metadata
language: en
library_name: transformers
pipeline_tag: image-classification
tags:
- vision
- cervical-cancer
- diagnosis
license: apache-2.0
🩺 MedSigLip Diagnosis Model
This repository contains MedSigLip, a deep learning model for cervical cancer image diagnosis.
It takes colposcopy images as input and predicts the most likely stage/class of the condition.
📊 Model Details
- Task: Image Classification
- Domain: Healthcare – Cervical Cancer Diagnosis
- Framework: Hugging Face Transformers / PyTorch
- Author: Khanyi Tapiwa Magagula (AI Eswatini)
🚀 Inference API
Once the Inference API is enabled, you can run predictions without any setup. Example:
from huggingface_hub import InferenceClient
# Replace with your repo name
client = InferenceClient("KhanyiTapiwa00/medsiglip-diagnosis")
# Run image classification
result = client.image_classification("1_10.jpg")
print(result)