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:
- ea04f4e66128ed48d91d8cb894d0b38540245c89eb290e0f1206fc7ba5169c30
- Size of remote file:
- 96.1 MB
- SHA256:
- b90e07a0bd2fea8682855698207e3ec209e7da79ea6c27aebfdfce29f8561f05
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