Awesome
Video Noise Contrastive Estimation (VINCE)
This is a repository containing code used to implement the models in the paper Watching the World Go By: Representation Learning from Unlabeled Videos (https://arxiv.org/abs/2003.07990).
<img src="https://danielgordon10.github.io/images/projects/vince.jpg" height="500"/>Environment Setup
We recommend using Anaconda
to manage your environment setup and run our code.
The following commands will create an environment similar to ours with minimal requirements.
Conda
conda create -n video-env python=3.6.8
conda deactivate
conda env update -n video-env -f env.yml
conda activate video-env
pip install git+https://github.com/danielgordon10/dg_util.git -U
Virtualenv
If you instead prefer virtualenv
or similar, we have also provided a requirements.txt.
virtualenv --python=python3.6 video-env
source video-env/bin/activate
pip install -r requirements.txt
Downlaod Random Related Video Views (R2V2)
Due to budgetary constraints, I can no longer directly host the dataset directly, however I have made available a script to recreate the dataset. Note however that many of the original videos have since been deleted from youtube, so their data cannot be recreated. If you are interested in hosting the dataset for me, please contact me.
Recreate the dataset
- Ensure you have set up the conda environment and installed
dg_util.git
as noted in Conda - Follow instructions to create cookies.txt
- Run
python download_scripts/recreate_r2v2_dataset.py
Notes
Original Dataset:
Size (GB) | Number of Files | Number of Images | Number of Folders | Number of Source Videos | |
---|---|---|---|---|---|
Train | 110 | 2,788,424 | 2,784,328 | 4096 | 696,082 |
Val | 8.8 | 226,620 | 222,524 | 4096 | 55,631 |
- Some folders have many more images than others. This is expected.
- The video and frame ids are also provided in datasets/info_files/r2v2_ids_train.txt and datasets/info_files/r2v2_ids_val.txt
Downloading your own set of YouTube videos
If you would like to download a different set of YouTube videos, you may still find our code helpful. Here is a basic workflow for downloading many YouTube videos.
- Follow instructions to create cookies.txt
- Create a list of many YouTube URLs to download.
- One option would be to use youtube_scrape/search_youtube_for_urls.py
- Another would be YouTube-8m URLs (https://github.com/danielgordon10/youtube8m-data)
- Run
python run_cache_video_dataset.py --title cache --description caching --num-workers 100
after appropriately formatting the files.- Note - You can often use more workers than your CPU has threads because YouTube downloading tends to be the bottleneck.
- youtube_scrape/download_kinetics.py is a convenient file for downloading Kinetics videos.
Create cookies.txt
- Follow instructions at https://apple.stackexchange.com/a/349759
- Go to any youtube video: https://www.youtube.com/watch?v=AKQE9RyOIMY
- Click the extension icon and save the data into
youtube_scrape/cookies.txt
.
Training
Train VINCE
- Download R2V2 training data or create your own dataset to train on.
- Read over the arguments list in arg_parser.py.
- Train the model. We have provided an example train script as well as a debug script to check everything is working. Edit the paths in the file to point to your data/output locations.
Train baselines
- The official MoCo baseline is available at https://github.com/facebookresearch/moco, but for our work, we wrote our own version.
- We have provided an example train script to train this model.
- We additionally include MoCoV2 baseline scripts for ResNet50 at vince/train_moco_v2.sh.
- We additionally include the Jigsaw method from PIRL and an accompanying script vince/train_vince_jigsaw.sh. Pretrained weights and results are currently not provided.
Train End Task
- We include various end tasks and an interface for easily adding more. Training scripts for each task are available at:
- New end tasks can be added by creating a new solver which inherits from EndTaskBaseSolver and an accompanying dataset which inherits from BaseDataset.
Evaluation
- While training each end task, evaluation is done after every epoch on a val set.
- If more evaluation is needed, it can be added by implementing
run_eval
for that solver. For an example, see solvers/end_task_tracking_solver.py and end_tasks/eval_tracking.sh.
Download Pretrained Weights
Pretrained weights are available for VINCE as well as all baselines mentioned in the paper. We provide the pretrained weights for the backbone only, not for any end task.
ResNet18
To download the weights, from the root directory, run sh download_scripts/download_pretrained_weights_resnet18.sh
Alternatively, download them directly from https://drive.google.com/uc?id=1L2SZvsvpxe-A1gCN9Nxg9LwB_d604aQf
ResNet50
These models were trained using the hyperparameters in https://arxiv.org/abs/2003.04297 except for batch size which was 896 (starting loss was scaled proportionally to 0.105).
To download the weights, from the root directory, run sh download_scripts/download_pretrained_weights_resnet50.sh
Alternatively, download them directly from https://drive.google.com/uc?id=11TfKfZLLx2FYCATjkll5nUIOxSgSBWGi
Benchmark Results
The results you achieve should somewhat match the table below, though different learning schedules and other factors may slightly change performance.
Method Name (In Paper) | Dir Name | Backbone | ImageNet | Sun Scenes | Kinetics 400 | OTB 2015 Precision | OTB 2015 Success |
---|---|---|---|---|---|---|---|
Sup-IN | N/A | ResNet18 | 0.696 | 0.491 | 0.207 | 0.557 | 0.396 |
MoCo-IN | moco-in | ResNet18 | 0.447 | 0.487 | 0.336 | 0.583 | 0.429 |
MoCo-G | moco-g | ResNet18 | 0.393 | 0.444 | 0.313 | 0.511 | 0.413 |
MoCo-R2V2 | moco-r2v2 | ResNet18 | 0.358 | 0.450 | 0.318 | 0.555 | 0.403 |
VINCE | vince-r2v2-multi-frame-multi-pair | ResNet18 | 0.400 | 0.495 | 0.362 | 0.629 | 0.465 |
Sup-IN | N/A | ResNet50 | 0.762 | 0.593 | 0.305 | 0.458 | 0.320 |
MoCo-V2-IN | moco-v2-in | ResNet50 | 0.652 | 0.608 | 0.459 | 0.300 | 0.260 |
MoCo-R2V2 | moco-v2-r2v2 | ResNet50 | 0.536 | 0.581 | 0.456 | 0.386 | 0.299 |
VINCE | vince-r2v2-multi-frame-multi-pair | ResNet50 | 0.544 | 0.611 | 0.491 | 0.402 | 0.300 |
Citation
@misc{gordon2020watching,
title={Watching the World Go By: Representation Learning from Unlabeled Videos},
author={Gordon, Daniel and Ehsani, Kiana and Fox, Dieter and Farhadi, Ali},
year={2020},
eprint={2003.07990},
archivePrefix={arXiv},
primaryClass={cs.CV}
}