Image Classification
Transformers
PyTorch
TensorBoard
deit
Generated from Trainer
Eval Results (legacy)
Instructions to use raedinkhaled/deit-base-mri with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raedinkhaled/deit-base-mri with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="raedinkhaled/deit-base-mri") 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("raedinkhaled/deit-base-mri") model = AutoModelForImageClassification.from_pretrained("raedinkhaled/deit-base-mri") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25fc401c8d7324767797f606c7b81f0eb12aae1b4ac2cb8f134097c9ec74c9be
- Size of remote file:
- 3.31 kB
- SHA256:
- dfe6d09e5485fa6cbb624bc070c6596b9523b32e38868a0a78768d0a8ce86200
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