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:
- bc120f97890c92d36cdba9dc27c9a40e7d455b64e731566f4e66b4b23149f6d2
- Size of remote file:
- 1.99 GB
- SHA256:
- a0cfa27fa4f23361d882bc5b581110aa302c9c79eab8fcad1234e27ad27c10ef
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