Official PyTorch codebase for the video joint-embedding predictive architecture, V-JEPA, a method for self-supervised learning of visual representations from video.
Adrien Bardes, Quentin Garrido, Jean Ponce, Xinlei Chen, Michael Rabbat, Yann LeCun, Mahmoud Assran*, Nicolas Ballas*
V-JEPA models are trained by passively watching video pixels from the VideoMix2M dataset, and produce versatile visual representations that perform well on downstream video and image tasks, without adaption of the model’s parameters; e.g., using a frozen backbone and only a light-weight task-specific attentive probe.
V-JEPA pretraining is based solely on an unsupervised feature prediction objective, and does not utilize pretrained image encoders, text, negative examples, human annotations, or pixel-level reconstruction.
As opposed to generative methods that have a pixel decoder, V-JEPA has a predictor that makes predictions in latent space. We train a conditional diffusion model to decode the V-JEPA feature-space predictions to interpretable pixels; the pretrained V-JEPA encoder and predictor networks are kept frozen in this process. The decoder is only fed the representations predicted for the missing regions of the video, and does not have access to the unmasked regions of the video.
The V-JEPA feature predictions are indeed grounded, and exhibit spatio-temporal consistency with the unmasked regions of the video.
| model | patch size | resolution | iterations | batch size | data | download | |
|---|---|---|---|---|---|---|---|
| ViT-L | 2x16x16 | 224x224 | 90K | 3072 | VideoMix2M | checkpoint | configs |
| ViT-H | 2x16x16 | 224x224 | 90K | 3072 | VideoMix2M | checkpoint | configs |
| ViT-H | 2x16x16 | 384x384 | 90K | 2400 | VideoMix2M | checkpoint | configs |
| model | resolution | accuracy (16x8x3) | download | |
|---|---|---|---|---|
| ViT-L/16 | 224x224 | 80.8 | attentive probe checkpoint | configs |
| ViT-H/16 | 224x224 | 82.0 | attentive probe checkpoint | configs |
| ViT-H/16 | 384x384 | 81.9 | attentive probe checkpoint | configs |
| model | resolution | accuracy (16x2x3) | download | |
|---|---|---|---|---|
| ViT-L/16 | 224x224 | 69.5 | attentive probe checkpoint | configs |
| ViT-H/16 | 224x224 | 71.4 | attentive probe checkpoint | configs |
| ViT-H/16 | 384x384 | 72.2 | attentive probe checkpoint | configs |
| model | resolution | accuracy | download | |
|---|---|---|---|---|
| ViT-L/16 | 224x224 | 74.8 | attentive probe checkpoint | configs |
| ViT-H/16 | 224x224 | 75.9 | attentive probe checkpoint | configs |
| ViT-H/16 | 384x384 | 77.4 | attentive probe checkpoint | configs |
| model | resolution | accuracy | download | |
|---|---|---|---|---|
| ViT-L/16 | 224x224 | 60.3 | attentive probe checkpoint | configs |
| ViT-H/16 | 224x224 | 61.7 | attentive probe checkpoint | configs |
| ViT-H/16 | 384x384 | 62.8 | attentive probe checkpoint | configs |
| model | resolution | accuracy | download | |
|---|---|---|---|---|
| ViT-L/16 | 224x224 | 67.8 | attentive probe checkpoint | configs |
| ViT-H/16 | 224x224 | 67.9 | attentive probe checkpoint | configs |
| ViT-H/16 | 384x384 | 72.6 | attentive probe checkpoint | configs |
Config files: All experiment parameters are specified in config files (as opposed to command-line arguments). See the configs/ directory for example config files. Note, before launching an experiment, you must update the paths in the config file to point to your own directories, indicating where to save the logs and checkpoints and where to find the training data.
.
├── app # the only place where training loops are allowed
│ ├── vjepa # Video JEPA pre-training
│ ├── main_distributed.py # entrypoint for launching app on slurm cluster
│ └── main.py # entrypoint for launching app locally on your machine for debugging
├── evals # the only place where evaluation of 'apps' are allowed
│ ├── image_classification # training an attentive probe for image classification with frozen backbone
│ ├── video_classification # training an attentive probe for video classification with frozen backbone
│ ├── main_distributed.py # entrypoint for launching distributed evaluations on slurm cluster
│ └── main.py # entrypoint for launching evaluations locally on your machine for debugging
├── src # the package
│ ├── datasets # datasets, data loaders, ...
│ ├── models # model definitions
│ ├── masks # mask collators, masking utilities, ...
│ └── utils # shared utilities
└── configs # the only place where config files are allowed (specify experiment params for app/eval runs)
├── evals # configs for launching vjepa frozen evaluations
└── pretrain # configs for launching vjepa pretraining
V-JEPA pretraining and evaluations work with many standard video formats.
To make a video dataset compatible with the V-JEPA codebase, you simply need to create a .csv file with the following format and then specify the path to this CSV file in your config.
/absolute_file_path.[mp4, webvid, etc.] $integer_class_label
/absolute_file_path.[mp4, webvid, etc.] $integer_class_label
/absolute_file_path.[mp4, webvid, etc.] $integer_class_label
...
Since V-JEPA is entirely unsupervised, the pretraining code will disregard the $integer_class_label in the CSV file.
Thus, feel free to put a random value in this column.
However, if you wish to run a supervised video classification evaluation on your video dataset, you must replace $integer_class_label with the ground truth label for each video.
We use the standard PyTorch ImageFolder class in our image classification evals.
Thus, to set up an image dataset for the image classification evaluation, first create a directory to store your image datasets $your_directory_containing_image_datasets.
Next, download your image datasets into this directory in a format compatible with PyTorch ImageFolder.
For example, suppose we have a directory called my_image_datasets. We would then download our image datasets into this directory so that we end up with the following file tree
.
└── /my_image_datasets/ # where we store image datasets
├── places205/121517/pytorch/ # Places205
│ └── [...]
├── iNaturalist-2021/110421/ # iNaturalist21
│ └── [...]
├── [...] # Other Image Datasets
│ └── [...]
└── imagenet_full_size/061417/ # ImageNet1k
└── train
│ ├── $class_1
│ │ ├── xxx.[png, jpeg, etc.]
│ │ ├── [...]
│ │ └── xxz.[png, jpeg, etc.]
│ ├── [...]
│ └── $class_n
│ ├── abc.[png, jpeg, etc.]
│ ├── [...]
│ └── abz.[png, jpeg, etc.]
└── val
├── $class_1
│ ├── xxx.[png, jpeg, etc.]
│ ├── [...]
│ └── xxz.[png, jpeg, etc.]
├── [...]
└── $class_n
├── abc.[png, jpeg, etc.]
├── [...]
└── abz.[png, jpeg, etc.]
If you wish to debug your code or setup before launching a distributed training run, we provide the functionality to do so by running the pretraining script locally on a multi-GPU (or single-GPU) machine, however, reproducing our results requires launching distributed training.
The single-machine implementation starts from the app/main.py, which parses the experiment config file and runs the pretraining locally on a multi-GPU (or single-GPU) machine. For example, to run V-JEPA pretraining on GPUs "0", "1", and "2" on a local machine using the config configs/pretrain/vitl16.yaml, type the command:
python -m app.main \
--fname configs/pretrain/vitl16.yaml \
--devices cuda:0 cuda:1 cuda:2To launch a distributed training run, the implementation starts from app/main_distributed.py, which, in addition to parsing the config file, also allows for specifying details about distributed training. For distributed training, we use the popular open-source submitit tool and provide examples for a SLURM cluster.
For example, to launch a distributed pre-training experiment using the config configs/pretrain/vitl16.yaml, type the command:
python -m app.main_distributed \
--fname configs/pretrain/vitl16.yaml \
--folder $path_to_save_stderr_and_stdout \
--partition $slurm_partitionIf you wish to debug your eval code or setup before launching a distributed training run, we provide the functionality to do so by running the pretraining script locally on a multi-GPU (or single-GPU) machine, however, reproducing the full eval would require launching distributed training. The single-machine implementation starts from the eval/main.py, which parses the experiment config file and runs the eval locally on a multi-GPU (or single-GPU) machine.
For example, to run ImageNet image classification on GPUs "0", "1", and "2" on a local machine using the config configs/eval/vitl16_in1k.yaml, type the command:
python -m evals.main \
--fname configs/eval/vitl16_in1k.yaml \
--devices cuda:0 cuda:1 cuda:2To launch a distributed evaluation run, the implementation starts from eval/main_distributed.py, which, in addition to parsing the config file, also allows for specifying details about distributed training. For distributed training, we use the popular open-source submitit tool and provide examples for a SLURM cluster.
For example, to launch a distributed ImageNet image classification experiment using the config configs/eval/vitl16_in1k.yaml, type the command:
python -m evals.main_distributed \
--fname configs/eval/vitl16_in1k.yaml \
--folder $path_to_save_stderr_and_stdout \
--partition $slurm_partitionSimilarly, to launch a distributed K400 video classification experiment using the config configs/eval/vitl16_k400.yaml, type the command:
python -m evals.main_distributed \
--fname configs/eval/vitl16_k400.yaml \
--folder $path_to_save_stderr_and_stdout \
--partition $slurm_partitionCreate a new Conda environment, activate it, and run the setup.py script.
See the LICENSE file for details about the license under which this code is made available.
If you find this repository useful in your research, please consider giving a star ⭐ and a citation
@article{bardes2024revisiting,
title={Revisiting Feature Prediction for Learning Visual Representations from Video},
author={Bardes, Adrien and Garrido, Quentin and Ponce, Jean and Rabbat, Michael, and LeCun, Yann and Assran, Mahmoud and Ballas, Nicolas},
journal={arXiv preprint},
year={2024}
}