Video Classification
Transformers
Safetensors
vjepa21
feature-extraction
video
vjepa
vjepa2
v-jepa-2.1
self-supervised
world-model
custom_code
Instructions to use apiantonio/vjepa2.1-vit-gigantic-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apiantonio/vjepa2.1-vit-gigantic-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="apiantonio/vjepa2.1-vit-gigantic-384", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("apiantonio/vjepa2.1-vit-gigantic-384", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 48e19bb19f1b1e4e1b475fa6c87cddc245ba5024221bed7c950de71ec54ae2a8
- Size of remote file:
- 1.67 GB
- SHA256:
- 68bc4b848e52d528a763a8abe14afecdf36d72ccd3578450c4a848866c4d4d53
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