transformers/docs/source/en/model_doc/vjepa2.md

V-JEPA 2

V-JEPA 2 is a self-supervised approach to training video encoders developed by FAIR, Meta. Using internet-scale video data, V-JEPA 2 attains state-of-the-art performance on motion understanding and human action anticipation tasks. V-JEPA 2-AC is a latent action-conditioned world model post-trained from V-JEPA 2 (using a small amount of robot trajectory interaction data) that solves robot manipulation tasks without environment-specific data collection or task-specific training or calibration.

You can find all original V-JEPA2 checkpoints under the V-JEPA 2 collection.

This model was contributed by koustuvs, yonigozlan and qubvel. The original code can be found here.

Usage example

The snippet below shows how to load the V-JEPA 2 model using the AutoModel class.

import torch
from torchcodec.decoders import VideoDecoder
import numpy as np

processor = AutoVideoProcessor.from_pretrained("facebook/vjepa2-vitl-fpc64-256")
model = AutoModel.from_pretrained(
    "facebook/vjepa2-vitl-fpc64-256",
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="sdpa"
)

video_url = "https://huggingface.co/datasets/nateraw/kinetics-mini/resolve/main/val/archery/-Qz25rXdMjE_000014_000024.mp4"

vr = VideoDecoder(video_url)
frame_idx = np.arange(0, 64) # choosing some frames. here, you can define more complex sampling strategy
video = vr.get_frames_at(indices=frame_idx).data  # T x C x H x W
video = processor(video, return_tensors="pt").to(model.device)
outputs = model(**video)

# V-JEPA 2 encoder outputs, same as calling `model.get_vision_features()`
encoder_outputs = outputs.last_hidden_state

# V-JEPA 2 predictor outputs
predictor_outputs = outputs.predictor_output.last_hidden_state

VJEPA2Config

autodoc VJEPA2Config

VJEPA2Model

autodoc VJEPA2Model - forward

VJEPA2VideoProcessor

autodoc VJEPA2VideoProcessor