Frozen️ in Time

A Joint Video and Image Encoder for End-to-End Retrieval

Repository containing the code, models, data for end-to-end retrieval. WebVid data can be found here


πŸ“ Preparation

  1. Create conda env conda env create -f requirements/frozen.yml

  2. Create data / experiment folders mkdir data; mkdir exps, note this can just be a symlink to where you want to store big data.

πŸ”§ Finetuning (benchmarks: MSR-VTT)

  1. wget https://www.robots.ox.ac.uk/~maxbain/frozen-in-time/data/MSRVTT.zip -P data; unzip data/MSRVTT.zip -d data

  2. Change num_gpus in the config file accordingly.

  3. Train python train.py --config configs/msrvtt_4f_i21k.json

  4. Test python test.py --resume exps/models/{EXP_NAME}/{EXP_TIMESTAMP}/model_best.pth

For finetuning a pretrained model, set "load_checkpoint": "PATH_TO_MODEL" in the config file.

πŸ‹οΈβ€οΈ Pretraining

  1. Download WebVid-2M (see https://github.com/m-bain/webvid)

  2. Download CC-3M (see https://ai.google.com/research/ConceptualCaptions/download)

  3. Train. python train.py --config CONFIG_PATH. Here are the different options:

    a. Dataset combinations

     i. CC-3M + WebVid2M: configs/cc-webvid2m-pt-i2k.json
     ii. WebVid2M : configs/webvid2m-pt-i2k.json
    

    You can add in an arbitrary number of image/video datasets for pre-training by adding as many dataloaders to the config file dataloader list as your heart desires. Adding more datasets will likely to higher downstream performance.

    b. Number of frames

    For image datasets, this should always be set to video_params": {"num_frames": 1, ...}.

    For video datasets, set this to what you want.
    N.B. More frames requires = more gpu memory.

    If, like us, you are not a big company and have limited compute, then you will benefit by training via a curriculum on the number of frames.
    A lot of the knowledge can be learned in the 1-frame setting, as we show in the paper. You can then finetune with more frames. See curriculum learning section

    c. Finetuning

    Set "load_checkpoint": "FULL_MODEL_PATH" in the config file. You can now use different experiment params, such as num_frames, to do curriculum learning for example.

πŸ—„ Pretrained Weights

πŸ“š Curriculum Learning on #frames

Curriculum learning on the number of frames in pretraining achieves similar performance with significant reduction in compute (both memory and training time). This is because model has higher throughput for fewer frames, as well as allowing a bigger batch size for the same gpu memory.

Our best model was trained on 1-frame then finetuned on 4-frames on CC+WebVid2M.

Train on 1-frame until the training loss converges, then finetune on 4-frames with the same config, from the 1-frame checkpoint via setting load_checkpoint in config file. 4-frame finetuning needs much less iterations (~10% of 1-frame setting is sufficient) since most of the knowledge is learned in the 1-frame setting.

πŸ“ˆ Experiment Logging and Visualising

This repository uses a sacred backbone for logging and tracking experiments, with a neptune front end. It makes life a lot easier.
If you want to activate this:

  1. Create a neptune.ai account.
  2. Create a project, copy in your credentials in train.py and remove the ValueError
  3. Set neptune: true in your config files.

πŸŽ“ Cite

If you use this code in your research, please cite:

@misc{bain2021frozen,
      title={Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval}, 
      author={Max Bain and Arsha Nagrani and GΓΌl Varol and Andrew Zisserman},
      year={2021},
      eprint={2104.00650},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

GitHub

https://github.com/m-bain/frozen-in-time