vjepa2.1-vitb-fpc64-384
V-JEPA 2.1 ViT-B encoder, distilled from a ViT-G teacher, trained by Meta AI on video self-supervised learning.
About this checkpoint
This is a HuggingFace Transformers conversion of Meta's original V-JEPA 2.1 ViT-B checkpoint. Original weights belong to Meta AI; the license follows Meta's original terms. This checkpoint has been uploaded here pending transfer to Meta's official Hub organization.
- Original repository: https://github.com/facebookresearch/vjepa2
- Paper: V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Model details
| Parameter | Value |
|---|---|
| Architecture | ViT-B encoder |
| Crop size | 384 px |
| Frames per clip | 64 |
| Distilled from | ViT-G teacher |
Usage
import torch
from transformers import AutoVideoProcessor, VJEPA2Model
model_id = "davevanveen/vjepa2.1-vitb-fpc64-384"
processor = AutoVideoProcessor.from_pretrained(model_id)
model = VJEPA2Model.from_pretrained(model_id)
model.eval()
# video: list of PIL images or a (T, H, W, C) numpy array
# Here we use a random tensor as a placeholder
video_frames = torch.randint(0, 256, (64, 384, 384, 3), dtype=torch.uint8).numpy()
inputs = processor(videos=[video_frames], return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
# Patch-level features: (batch, num_patches, hidden_dim)
features = outputs.last_hidden_state
print(features.shape)
Attribution
Original V-JEPA 2 work by Meta AI Research. If you use this model, please cite the original work:
@article{vjepa2_2025,
title = {V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning},
author = {Assran, Mahmoud and others},
year = {2025},
url = {https://github.com/facebookresearch/vjepa2}
}