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:
- 2084642db2eee1cd6c8de7f1285fb20f6aa2dae92b496a3297a8a081f5cfd331
- Size of remote file:
- 3.44 kB
- SHA256:
- 389e0478e824940000d81b8917ec2e0cbc60acada58430ec0beb900a1665d9c6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.