Instructions to use surajjoshi/swin-tiny-patch4-window7-224-finetuned-brainTumorData with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use surajjoshi/swin-tiny-patch4-window7-224-finetuned-brainTumorData with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="surajjoshi/swin-tiny-patch4-window7-224-finetuned-brainTumorData") 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("surajjoshi/swin-tiny-patch4-window7-224-finetuned-brainTumorData") model = AutoModelForImageClassification.from_pretrained("surajjoshi/swin-tiny-patch4-window7-224-finetuned-brainTumorData", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63943558cd7b43ef73ac95beaf86d67b67c5359d7e418a704b1a5e755f67fb26
- Size of remote file:
- 110 MB
- SHA256:
- 36b7f899bb435a324d72f3c4048d950445467a189eeb8204cfae200748588b1f
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