| --- |
| title: Facial Keypoints Detection |
| emoji: 🚀 |
| colorFrom: red |
| colorTo: red |
| sdk: docker |
| app_port: 8501 |
| tags: |
| - streamlit |
| pinned: false |
| short_description: Streamlit template space |
| --- |
| |
| # 🧠 Facial Keypoints Detection |
|
|
| A Streamlit application that predicts facial keypoints using a trained ResNet model. |
|
|
| --- |
|
|
| ## 📌 What this app does |
|
|
| - Upload a face image |
| - Converts image to grayscale |
| - Resizes to 96x96 |
| - Predicts facial keypoints (eyes, nose, mouth, etc.) |
| - Visualizes the keypoints directly on the image |
|
|
| --- |
|
|
| ## 🖼 Example Workflow |
|
|
| 1. Upload a front-facing face image |
| 2. Model predicts keypoints |
| 3. Red dots appear on facial landmarks |
|
|
| --- |
|
|
| ## 🏗 Model |
|
|
| - Architecture: ResNet-based CNN |
| - Input shape: 96x96 grayscale |
| - Output: (x, y) coordinates for facial landmarks |
| - Loss used during training: MSE |
|
|
| --- |
|
|
| ## 🛠 Tech Stack |
|
|
| - Streamlit |
| - TensorFlow / Keras |
| - NumPy |
| - Matplotlib |
| - Pillow |
|
|
| --- |
|
|
| ## 🚀 Run locally |
|
|
| ```bash |
| pip install -r requirements.txt |
| streamlit run src/streamlit_app.py |
| |
