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:
- 3af3655609412047cb8328be790f3e303f82ddf9556d69dac62880c21abef4b1
- Size of remote file:
- 114 MB
- SHA256:
- 2beed3138079b4f8716c2fc93c3bc8cfa6f95c5f7c0dfebc003de8852d368891
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