bpfliegel
commented
3 years ago You can already statically preaugment your dataset like you described above. A better technique is however, to include dynamic augmentations in the training pipeline.
Please refer to the guide at: https://detectron2.readthedocs.io/tutorials/data_loading.html
You can use e.g. the albumentations library for sharpness or other augmentations: https://github.com/albumentations-team/albumentations
Nwaf-sl
🚀 Feature
I have been using Detectron2 for 3 months. I have tried nearly all the data augmentation techniques which are provided out-of-the-box by Detectron2. I would like to propose a new data augmentation technique that will enhance the input image quality, "Sharpness".
Motivation & Examples
Image sharpness enhances the edges of objects in the image which improves the masking and overall loss. A lot of people use sharped images as a separate dataset and put them in the training, which increases the training images. With this new augmentation, selective images will get sharped and seen by the model.