vjepa2 vitl fpc16 256 ssv2 EU AI Act Compliance Profile

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.

<img src="https://github.com/user-attachments/assets/914942d8-6a1e-409d-86ff-ff856b7346ab"> 

Installation

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

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

Video classification code snippet

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

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Tags

Library

Base Model