Instructions to use Devarshi/Brain_Tumor_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Devarshi/Brain_Tumor_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Devarshi/Brain_Tumor_Classification") 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("Devarshi/Brain_Tumor_Classification") model = AutoModelForImageClassification.from_pretrained("Devarshi/Brain_Tumor_Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0582fe2404dd14549ddedd98b10e621e26b506655c5db1fbac54cdee664f19eb
- Size of remote file:
- 110 MB
- SHA256:
- aa057f5f4594c8baa83623ad82e194d4139d97e45f0254a73cca2fdfc1b05218
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.