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CL-Baselines

This is a Pytorch implementation of contrastive Learning(CL) baselines(SimCLR, MoCov2, SimSiam).

You can

  1. run recent CL baselines on multi-scale datasets(CIFAR10/100, ImageNet-100, ImageNet, COCO).
  2. easily modify the various baselines for your research/project.

Preparation

  1. Install conda
  2. Make conda environment & activate it
conda env create -f cl_env.yaml
conda activate cl_env

Results

Experimented on CIFAR100 with Resnet-18

Knn-acc

SimCLR MoCo v2 SimSiam
Knn Acc. 59.72 60.61 62.64

Training Curve

Training

SimCLR

CIFAR

python main_train.py \
    --method simclr --arch resnet18 \
    --dataset cifar100 --batch_size 512 --eval_batch_size 512 --num_workers 8 \
    --epochs 1000 --knn_eval_freq 20 --lr 0.5 --wd 1e-4 --temp 0.5 \
    --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
    --trial 0

ImageNet-100

python main_train.py \
    --method simclr --arch resnet50 \
    --dataset ImageNet-100 --batch_size 256 --eval_batch_size 512 --num_workers 8 \
    --data_path [your imagenet-folder] \  
    --epochs 200 --knn_eval_freq 20 --lr 0.5 --wd 1e-4 --temp 0.5 \
    --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
    --trial 0

MoCo v2

CIFAR

python main_train.py \
    --method moco --arch resnet18 \
    --dataset cifar100 --batch_size 512 --eval_batch_size 512 --num_workers 8 \
    --epochs 800 --knn_eval_freq 20 --lr 0.06 --wd 5e-4 --temp 0.1\
    --moco-k 4096 --moco-m 0.99 \
    --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
    --trial 0

ImageNet-100

python main_train.py \
    --method moco --arch resnet50 \
    --dataset ImageNet-100 --batch_size 256 --eval_batch_size 512 --num_workers 8 \
    --data_path [your imagenet-folder] \  
    --epochs 200 --knn_eval_freq 20 --lr 0.06 --wd 1e-4 --temp 0.1\
    --moco-k 16128 --moco-m 0.999 \
    --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
    --trial 0

SimSiam

CIFAR

python main_train.py \
    --method simsiam --arch resnet18 \
    --dataset cifar100 --batch_size 512 --eval_batch_size 512 --num_workers 8 \
    --epochs 800 --knn_eval_freq 20 --lr 0.06 --wd 5e-4 \
    --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
    --trial 0

ImageNet-100

python main_train.py \
    --method simsiam --arch resnet50 \
    --dataset Imagenet-100 --batch_size 256 --eval_batch_size 512 --num_workers 8 \
    --data_path [your imagenet-folder] \ 
    --epochs 200 --knn_eval_freq 20 --lr 0.06 --wd 1e-4 \
    --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
    --trial 0

Knn Evaluation

python main_knn_eval.py \
    --method simclr --arch resnet18 --dataset cifar100 \
    --saved_path ../CL_logs/cifar100-simclr_resnet18-None-0

References

(1) SimCLR: https://github.com/HobbitLong/SupContrast
(2) MoCo: https://github.com/facebookresearch/moco
(3) SimSiam: https://github.com/facebookresearch/simsiam