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
- Xet hash:
- 4607e49ff22c6118f0e225d75b4d08bbc740e3cecd21fc8d9ec709549f4ec0ab
- Size of remote file:
- 114 MB
- SHA256:
- fb4c6d3090b3b7c29e33c6d950933024b8c98131d56bedd5ee7744d73a5a63c7
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