Internal change

PiperOrigin-RevId: 451273837

official/projects/backbone_reuse/README.md

 # Proper Reuse of Image Classification Features Improves Object Detection
 
-Coming soon
-1. CVPR 2022 paper
-2. Table of results
\ No newline at end of file
+This project brings the backbone freezing training approach into the Mask-RCNN
+architecture. Please see the paper for more details
+\([arxiv](https://arxiv.org/abs/2204.00484) - selected for oral presentation at
+CVPR 2022\).
+
+### 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](https://github.com/tensorflow/models/tree/master/official/vision#resnet-rs-models-trained-with-various-settings),
+    \([checkpoint](https://storage.cloud.google.com/tf_model_garden/vision/resnet-rs/resnet-rs-101-i192.tar.gz)\)
+
+2.  Config files used in our Resnet-101 ablations are included in the
+    [configs folder](https://github.com/tensorflow/models/tree/master/official/projects/backbone_reuse/configs/experiments/faster_rcnn).
+    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},
+```