Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_06 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_06 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_06") 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("amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_06") model = AutoModelForImageClassification.from_pretrained("amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_06") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 9.81, | |
| "total_flos": 4.0507959904511017e+18, | |
| "train_loss": 0.24423522857519298, | |
| "train_runtime": 12543.349, | |
| "train_samples_per_second": 4.199, | |
| "train_steps_per_second": 0.01 | |
| } |