This repository contains the implementation of:
- InfoNCE (MoCo on videos)
- UberNCE (supervised contrastive learning on videos)
- CoCLR
- [2021.01.29] Upload both RGB and optical flow dataset for UCF101 (links).
- [2021.01.11] Update our paper for NeurIPS2020 final version: corrected InfoNCE-RGB-linearProbe baseline result in Table1 from 52.3% (pretrained for 800 epochs, unnessary and unfair) to 46.8% (pretrained for 500 epochs, fair comparison). Thanks @liuhualin333 for pointing out.
- [2020.12.08] Update instructions.
- [2020.11.17] Upload pretrained weights for UCF101 experiments.
- [2020.10.30] Update "draft" dataloader files, CoCLR code, evaluation code as requested by some researchers. Will check and add detailed instructions later.
- InfoNCE pretrain on UCF101-RGB
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
--nproc_per_node=2 main_nce.py --net s3d --model infonce --moco-k 2048 \
--dataset ucf101-2clip --seq_len 32 --ds 1 --batch_size 32 \
--epochs 300 --schedule 250 280 -j 16
- InfoNCE pretrain on UCF101-Flow
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
--nproc_per_node=2 main_nce.py --net s3d --model infonce --moco-k 2048 \
--dataset ucf101-f-2clip --seq_len 32 --ds 1 --batch_size 32 \
--epochs 300 --schedule 250 280 -j 16
- CoCLR pretrain on UCF101 for one cycle
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
--nproc_per_node=2 main_coclr.py --net s3d --topk 5 --moco-k 2048 \
--dataset ucf101-2stream-2clip --seq_len 32 --ds 1 --batch_size 32 \
--epochs 100 --schedule 80 --name_prefix Cycle1-FlowMining_ -j 8 \
--pretrain {rgb_infoNCE_checkpoint.pth.tar} {flow_infoNCE_checkpoint.pth.tar}
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
--nproc_per_node=2 main_coclr.py --net s3d --topk 5 --moco-k 2048 --reverse \
--dataset ucf101-2stream-2clip --seq_len 32 --ds 1 --batch_size 32 \
--epochs 100 --schedule 80 --name_prefix Cycle1-RGBMining_ -j 8 \
--pretrain {flow_infoNCE_checkpoint.pth.tar} {rgb_cycle1_checkpoint.pth.tar}
- InfoNCE pretrain on K400-RGB
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch \
--nproc_per_node=4 main_infonce.py --net s3d --model infonce --moco-k 16384 \
--dataset k400-2clip --lr 1e-3 --seq_len 32 --ds 1 --batch_size 32 \
--epochs 300 --schedule 250 280 -j 16
- InfoNCE pretrain on K400-Flow
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch \
--nproc_per_node=4 teco_fb_main.py --net s3d --model infonce --moco-k 16384 \
--dataset k400-f-2clip --lr 1e-3 --seq_len 32 --ds 1 --batch_size 32 \
--epochs 300 --schedule 250 280 -j 16
- CoCLR pretrain on K400 for one cycle
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
--nproc_per_node=2 main_coclr.py --net s3d --topk 5 --moco-k 16384 \
--dataset k400-2stream-2clip --seq_len 32 --ds 1 --batch_size 32 \
--epochs 50 --schedule 40 --name_prefix Cycle1-FlowMining_ -j 8 \
--pretrain {rgb_infoNCE_checkpoint.pth.tar} {flow_infoNCE_checkpoint.pth.tar}
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
--nproc_per_node=2 main_coclr.py --net s3d --topk 5 --moco-k 16384 --reverse \
--dataset k400-2stream-2clip --seq_len 32 --ds 1 --batch_size 32 \
--epochs 50 --schedule 40 --name_prefix Cycle1-RGBMining_ -j 8 \
--pretrain {flow_infoNCE_checkpoint.pth.tar} {rgb_cycle1_checkpoint.pth.tar}
- RGB for UCF101: [download] (tar file, 29GB, packed with lmdb)
- TVL1 optical flow for UCF101: [download] (tar file, 20.5GB, packed with lmdb)
- Note: I created these lmdb files with msgpack==0.6.2, when load them with msgpack>=1.0.0, you can do
msgpack.loads(raw_data, raw=True)(issue#32)
Finetune entire network for action classification on UCF101:
Our models:
- UCF101-RGB-CoCLR: [download] [NN@1=51.8 on UCF101-RGB]
- UCF101-Flow-CoCLR: [download] [NN@1=48.4 on UCF101-Flow]
Baseline models:
- UCF101-RGB-InfoNCE: [download] [NN@1=33.1 on UCF101-RGB]
- UCF101-Flow-InfoNCE: [download] [NN@1=45.2 on UCF101-Flow]
Kinetics400-pretrained models comming soon.