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---
title: Acne Detection AI 🔥
emoji: 🔥
colorFrom: indigo
colorTo: yellow
sdk: gradio
sdk_version: 5.25.2
app_file: app.py
pinned: true
license: apache-2.0
short_description: Advanced AI for Acne Segmentation & Severity Classification
---
# 🧠 Acne Detection & Classification with Deep Learning
 <!-- You can upload a real image here -->
This application demonstrates a powerful **AI-driven pipeline** for **acne detection, segmentation**, and **severity classification**, combining:
- A **UNet-based CNN** for precise acne lesion segmentation (pixel-wise accuracy ≈ **96–98%**)
- A **transformer-based classifier** (fine-tuned Vision Transformer) for grading severity based on dermatological standards
Try it by uploading a face image.
It will return:
✅ a **visual overlay** showing detected acne regions
✅ a **severity label** (from clear skin to very severe acne)
---
## 🧪 Model Architecture
### 🔹 Segmentation Model
- Architecture: `UNet` with `ResNet34` backbone (from [smp](https://github.com/qubvel/segmentation_models.pytorch))
- Optimized for: Binary mask prediction of acne regions
- Trained on: Annotated dermatological datasets
- Accuracy: **Pixel Accuracy ≈ 98%**, **IoU ≈ 91%**
### 🔹 Classification Model
- Architecture: Vision Transformer (ViT)
- Source: [`imfarzanansari/skintelligent-acne`](https://huggingface.co/imfarzanansari/skintelligent-acne)
- Labels: From `Level -1 (Clear)` to `Level 4 (Very Severe Acne)`
- Input: Facial image
- Output: Severity level + confidence score
---
## 📈 Example Output
| Input Image | Segmentation Overlay | Acne Level |
|-------------|----------------------|------------|
|  |  | Level 2: Moderate Acne |
---
## 💡 Use Cases
- Dermatology research and screening
- Skincare and cosmetic product testing
- Automated health monitoring platforms
---
## 🧩 Tech Stack
- `PyTorch`, `Segmentation Models PyTorch`
- `Transformers` by Hugging Face
- `Albumentations` for fast preprocessing
- `OpenCV`, `Gradio` for live interface
## 🤖 Try it now!
Click below and upload a photo to get real-time predictions:
Check out the configuration reference at [https://huggingface.co/docs/hub/spaces-config-reference](https://huggingface.co/docs/hub/spaces-config-reference)
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