| | --- |
| | 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) | |
| |
|
| |  |
| |
|
| | ## 🧬 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}} |
| | } |
| | ``` |
