Learning Data Augmentation Strategies for Object Detection

    Barret Zoph
    Barret Zoph
    Ekin D. Cubuk
    Ekin D. Cubuk
    Golnaz Ghiasi
    Golnaz Ghiasi

    CoRR, 2019.

    Cited by: 0|Bibtex|Views38|Links
    detection modelstrong baselinedatum augmentation strategydata augmentation policyimage classification modelMore(10+)
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    We find that a learned data augmentation policy is effective across all data sizes considered, with a larger improvement when the training set is small



    • Deep neural networks are powerful machine learning systems that work best when trained on vast amounts of data.
    • Recent work has shown that instead of manually designing data augmentation strategies, learning an optimal policy from data can lead to significant improvements in generalization performance of image classification models [22, 45, 8, 33, 31, 54, 2, 43, 37, 5].
    • We investigate how the performance of a data augmentation policy depends on the number of operations included in the search space and how the effective of the augmentation technique varies as dataset size changes.
    • Design and implement a search method to combine and optimize data augmentation policies for object detection problems by combining novel operations specific to bounding box annotations.
    • A good data augmentation policy is one that can transfer between models, between datasets and work well for models trained on different image sizes.
    • We experiment with the learned augmentation policy on a different backbone architecture and detection model.
    • These experiments show that the augmentation policy transfers well across a different backbone architecture, detection algorithm, image sizes (i.e. 640 → 1280 pixels), and training procedure .
    • To evaluate the transferability of the learned policies to an entirely different dataset and another different detection algorithm, we train a Faster R-CNN [39] model with a ResNet-101 backbone on PASCAL VOC dataset [11].
    • The improvements due to the learned augmentation policy is larger when the model is trained on smaller datasets, which can be seen in Fig. 3 and in Table 5.
    • As the training set size is increased, the effect of the learned augmentation policy is decreased, the improvements are still significant.
    • It is interesting to note that models trained with learned augmentation policy seem to do especially well on detecting smaller objects, especially when fewer images are present in the training dataset.
    • For small objects, applying the learned augmentation policy seems to be better than increasing the dataset size by 50%, as seen in Table.
    • 5. For small objects, training with the learned augmentation policy with 9000 examples results in better performance than the baseline when using 15000 images.
    • When we apply the learned data augmentation, the training loss is increased significantly for all dataset sizes.
    • We investigate the application of a learned data augmentation policy on object detection performance.
    • We find that a learned data augmentation policy is effective across all data sizes considered, with a larger improvement when the training set is small.
    • We show that for models trained on 5,000 training samples, the learned augmentation policy can improve mAP by more than 70% relative to the baseline
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