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 Image Prediction - Quick Start 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:
from autogluon.vision import ObjectDetector
/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.
url = 'https://autogluon.s3.amazonaws.com/datasets/tiny_motorbike.zip'
dataset_train = ObjectDetector.Dataset.from_voc(url, splits='trainval')
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.
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)
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
0%| | 0/2 [00:00<?, ?it/s]
modified configs(<old> != <new>): {
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(<old> != <new>): {
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': <class 'gluoncv.auto.estimators.yolo.yolo.YOLOv3Estimator'>,
'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}
<autogluon.vision.detector.detector.ObjectDetector at 0x7f6ee139a850>
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().
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]))
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.
image_path = dataset_test.iloc[0]['image']
result = detector.predict(image_path)
print(result)
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:
bulk_result = detector.predict(dataset_test)
print(bulk_result)
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.
savefile = 'detector.ag'
detector.save(savefile)
new_detector = ObjectDetector.load(savefile)