Image Classification
Transformers
LiteRT
ONNX
Safetensors
English
siglip
zero-shot-image-classification
vision
cervical-cancer
diagnosis
Instructions to use KhanyiTapiwa00/medsiglip-diagnosis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KhanyiTapiwa00/medsiglip-diagnosis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="KhanyiTapiwa00/medsiglip-diagnosis") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("KhanyiTapiwa00/medsiglip-diagnosis") model = AutoModelForZeroShotImageClassification.from_pretrained("KhanyiTapiwa00/medsiglip-diagnosis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3cd66e252373a38a8aa100d94101143b4b6ed238096955d3102d597c9c0f86e
- Size of remote file:
- 96.5 MB
- SHA256:
- 3190baf341387dc0a36521f586c3dd4b8310695292d215ad8274689c7ed9faff
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