': 0.3966425359249115, 'ymin': 0.3692439... 1859 {'xmin': 0.25758716464042664, 'ymin': 0.019422... 1860 {'xmin': 0.3919074833393097, 'ymin': 0.0, 'xma... 1861 {'xmin': 0.3911811411380768, 'ymin': 0.0282618... 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... ... ... 1857 /var/lib/jenkins/.gluoncv/datasets/tiny_motorb... 1858 /var/lib/jenkins/.gluoncv/datasets/tiny_motorb... 1859 /var/lib/jenkins/.gluoncv/datasets/tiny_motorb... 1860 /var/lib/jenkins/.gluoncv/datasets/tiny_motorb... 1861 /var/lib/jenkins/.gluoncv/datasets/tiny_motorb... [1862 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)