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:
- 01b24d83ca45835b06d729c7ae7033ef49c0423e0195be0a2b21c7ddd758fa7c
- Size of remote file:
- 1.99 GB
- SHA256:
- 34dd5889899ae249c80e96f43f02cbbf8704de7621b5bdd03b5be00834993dfa
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