Learning Data Augmentation Strategies for Object Detection
detection modelstrong baselinedatum augmentation strategydata augmentation policyimage classification modelMore(10+)
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  model with a ResNet-101 backbone on PASCAL VOC dataset .
- 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