Instructions to use prithivMLmods/BrainTumor-Classification-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/BrainTumor-Classification-Mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/BrainTumor-Classification-Mini") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/BrainTumor-Classification-Mini") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/BrainTumor-Classification-Mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eed1510386bbc842b078369cb2ee50bfd12df403f51300620ebe55dc26f6b33e
- Size of remote file:
- 687 MB
- SHA256:
- 738f51692d2eb41381d20fdc381de11bd85e142af7438ca7aaafe9777686fd4b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.