How to augment data to increase the number of instances being trained on?

[mohamedamrali1993]

Hi, I am having a hard time understanding "Augmentation" in the context of Detectron2. It seems like the Data Augmentation page is striving to create variations within the dataset, but not necessarily "augmenting" as in increasing the size of the dataset. i.e. if I have a dataset with 100 images and I apply RandomBrightness() transformation to 5% of the data, I still end up with 100 images. Not 105! Am I understanding this correctly?

I tracked down the process of data augmentation, and at no point did I see data being appended to a larger query (with original images + transformed images), which is eventually sent to the model. Here is what I understand so far:

1. The trainer calls on to the build_train_loader(), which returns the build_detection_test_loader() with the "mapper" as an argument.
2. We can custom make our own "mapper" and include the appropriate transformations we want.
3. The mapper needs to transform images sequentially, and consequently transform annotations (such as bbox, masks, and keypoints) using the utils.transform_instance_annotations(annotation, transform, size)
4. Finally utils.annotations_to_instances(annotations, size, mask_format) creates the instances that are forwarded to the model.

I would like to increase the number of images that I am training on, and their respective transformed labels. Is this doable using Detectron2 and is it a good idea for Mask R-CNN? FAIR's 2018 Mask R-CNN paper has a small section in Appendix B that talks about "Train-time augmentation" where they scale images (if I am understanding things correctly), but I am not sure if they are actually training on the original + scaled images.

If this can be done using Detectron2 then please advise (including code examples), if not, I would imagine this would be a good enhancement.

Thanks!