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
| { | |
| "epoch": 2.0, | |
| "total_flos": 3.0955504328740454e+18, | |
| "train_loss": 0.010772173270583153, | |
| "train_runtime": 1053.5222, | |
| "train_samples_per_second": 37.917, | |
| "train_steps_per_second": 1.186 | |
| } |