Official code repo for TCLR: Temporal Contrastive Learning for Video Representation, Computer Vision and Image Understanding Journal Paper and Arxiv Version. In the current state, the repository exactly reproduces state-of-the-art results of our paper for UCF101 self-supervised pretraining for R3D-18 model: 69.9% linear evaluation, 82% on Full-Finetuning, 56.1% on NN Retrieval.

# Clone the github to your path, expected space: 15G
git clone https://github.com/DAVEISHAN/TCLR.git && cd TCLR
# Create environment
conda env create -f tclr_env.yml
# UCF101 data preparation
mkdir data && cd data
wget https://www.crcv.ucf.edu/data/UCF101/UCF101.rar --no-check-certificate
unrar x UCF101.rar
rm -rf UCF101.rar
wget https://www.crcv.ucf.edu/data/UCF101/UCF101TrainTestSplits-RecognitionTask.zip --no-check-certificate
unzip UCF101TrainTestSplits-RecognitionTask.zip
rm -rf UCF101TrainTestSplits-RecognitionTask.zip

GPU Memory requirement: 48G

  cd tclr_pretraining/

Activate the environment: conda activate tclr_env or source activate tclr_env

Run TCLR pretraining code using the following command:

  python train_gen_all_step.py --run_id="EXP_NAME"

Use "--restart" to continue the stopped training

The pretraining will save models at tclr_pretraining/ss_saved_models and tensorboard logs in tclr_pretraining/logs

Change directory to cd linear_eval

Run the linear evaluation code using the following command:

python train.py --saved_model="FULL/PATH/TO/SAVED/PRETRAINED/MODEL" --linear

The trained linear classifier will be saved at linear_eval/saved_models and tensorboard logs in linear_eval/logs

  cd nn_retreival
  python complete_retrieval.py --run_id="provide_exp_id_here" --saved_model="provide_complete_path_to_saved_ssl_pretrained_model"

R3D-18 with UCF101 pretraining: Google Drive
R3D-18 with Kinetics400 pretraining: Google Drive
R2+1D-18 with Kinetics400 pretraining: Google Drive

Pl, note that all models are trained on BGR video input, for inference dataloading refer to linear_eval/dl_linear

If you find the repo useful for your research, please consider citing our paper:

@article{dave2022tclr,
  title={Tclr: Temporal contrastive learning for video representation},
  author={Dave, Ishan and Gupta, Rohit and Rizve, Mamshad Nayeem and Shah, Mubarak},
  journal={Computer Vision and Image Understanding},
  pages={103406},
  year={2022},
  publisher={Elsevier}
}

For any questions, welcome to create an issue or contact Ishan Dave (ishandave@ucf.edu).