SetSim.pytorch: A Python repository from DerrickWang005

[CVPR 2022] Exploring Set Similarity for Dense Self-supervised Representation Learning

This is a PyTorch implementation of our paper.

@inproceedings{wang2022exploring,
  title={Exploring set similarity for dense self-supervised representation learning},
  author={Wang, Zhaoqing and Li, Qiang and Zhang, Guoxin and Wan, Pengfei and Zheng, Wen and Wang, Nannan and Gong, Mingming and Liu, Tongliang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={16590--16599},
  year={2022}
}

Requirements

Python >= 3.7.12
PyTorch >= 1.10.2
torchvision >= 0.11.3

Install PyTorch and ImageNet dataset following the official PyTorch ImageNet training code.

For other dependencies, please run:

pip install -r requirements.txt

Unsupervised Pre-training

This implementation only supports multi-gpu, DistributedDataParallel training, which is faster and simpler; single-gpu or DataParallel training is not supported.

To do unsupervised pre-training of a ResNet-50 model on ImageNet in an 8-gpu machine, please run:

bash train_pretrain.sh

Linear Classification

With a pre-trained model, to train a supervised linear classifier on frozen features/weights in an 8-gpu machine, please run:

bash train_lincls.sh

Fine-tuning on Pascal VOC object detection

With a pre-trained model, to fine-tune a Faster R-CNN on unfrozen features/weights in an 8-gpu machine, please run:

bash train_det.sh

License