Image Classification
Transformers
PyTorch
TensorBoard
swinv2
Generated from Trainer
Eval Results (legacy)
Instructions to use amjadfqs/swinv2-tiny-patch4-window8-256-finetuned-brain-tumor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amjadfqs/swinv2-tiny-patch4-window8-256-finetuned-brain-tumor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="amjadfqs/swinv2-tiny-patch4-window8-256-finetuned-brain-tumor") 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/swinv2-tiny-patch4-window8-256-finetuned-brain-tumor") model = AutoModelForImageClassification.from_pretrained("amjadfqs/swinv2-tiny-patch4-window8-256-finetuned-brain-tumor") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 5.0, | |
| "total_flos": 1.1595881296429056e+18, | |
| "train_loss": 0.1904253252960266, | |
| "train_runtime": 7888.6282, | |
| "train_samples_per_second": 4.518, | |
| "train_steps_per_second": 0.03 | |
| } |