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This repository provides the PyTorch implementation of Region Similarity Representation Learning (ReSim) described in this paper:
@Article{xiao2021region,
author = {Tete Xiao and Colorado J Reed and Xiaolong Wang and Kurt Keutzer and Trevor Darrell},
title = {Region Similarity Representation Learning},
journal = {arXiv preprint arXiv:2103.12902},
year = {2021},
}
tldr; ReSim maintains spatial relationships in the convolutional feature maps when performing instance contrastive pre-training, which is useful for region-related tasks such as object detection, segmentation, and dense pose estimation.
Installation
Assuming a conda environment:
conda create --name resim python=3.7
conda activate resim
# NOTE: if you are not using CUDA 10.2, you need to change the 10.2 in this command appropriately.
# Code tested with torch 1.6 and 1.7
# (check CUDA version with e.g. `cat /usr/local/cuda/version.txt`)
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch
Pre-training
This codebase is based on the original MoCo codebase -- see this README for more details.
To pre-train for 200 epochs using the ReSim-FPN implementation as described in the paper: