VeritaDerm is a high-performance computer vision model designed for the automated detection and classification of common dermatological conditions. Trained on a curated dataset of 5,000 images, VeritaDerm leverages the latest YOLO11 architecture to provide a balance between real-time inference speed and clinical accuracy.
This model is intended to assist in research and act as a preliminary screening tool for identifying dermatological patterns in digital imagery.
📊 Performance Metrics
The model achieved the following results on the validation set after rigorous training on an NVIDIA RTX A6000:
Metric
Value
mAP@.5
85.4%
mAP@.5-.95
54.5%
Precision
82.2%
Recall
81.8%
Inference Speed
~4.7ms (on RTX A6000)
VeritaDerm Screenshot
🧬 Supported Classes (8)
The model is trained to identify the following categories:
Acne
Chicken Skin (Keratosis Pilaris)
Eczema
Leprosy
Psoriasis
Ringworm
Warts
Healthy Skin (Background/Control)
🚀 How to Use
You can run VeritaDerm directly using the ultralytics library.
1. Install Requirements
pip install ultralytics
2. Run Inference
Python
1from ultralytics import YOLO
23# Load the model from Hugging Face
4model = YOLO("XythicK/veritaderm")
56# Predict on an image
7results = model.predict(source="path_to_skin_image.jpg", conf=0.25)
89# View results
10results[0].show()
Augmentations: Mosaic, Mixup, and HSV-adjustments used to enhance generalizability.
⚠️ Medical Disclaimer
VeritaDerm is provided for educational and research purposes only. It is NOT a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of a qualified dermatologist or healthcare provider with any questions you may have regarding a medical condition.
✉️ Contact & Citation
If you use this model in your research or project, please credit the author:
@misc{xythick2026veritaderm,
author = {M Mashhudur Rahim},
title = {VeritaDerm: A Diagnostic Framework for Multi-Class Skin Disease Detection},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/XythicK/veritaderm}}
}
