Skin Disease Prediction Experimental

🚀 Usage Example

import torch
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch.nn.functional as F

model_path = "Ateeqq/skin-disease-prediction-exp-v1"
processor = AutoImageProcessor.from_pretrained(model_path)
model = SiglipForImageClassification.from_pretrained(model_path)

image_path = r"/content/download.jpg"
image = Image.open(image_path).convert("RGB")
inputs = processor(images=image, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits
probabilities = F.softmax(logits, dim=1)

predicted_class_id = logits.argmax().item()
predicted_class_label = model.config.id2label[predicted_class_id]
confidence_scores = probabilities[0].tolist()

print(f"Predicted class ID: {predicted_class_id}")
print(f"Predicted class label: {predicted_class_label}\n")
for i, score in enumerate(confidence_scores):
    label = model.config.id2label[i]
    print(f"Confidence for '{label}': {score:.6f}")

Output

Predicted class ID: 5
Predicted class label: Warts Molluscum and other Viral Infections

Confidence for 'Atopic Dermatitis': 0.000061
Confidence for 'Eczema': 0.000006
Confidence for 'Psoriasis pictures Lichen Planus and related diseases': 0.000385
Confidence for 'Seborrheic Keratoses and other Benign Tumors': 0.000000
Confidence for 'Tinea Ringworm Candidiasis and other Fungal Infections': 0.000000
Confidence for 'Warts Molluscum and other Viral Infections': 0.999548

📊 Training Metrics

📌 Confusion Matrix

Dataset

https://www.kaggle.com/datasets/ismailpromus/skin-diseases-image-dataset