This repository contains the official PyTorch implementation of the ORL algorithm for self-supervised representation learning.

Unsupervised Object-Level Representation Learning from Scene Images,
Jiahao Xie, Xiaohang Zhan, Ziwei Liu, Yew Soon Ong, Chen Change Loy
In NeurIPS 2021
[Paper][Project Page][Bibtex]

• [12/2021] Code and pre-trained models of ORL are released.

Please refer to INSTALL.md for installation and dataset preparation.

Pre-trained models can be downloaded from Google Drive. Please see our paper for transfer learning results on different benchmarks.

You need to pre-train an image-level contrastive learning model in this stage. Take BYOL as an example:

bash tools/dist_train.sh configs/selfsup/orl/coco/stage1/r50_bs512_ep800.py 8

This stage can be freely replaced with other image-level contrastive learning models.

• KNN image retrieval

First, extract all features in the training set using the pre-trained model weights in Stage 1:

bash tools/dist_train.sh configs/selfsup/orl/coco/stage1/r50_bs512_ep800_extract_feature.py 8 --resume_from work_dirs/selfsup/orl/coco/stage1/r50_bs512_ep800/epoch_800.pth

Second, retrieve KNN for each image using tools/coco_knn_image_retrieval.ipynb. The corresponding KNN image ids will be saved as a json file train2017_knn_instance.json under data/coco/meta/.

• RoI generation

Apply selective search to generate region proposals for all images in the training set:

bash tools/dist_selective_search_single_gpu.sh configs/selfsup/orl/coco/stage2/selective_search_train2017.py data/coco/meta/train2017_selective_search_proposal.json

The script and config only support single-image single-gpu inference since different images can have different number of generated region proposals by selective search, which cannot be gathered if distributed in multiple gpus. You can also directly download here under data/coco/meta/ if you want to skip this step.

• RoI pair retrieval

Retrieve top-ranked RoI pairs:

bash tools/dist_generate_correspondence_single_gpu.sh configs/selfsup/orl/coco/stage2/r50_bs512_ep800_generate_all_correspondence.py work_dirs/selfsup/orl/coco/stage1/r50_bs512_ep800/epoch_800.pth data/coco/meta/train2017_knn_instance.json data/coco/meta/train2017_knn_instance_correspondence.json

The script and config also only support single-image single-gpu inference since different image pairs can have different number of generated inter-RoI pairs, which cannot be gathered if distributed in multiple gpus. A workaround to speed up the retrieval process is to split the whole dataset into several parts and process each part on each gpu in parallel. We provide an example of these configs (10 parts in total) in configs/selfsup/orl/coco/stage2/r50_bs512_ep800_generate_partial_correspondence/. After generating each part, you can use tools/merge_partial_correspondence_files.py to merge them together and save the final correspondence json file train2017_knn_instance_correspondence.json under data/coco/meta/.

After obtaining the correspondence file in Stage 2, you can then perform object-level pre-training:

bash tools/dist_train.sh configs/selfsup/orl/coco/stage3/r50_bs512_ep800.py 8

Please refer to GETTING_STARTED.md for transferring to various downstream tasks.

We would like to thank the OpenSelfSup for its open-source project and PyContrast for its detection evaluation configs.

Please consider citing our paper in your publications if the project helps your research. BibTeX reference is as follows:

@inproceedings{xie2021unsupervised,
  title={Unsupervised Object-Level Representation Learning from Scene Images},
  author={Xie, Jiahao and Zhan, Xiaohang and Liu, Ziwei and Ong, Yew Soon and Loy, Chen Change},
  booktitle={NeurIPS},
  year={2021}
}