Instructions to use kmunzwa/medsiglip-diagnosis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kmunzwa/medsiglip-diagnosis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kmunzwa/medsiglip-diagnosis") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("kmunzwa/medsiglip-diagnosis") model = AutoModelForImageClassification.from_pretrained("kmunzwa/medsiglip-diagnosis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
π©Ί 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.
π Model Details
- Task: Image Classification
- Domain: Healthcare β Cervical Cancer Diagnosis
- Framework: Hugging Face Transformers / PyTorch
- Author: Khanyi Tapiwa Magagula (AI Eswatini)
π 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)
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