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--- |
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title: Instance Segmentation Demo |
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emoji: 🖼️ |
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colorFrom: pink |
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colorTo: purple |
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sdk: gradio |
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sdk_version: 5.34.2 |
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app_file: app.py |
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pinned: false |
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--- |
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# 🖼️ Instance Segmentation with Mask2Former |
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This demo performs **advanced instance segmentation** using [Mask2Former](https://huggingface.co/facebook/mask2former-swin-large-coco-instance) from Facebook AI. It identifies and highlights individual objects in an image with: |
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- **Colored masks** |
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- **Bounding boxes** |
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- **Class labels and confidence scores** |
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## 🚀 How It Works |
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- Input an image via upload or example selection. |
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- The app uses the `facebook/mask2former-swin-large-coco-instance` model to detect objects. |
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- Only the following classes are visualized: |
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- `cat`, `dog`, `car`, `truck`, `bus`, `person` |
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- Results are drawn on the image and displayed along with a status message. |
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## 🧠 Model |
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- **Architecture:** Mask2Former with Swin-Large backbone |
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- **Dataset:** COCO Instance |
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- **Framework:** Hugging Face Transformers + PyTorch |
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## 💻 Technologies Used |
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- Python 🐍 |
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- [Gradio](https://gradio.app) for UI |
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- Hugging Face Transformers |
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- PIL & NumPy for image manipulation |
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## 📷 Example Images |
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Try out with example images like: |
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- Cats vs. Dogs |
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- Street scenes with vehicles and people |
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You can also upload your own images! |
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## 📌 Notes |
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- Detection is limited to high-confidence predictions (`score > 0.9`) |
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- This demo is optimized for CPU; inference may take up to 30 seconds. |
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--- |
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## 🛠️ Developer Notes |
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This app uses the following Gradio configuration: |
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```yaml |
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sdk: gradio |
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sdk_version: "4.24.0" |
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app_file: app.py |
