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digo-prayudha
/
vit-emotion-classification

Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use digo-prayudha/vit-emotion-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use digo-prayudha/vit-emotion-classification with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-classification", model="digo-prayudha/vit-emotion-classification")
    pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")
    # Load model directly
    from transformers import AutoImageProcessor, AutoModelForImageClassification
    
    processor = AutoImageProcessor.from_pretrained("digo-prayudha/vit-emotion-classification")
    model = AutoModelForImageClassification.from_pretrained("digo-prayudha/vit-emotion-classification", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
vit-emotion-classification
343 MB
Ctrl+K
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  • 1 contributor
History: 4 commits
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Update README.md
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  • .gitattributes
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  • README.md
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  • all_results.json
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  • config.json
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  • eval_results.json
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  • model.safetensors
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  • preprocessor_config.json
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  • train_results.json
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  • trainer_state.json
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  • training_args.bin
    5.3 kB
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