Video Classification
Transformers
TensorBoard
Safetensors
PyTorch
vjepa2
computer-vision
video-understanding
fine-tuned
Instructions to use eagle0504/vjepa2-vitl-fpc16-256-ssv2-ucf101 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eagle0504/vjepa2-vitl-fpc16-256-ssv2-ucf101 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="eagle0504/vjepa2-vitl-fpc16-256-ssv2-ucf101")# Load model directly from transformers import AutoTokenizer, AutoModelForVideoClassification tokenizer = AutoTokenizer.from_pretrained("eagle0504/vjepa2-vitl-fpc16-256-ssv2-ucf101") model = AutoModelForVideoClassification.from_pretrained("eagle0504/vjepa2-vitl-fpc16-256-ssv2-ucf101", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files
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