---
title: Instance Segmentation Demo
emoji: 🖼️
colorFrom: pink
colorTo: purple
sdk: gradio
sdk_version: 5.34.2
app_file: app.py
pinned: false
---

# 🖼️ Instance Segmentation with Mask2Former

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:
- **Colored masks**
- **Bounding boxes**
- **Class labels and confidence scores**

## 🚀 How It Works

- Input an image via upload or example selection.
- The app uses the `facebook/mask2former-swin-large-coco-instance` model to detect objects.
- Only the following classes are visualized:
  - `cat`, `dog`, `car`, `truck`, `bus`, `person`
- Results are drawn on the image and displayed along with a status message.

## 🧠 Model

- **Architecture:** Mask2Former with Swin-Large backbone
- **Dataset:** COCO Instance
- **Framework:** Hugging Face Transformers + PyTorch

## 💻 Technologies Used

- Python 🐍
- [Gradio](https://gradio.app) for UI
- Hugging Face Transformers
- PIL & NumPy for image manipulation

## 📷 Example Images

Try out with example images like:
- Cats vs. Dogs
- Street scenes with vehicles and people

You can also upload your own images!

## 📌 Notes

- Detection is limited to high-confidence predictions (`score > 0.9`)
- This demo is optimized for CPU; inference may take up to 30 seconds.

---

## 🛠️ Developer Notes

This app uses the following Gradio configuration:

```yaml
sdk: gradio
sdk_version: "4.24.0"
app_file: app.py