Instructions to use SeyedAli/Melanoma-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SeyedAli/Melanoma-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SeyedAli/Melanoma-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("SeyedAli/Melanoma-Classification") model = AutoModelForImageClassification.from_pretrained("SeyedAli/Melanoma-Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 053ea895bd4baa0ba60d70d97196cb46756ebb9b4aec2d238eded6955e4cd164
- Size of remote file:
- 4.92 kB
- SHA256:
- 94bf4cc7500dcc931037f77b38e40227f16449a080b18acb41cb69b3e754fba7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.