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:
- 2ca5fff39eb24d2164d9521e9b52ad1ed596fa4c57e6e84d4d7567ea0735ba48
- Size of remote file:
- 1.96 GB
- SHA256:
- e3eb6fc261ad8d0341a6cafeb19d8fb39419d16fab9761b8b8385a91f08dc771
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