metadata

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 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 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:

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