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
- Xet hash:
- 50b04c80f0fe381fe21122c4b10f489a3c4a3cffaa62a233c6fe9388a67eb770
- Size of remote file:
- 4.03 kB
- SHA256:
- 2fa70183d5ed6cfd50d5899a1a893569b86def8b910ffb25729193c3327e539a
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