Proper Reuse of Image Classification Features Improves Object Detection

This project brings the backbone freezing training approach into the Mask-RCNN architecture. Please see the paper for more details (arxiv - selected for oral presentation at CVPR 2022).

Training Mask-Rcnn Models with backbone frozen.

Freezing Resnet-RS-101 checkpoint (ImageNet pretrained).

1. Download the ResNet-RS-101 pretrained checkpoint from TF-Vision Model Garden, (checkpoint)

2. Config files used in our Resnet-101 ablations are included in the configs folder. Select one according to the target architecture (FPN, NASFPN, NASFPN + Cascades) and training schedule preference (shorter--72 epochs, or longer --600 epochs).

3. Change the config flag init_checkpoint to point to the downloaded file.

You are all set. Follow the standard TFVision Mask-Rcnn training pipeline to complete the training.

How does it work?

The config files set the task's flag freeze_backbone: true. This flag prevents the pretrained backbone weights from being updated during the downstream model training.

Citation

@inproceedings{vasconcelos2022backbonefreeze,
      title = {Proper Reuse of Image Classification Features Improves Object Detection},
      author = {Cristina Vasconcelos and Vighnesh Birodkar and Vincent Dumoulin},
      booktitle={CVPR}
      year={2022},