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:
- ccca1b1ef849f2df73312ac31fd767e73a868f8b1c6671034dc575891f3bb96a
- Size of remote file:
- 829 MB
- SHA256:
- f1f87d93c4723a522d8f5f76bc192b0f3f349db9a2d95b622065def074b26493
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