ModelPublicadoExterno

Medsiglip Diagnosis

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.

Fuentehuggingface.co/KhanyiTapiwa00/medsiglip-diagnosis/tree/a635f36a1738b1e0bbc40f9b80dadaeaebf79da4

TipoText modelVersiónmodel

Fuente del modelo

Tipo: Text model
Tamaño del archivo: 3,27 GB
Versión: vmodel
Actualizado: 17 sept 2026
Descargas: 115
Fuente: Hugging Face

Descripción de la fuente

🩺 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)

Fuentes

1 fuente

Hugging FacePrincipalVersión a635f36a1738b1e0bbc40f9b80dadaeaebf79da4

huggingface.co/KhanyiTapiwa00/medsiglip-diagnosis/tree/a635f36a1738b1e0bbc40f9b80dadaeaebf79da4

Verificado 17 sept

Artefactos del modelo

1 artefacto

model.safetensors

safetensors · 3,27 GB · SHA-256 69d2e0293b9b…0929 · Hugging Face

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Extractos de fuentes

2 extractos

Readme17 septhuggingface.co/KhanyiTapiwa00/medsiglip-diagnosis

--- 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: ```python 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)

Note17 septhuggingface.co/KhanyiTapiwa00/medsiglip-diagnosis

Source context: 115 downloads · 0 likes · Pipeline image-classification · Library transformers · Repo KhanyiTapiwa00/medsiglip-diagnosis