Image Classification
Transformers
PyTorch
TensorBoard
efficientnet
Generated from Trainer
Eval Results (legacy)
Instructions to use DunnBC22/efficientnet-b5-Brain_Tumors_Image_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/efficientnet-b5-Brain_Tumors_Image_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DunnBC22/efficientnet-b5-Brain_Tumors_Image_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("DunnBC22/efficientnet-b5-Brain_Tumors_Image_Classification") model = AutoModelForImageClassification.from_pretrained("DunnBC22/efficientnet-b5-Brain_Tumors_Image_Classification") - Notebooks
- Google Colab
- Kaggle
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- Pytorch 2.0.0
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- Datasets 2.11.0
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- Tokenizers 0.13.3
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- Pytorch 2.0.0
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- Datasets 2.11.0
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- Tokenizers 0.13.3
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## License Notice
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This model is a fine-tuned derivative of a pretrained model.
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Users must comply with the original model license.
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## Dataset Notice
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This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions.
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