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autoevaluate
/
image-multi-class-classification

Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Model card Files Files and versions
xet
Metrics Training metrics Community
5

Instructions to use autoevaluate/image-multi-class-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use autoevaluate/image-multi-class-classification with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-classification", model="autoevaluate/image-multi-class-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("autoevaluate/image-multi-class-classification")
    model = AutoModelForImageClassification.from_pretrained("autoevaluate/image-multi-class-classification")
  • Notebooks
  • Google Colab
  • Kaggle
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Librarian Bot: Add base_model information to model

#5 opened over 2 years ago by
librarian-bot

Adding `safetensors` variant of this model

#4 opened about 3 years ago by
SFconvertbot

Add evaluation results on autoevaluate/mnist-sample dataset

#3 opened almost 4 years ago by
lewtun

Add evaluation results on autoevaluate/mnist-sample

#2 opened almost 4 years ago by
abhishek

Add evaluation results on mnist

#1 opened almost 4 years ago by
abhishek
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