Object Detection - Quick Start¶
Note: AutoGluon ObjectDetector will be deprecated in v0.7. Please try our AutoGluon MultiModalPredictor for more functionalities and better support for your object detection need.
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
/home/ci/opt/venv/lib/python3.8/site-packages/gluoncv/__init__.py:40: UserWarning: Both mxnet==1.9.1 and torch==1.12.1+cu113 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. ' INFO:matplotlib.font_manager:generated new fontManager INFO:torch.distributed.nn.jit.instantiator:Created a temporary directory at /tmp/tmp0osfunrh INFO:torch.distributed.nn.jit.instantiator:Writing /tmp/tmp0osfunrh/_remote_module_non_scriptable.py
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')
Downloading /home/ci/.gluoncv/archive/tiny_motorbike.zip from https://autogluon.s3.amazonaws.com/datasets/tiny_motorbike.zip...
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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)
=============================================================================
WARNING: ObjectDetector is deprecated as of v0.4.0 and may contain various bugs and issues!
In a future release ObjectDetector may be entirely reworked to use Torch as a backend.
This future change will likely be API breaking.Users should ensure they update their code that depends on ObjectDetector when upgrading to future AutoGluon releases.
For more information, refer to ObjectDetector refactor GitHub issue: https://github.com/autogluon/autogluon/issues/1559
=============================================================================
The number of requested GPUs is greater than the number of available GPUs.Reduce the number to 1
Randomly split train_data into train[159]/validation[11] splits.
Starting HPO experiments
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Finished, total runtime is 75.31 s
{ 'best_config': { 'dataset': 'auto',
'dataset_root': 'auto',
'estimator': <class 'gluoncv.auto.estimators.ssd.ssd.SSDEstimator'>,
'gpus': [0],
'horovod': False,
'num_workers': 8,
'resume': '',
'save_interval': 1,
'ssd': { 'amp': False,
'base_network': 'resnet50_v1',
'data_shape': 512,
'filters': None,
'nms_thresh': 0.45,
'nms_topk': 400,
'ratios': ( [1, 2, 0.5],
[1, 2, 0.5, 3, 0.3333333333333333],
[1, 2, 0.5, 3, 0.3333333333333333],
[1, 2, 0.5, 3, 0.3333333333333333],
[1, 2, 0.5],
[1, 2, 0.5]),
'sizes': (30, 60, 111, 162, 213, 264, 315),
'steps': (8, 16, 32, 64, 100, 300),
'syncbn': False,
'transfer': 'ssd_512_resnet50_v1_coco'},
'train': { 'batch_size': 8,
'dali': False,
'early_stop_baseline': -inf,
'early_stop_max_value': inf,
'early_stop_min_delta': 0.001,
'early_stop_patience': 10,
'epochs': 5,
'log_interval': 100,
'lr': 0.001,
'lr_decay': 0.1,
'lr_decay_epoch': (160, 200),
'momentum': 0.9,
'seed': 421,
'start_epoch': 0,
'wd': 0.0005},
'valid': { 'batch_size': 8,
'iou_thresh': 0.5,
'metric': 'voc07',
'val_interval': 1}},
'total_time': 75.30475449562073,
'train_map': 0.7204280566885625,
'valid_map': 0.9341521150509916}
<autogluon.vision.detector.detector.ObjectDetector at 0x7f8f689c16a0>
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.
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: 0.39227659045813806
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 0.944437 1 person 0.829662 2 motorbike 0.319636 3 motorbike 0.233822 4 motorbike 0.227945 5 car 0.169213 6 motorbike 0.136762 7 motorbike 0.105894 8 pottedplant 0.104180 9 motorbike 0.072894 10 person 0.068709 11 person 0.067453 12 person 0.067396 13 person 0.061977 14 person 0.061744 15 motorbike 0.052180 16 bicycle 0.050272 17 person 0.049930 18 motorbike 0.048863 19 motorbike 0.047319 20 car 0.043177 21 person 0.041619 22 person 0.041228 23 person 0.037719 24 car 0.036797 25 person 0.036548 26 person 0.036156 27 person 0.035666 28 car 0.035511 29 person 0.034619 30 dog 0.034503 31 person 0.034149 32 motorbike 0.032053 33 person 0.032023 34 chair 0.031928 35 person 0.031283 36 person 0.031249 37 motorbike 0.030009 38 person 0.029745 39 person 0.029688 40 person 0.029669 41 person 0.029532 42 car 0.029507 43 person 0.029293 44 motorbike 0.029144 45 motorbike 0.028926 46 motorbike 0.028599 47 person 0.028130 48 person 0.027599 49 bicycle 0.027499 50 person 0.027135 51 person 0.027024 52 motorbike 0.026887 53 motorbike 0.026885 54 person 0.026587 55 car 0.026375 predict_rois 0 {'xmin': 0.3198387324810028, 'ymin': 0.4405676... 1 {'xmin': 0.3818334937095642, 'ymin': 0.2969620... 2 {'xmin': 0.7121447324752808, 'ymin': 0.1802718... 3 {'xmin': 0.0005333706503733993, 'ymin': 0.6465... 4 {'xmin': 0.3765932023525238, 'ymin': 0.3170163... 5 {'xmin': 0.0005333706503733993, 'ymin': 0.6465... 6 {'xmin': 0.7058811187744141, 'ymin': 0.3079434... 7 {'xmin': 0.6361035108566284, 'ymin': 0.0681726... 8 {'xmin': 0.3053668439388275, 'ymin': 0.4499387... 9 {'xmin': 0.7123414874076843, 'ymin': 0.3897169... 10 {'xmin': 0.3962763249874115, 'ymin': 0.2919827... 11 {'xmin': 0.33416154980659485, 'ymin': 0.345117... 12 {'xmin': 0.3133251667022705, 'ymin': 0.2210566... 13 {'xmin': 0.9876390695571899, 'ymin': 0.5288349... 14 {'xmin': 0.9027056097984314, 'ymin': 0.2078328... 15 {'xmin': 0.3133251667022705, 'ymin': 0.2210566... 16 {'xmin': 0.30911460518836975, 'ymin': 0.450034... 17 {'xmin': 0.37443625926971436, 'ymin': 0.306855... 18 {'xmin': 0.39615774154663086, 'ymin': 0.067459... 19 {'xmin': 0.755367636680603, 'ymin': 0.08559870... 20 {'xmin': 0.666469931602478, 'ymin': 0.05101162... 21 {'xmin': 0.30857816338539124, 'ymin': 0.365583... 22 {'xmin': 0.9970389604568481, 'ymin': 0.4524995... 23 {'xmin': 0.4953533709049225, 'ymin': 0.3068005... 24 {'xmin': 0.6799491047859192, 'ymin': 0.1457533... 25 {'xmin': 0.4737255573272705, 'ymin': 0.3021801... 26 {'xmin': 0.9882245063781738, 'ymin': 0.2375874... 27 {'xmin': 0.0, 'ymin': 0.3394560217857361, 'xma... 28 {'xmin': 0.6506831645965576, 'ymin': 0.1000173... 29 {'xmin': 0.44540950655937195, 'ymin': 0.287244... 30 {'xmin': 0.30911460518836975, 'ymin': 0.450034... 31 {'xmin': 0.7109805941581726, 'ymin': 0.3976593... 32 {'xmin': 0.3169269561767578, 'ymin': 0.5118487... 33 {'xmin': 0.9669552445411682, 'ymin': 0.5048523... 34 {'xmin': 0.3019549548625946, 'ymin': 0.0020512... 35 {'xmin': 0.43810808658599854, 'ymin': 0.086986... 36 {'xmin': 0.4799288213253021, 'ymin': 0.3076659... 37 {'xmin': 0.8052250146865845, 'ymin': 0.1642719... 38 {'xmin': 0.9810229539871216, 'ymin': 0.0498418... 39 {'xmin': 0.996984601020813, 'ymin': 0.53755748... 40 {'xmin': 0.9962202906608582, 'ymin': 0.3898071... 41 {'xmin': 0.5076624751091003, 'ymin': 0.3166834... 42 {'xmin': 0.30911460518836975, 'ymin': 0.450034... 43 {'xmin': 0.3156031370162964, 'ymin': 0.2795293... 44 {'xmin': 0.19459757208824158, 'ymin': 0.344114... 45 {'xmin': 0.37192651629447937, 'ymin': 0.109095... 46 {'xmin': 0.6792832016944885, 'ymin': 0.4234628... 47 {'xmin': 0.9949443340301514, 'ymin': 0.5022779... 48 {'xmin': 0.4073106646537781, 'ymin': 0.2365254... 49 {'xmin': 0.7109805941581726, 'ymin': 0.3976593... 50 {'xmin': 0.46960002183914185, 'ymin': 0.290261... 51 {'xmin': 0.922869861125946, 'ymin': 0.59202671... 52 {'xmin': 0.6162905693054199, 'ymin': 0.1245651... 53 {'xmin': 0.7131490707397461, 'ymin': 0.2423614... 54 {'xmin': 0.30911460518836975, 'ymin': 0.450034... 55 {'xmin': 0.7139614820480347, 'ymin': 0.0222105...
Prediction with multiple images is permitted:
bulk_result = detector.predict(dataset_test)
print(bulk_result)
predict_class predict_score 0 motorbike 0.944437 1 person 0.829662 2 motorbike 0.319636 3 motorbike 0.233822 4 motorbike 0.227945 ... ... ... 3857 person 0.036159 3858 person 0.035951 3859 motorbike 0.035950 3860 person 0.035922 3861 car 0.035859 predict_rois 0 {'xmin': 0.3198387324810028, 'ymin': 0.4405676... 1 {'xmin': 0.3818334937095642, 'ymin': 0.2969620... 2 {'xmin': 0.7121447324752808, 'ymin': 0.1802718... 3 {'xmin': 0.0005333706503733993, 'ymin': 0.6465... 4 {'xmin': 0.3765932023525238, 'ymin': 0.3170163... ... ... 3857 {'xmin': 0.34639793634414673, 'ymin': 0.984214... 3858 {'xmin': 0.17816868424415588, 'ymin': 0.661037... 3859 {'xmin': 0.09734556078910828, 'ymin': 0.565375... 3860 {'xmin': 0.42627769708633423, 'ymin': 0.227234... 3861 {'xmin': 0.7766627073287964, 'ymin': 0.4587219... image 0 /home/ci/.gluoncv/datasets/tiny_motorbike/tiny... 1 /home/ci/.gluoncv/datasets/tiny_motorbike/tiny... 2 /home/ci/.gluoncv/datasets/tiny_motorbike/tiny... 3 /home/ci/.gluoncv/datasets/tiny_motorbike/tiny... 4 /home/ci/.gluoncv/datasets/tiny_motorbike/tiny... ... ... 3857 /home/ci/.gluoncv/datasets/tiny_motorbike/tiny... 3858 /home/ci/.gluoncv/datasets/tiny_motorbike/tiny... 3859 /home/ci/.gluoncv/datasets/tiny_motorbike/tiny... 3860 /home/ci/.gluoncv/datasets/tiny_motorbike/tiny... 3861 /home/ci/.gluoncv/datasets/tiny_motorbike/tiny... [3862 rows x 4 columns]
We can also save the trained model, and use it later.
Warning
ObjectDetector.load()
used pickle
module implicitly, which is
known to be insecure. It is possible to construct malicious pickle
data which will execute arbitrary code during unpickling. Never load
data that could have come from an untrusted source, or that could
have been tampered with. Only load data you trust.
savefile = 'detector.ag'
detector.save(savefile)
new_detector = ObjectDetector.load(savefile)
/home/ci/opt/venv/lib/python3.8/site-packages/mxnet/gluon/block.py:1784: UserWarning: Cannot decide type for the following arguments. Consider providing them as input:
data: None
input_sym_arg_type = in_param.infer_type()[0]