---
license: mit
library_name: ultralytics
pipeline_tag: object-detection
tags:
- medical
- biology
- dermatology
- skin-disease
- yolo11
- vision
- healthcare
- transfer-learning
metrics:
- mAP50: 0.85
- mAP50-95: 0.54
- precision: 0.82
- recall: 0.81
model-index:
- name: VeritaDerm
  results:
  - task:
      type: object-detection
    metrics:
    - type: mAP50
      value: 84.5
      name: mAP@0.5
    - type: mAP@0.5:0.95
      value: 54.5
      name: mAP@0.5:0.95
    - type: recall
      value: 81.8
      name: recall
    - type: precision
      value: 83.2
      name: precision
base_model:
- Ultralytics/YOLO11
---

# VeritaDerm 🩺✨


## 📌 Overview
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](https://huggingface.co/arkito/VeritaDerm/resolve/main/Screenshot%202026-02-08%20205252.png)

## 🧬 Supported Classes (8)
The model is trained to identify the following categories:
1. **Acne**
2. **Chicken Skin (Keratosis Pilaris)**
3. **Eczema**
4. **Leprosy**
5. **Psoriasis**
6. **Ringworm**
7. **Warts**
8. **Healthy Skin** (Background/Control)

## 🚀 How to Use
You can run VeritaDerm directly using the `ultralytics` library.

### 1. Install Requirements
```bash
pip install ultralytics
```

### 2. Run Inference
```Python
from ultralytics import YOLO

# Load the model from Hugging Face
model = YOLO("XythicK/veritaderm")

# Predict on an image
results = model.predict(source="path_to_skin_image.jpg", conf=0.25)

# View results
results[0].show()
```
### 🛠️ Training Details
- Hardware: NVIDIA RTX A6000 

- Dataset Size: 5,000 high-resolution dermatological images.

- Optimizer: Auto (SGD/AdamW)

- Epochs: 42 (Optimized)

- 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}}
}
```