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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from amjadfqs/swinv2-tiny-patch4-window8-256-finetuned-brain-tumor: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/amjadfqs/swinv2-tiny-patch4-window8-256-finetuned-brain-tumor/resolve/main/training_args.bin
- Command line
-
hf download hf://amjadfqs/swinv2-tiny-patch4-window8-256-finetuned-brain-tumor/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/amjadfqs/swinv2-tiny-patch4-window8-256-finetuned-brain-tumor/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 57bc1f4d467b90f8241510ef13c892f07c56602a85c3c44000182604aa3d30ab
- Size of remote file:
- 3.58 kB
- SHA256:
- 463dc9fb5c626252844befc87b082988342f5ef240523b80d876638d75450e80
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