SynCo: Synthetic Hard Negatives in Contrastive Learning for Better Unsupervised Visual Representations
This is a PyTorch implementation of the SynCo paper:
@misc{giakoumoglou2024synco,
author = {Nikolaos Giakoumoglou and Tania Stathaki},
title = {SynCo: Synthetic Hard Negatives in Contrastive Learning for Better Unsupervised Visual Representations},
journal = {arXiv preprint arXiv:2410.02401},
year = {2024},
}
Install PyTorch and ImageNet dataset following the official PyTorch ImageNet training code.
This repo is based on MoCo v2 and Barlow Twins code:
diff main_synco.py <(curl https://raw.githubusercontent.com/facebookresearch/moco/main_moco.py)
diff main_lincls.py <(curl https://raw.githubusercontent.com/facebookresearch/moco/main_lincls.py)
diff main_semisup.py <(curl https://raw.githubusercontent.com/facebookresearch/barlowtwins/evaluate.py)
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, run:
python main_synco.py \
-a resnet50 \
--lr 0.03 \
--batch-size 256 \
--dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
--mlp --moco-t 0.2 --aug-plus --cos \
--n-hard 1024 --n1 256 --n2 256 --n3 256 --n4 64 --n5 64 --n6 64 \
[your imagenet-folder with train and val folders]
This script uses all the default hyper-parameters as described in the MoCo v2 paper.
With a pre-trained model, to train a supervised linear classifier on frozen features/weights in an 8-gpu machine, run:
python main_lincls.py \
-a resnet50 \
--lr 30.0 \
--batch-size 256 \
--pretrained [your checkpoint path]/checkpoint_0199.pth.tar \
--dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
[your imagenet-folder with train and val folders]
This script uses all the default hyper-parameters as described in the MoCo v2 paper.
To fine-tune the model end-to-end, including training a linear classifier on features/weights using a pre-trained model on an 8-GPU machine with a subset of the ImageNet training set, run:
python main_semisup.py \
-a resnet50 \
--lr-backbone [YOUR_LR] --lr-classifier [YOUR_LR] \
--train-percent 1 --weights finetune \
--batch-size 256 \
--pretrained [your checkpoint path]/checkpoint_0199.pth.tar \
--dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
[your imagenet-folder with train and val folders]
See ./detection.
Our pre-trained ResNet-50 models can be downloaded as follows:
| epochs | top-1 acc. | model | |
|---|---|---|---|
| SynCo | 200 | 68.1 | download |
| SynCo | 800 | 70.6 | download |
This project is under the CC-BY-NC 4.0 license. See LICENSE for details.