Name: vjepa2 vitl fpc16 256 ssv2
Author: facebook

Overview

V-JEPA (Video Joint-Embedding Predictive Architecture) represents a shift from generative to predictive pre-training for video understanding. Unlike models that predict every pixel, this ViT-L based architecture learns by predicting missing parts of a video in a latent space, significantly reducing computational overhead while capturing high-level semantic motion. For developers, this means a powerful backbone for downstream video classification and action recognition tasks that generalizes better across diverse datasets. It integrates seamlessly into PyTorch workflows, offering a robust feature extractor for temporal analysis without the noise associated with pixel-level reconstruction.

Highlights

Latent-space prediction reduces computational redundancy
ViT-L backbone optimized for temporal feature extraction
Superior generalization for complex video classification tasks
MIT licensed for flexible commercial integration

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("facebook/vjepa2-vitl-fpc16-256-ssv2")
tokenizer = AutoTokenizer.from_pretrained("facebook/vjepa2-vitl-fpc16-256-ssv2")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download facebook/vjepa2-vitl-fpc16-256-ssv2

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download facebook/vjepa2-vitl-fpc16-256-ssv2 config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('facebook/vjepa2-vitl-fpc16-256-ssv2')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/facebook/vjepa2-vitl-fpc16-256-ssv2

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/facebook/vjepa2-vitl-fpc16-256-ssv2

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('facebook/vjepa2-vitl-fpc16-256-ssv2')
tokenizer = AutoTokenizer.from_pretrained('facebook/vjepa2-vitl-fpc16-256-ssv2')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance

pip install modelscope

CLI Download

Download the full repository

modelscope download --model facebook/vjepa2-vitl-fpc16-256-ssv2

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model facebook/vjepa2-vitl-fpc16-256-ssv2 README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('facebook/vjepa2-vitl-fpc16-256-ssv2')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/facebook/vjepa2-vitl-fpc16-256-ssv2.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/facebook/vjepa2-vitl-fpc16-256-ssv2.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'facebook/vjepa2-vitl-fpc16-256-ssv2')

Full Documentation

来源: HuggingFace

---
license: mit
pipeline_tag: video-classification
tags:

video

library_name: transformers
datasets:

HuggingFaceM4/something_something_v2

base_model:

facebook/vjepa2-vitl-fpc64-256

---

V-JEPA 2

A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of VJEPA, resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale.
The code is released in this repository.

<div style="background-color: rgba(251, 255, 120, 0.4); padding: 10px; color: black; border-radius: 5px; box-shadow: 0 4px 8px rgba(0,0,0,0.1);">
💡 This is V-JEPA 2 <a href="https://huggingface.co/facebook/vjepa2-vitl-fpc64-256">ViT-L 256</a> model with video classification head pretrained on <a href="https://paperswithcode.com/dataset/something-something-v2" style="color: black;">Something-Something-V2</a> dataset.
</div>
<br></br>

Installation

To run V-JEPA 2 model, ensure you have installed the latest transformers:

bash

pip install -U git+https://github.com/huggingface/transformers

Video classification code snippet

python

import torch
import numpy as np
from torchcodec.decoders import VideoDecoder
from transformers import AutoVideoProcessor, AutoModelForVideoClassification
device = "cuda" if torch.cuda.is_available() else "cpu"
Load model and video preprocessor
hf_repo = "facebook/vjepa2-vitl-fpc16-256-ssv2"
model = AutoModelForVideoClassification.from_pretrained(hf_repo).to(device)
processor = AutoVideoProcessor.from_pretrained(hf_repo)
To load a video, sample the number of frames according to the model.
video_url = "https://huggingface.co/datasets/nateraw/kinetics-mini/resolve/main/val/bowling/-WH-lxmGJVY_000005_000015.mp4"
vr = VideoDecoder(video_url)
frame_idx = np.arange(0, model.config.frames_per_clip, 8) # you can define more complex sampling strategy
video = vr.get_frames_at(indices=frame_idx).data  # frames x channels x height x width
Preprocess and run inference
inputs = processor(video, return_tensors="pt").to(model.device)
with torch.no_grad():
    outputs = model(**inputs)
logits = outputs.logits
print("Top 5 predicted class names:")
top5_indices = logits.topk(5).indices[0]
top5_probs = torch.softmax(logits, dim=-1).topk(5).values[0]
for idx, prob in zip(top5_indices, top5_probs):
    text_label = model.config.id2label[idx.item()]
    print(f" - {text_label}: {prob:.2f}")

Output:

code

Top 5 predicted class names:
 - Stuffing [something] into [something]: 0.34
 - Putting [something] into [something]: 0.25
 - Putting [something] onto [something]: 0.04
 - Spreading [something] onto [something]: 0.04
 - Closing [something]: 0.03

Citation

code

@techreport{assran2025vjepa2,
  title={V-JEPA~2: Self-Supervised Video Models Enable Understanding, Prediction and Planning},
  author={Assran, Mahmoud and Bardes, Adrien and Fan, David and Garrido, Quentin and Howes, Russell and
  Komeili, Mojtaba and Muckley, Matthew and Rizvi, Ammar and Roberts, Claire and Sinha, Koustuv and Zholus, Artem and
  Arnaud, Sergio and Gejji, Abha and Martin, Ada and Robert Hogan, Francois and Dugas, Daniel and
  Bojanowski, Piotr and Khalidov, Vasil and Labatut, Patrick and Massa, Francisco and Szafraniec, Marc and
  Krishnakumar, Kapil and Li, Yong and Ma, Xiaodong and Chandar, Sarath and Meier, Franziska and LeCun, Yann and
  Rabbat, Michael and Ballas, Nicolas},
  institution={FAIR at Meta},
  year={2025}
}