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eagle0504
/
vjepa2-vitl-fpc16-256-ssv2-ucf101

Video Classification
Transformers
TensorBoard
Safetensors
PyTorch
vjepa2
computer-vision
video-understanding
fine-tuned
Model card Files Files and versions
xet
Metrics Training metrics Community

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
vjepa2-vitl-fpc16-256-ssv2-ucf101
1.5 GB
Ctrl+K
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  • 1 contributor
History: 5 commits
eagle0504's picture
eagle0504
Update README.md
f2f3210 verified about 1 year ago
  • runs
    Upload folder using huggingface_hub about 1 year ago
  • .gitattributes
    1.52 kB
    initial commit about 1 year ago
  • README.md
    8.12 kB
    Update README.md about 1 year ago
  • config.json
    1.41 kB
    Upload VJEPA2ForVideoClassification about 1 year ago
  • model.safetensors
    1.5 GB
    xet
    Upload VJEPA2ForVideoClassification about 1 year ago
  • video_preprocessor_config.json
    683 Bytes
    Upload processor about 1 year ago