README.md

metadata

title: Detection + Segmentation Studio
emoji: 🧠
colorFrom: yellow
colorTo: red
sdk: gradio
sdk_version: 6.12.0
app_file: app.py
python_version: '3.10'
fullWidth: true
header: default
short_description: FCN, R-CNN family, Mask R-CNN, and live comparison demo.
tags:
  - gradio
  - computer-vision
  - semantic-segmentation
  - object-detection
  - instance-segmentation
  - education

Detection + Segmentation Studio

An interaction-first Hugging Face Space built from the lecture 09 Detection + Segmentation.

What is inside

• FCN semantic segmentation with upload, overlay, class coverage, and legend
• R-CNN / Fast R-CNN / Faster R-CNN teaching studio
• Real Faster R-CNN live inference on uploaded images
• Real Mask R-CNN instance segmentation on uploaded images
• Public-metric and live-runtime comparison panel
• PDF report generation for submission

Run locally

python app.py

The default local address is http://127.0.0.1:7860.

Generate the PDF report

python tools/generate_detection_report.py

This creates:

• deliverables/Detection_Segmentation_Vibe_Report.pdf
• deliverables/design_prompt.txt

Deploy to Hugging Face Spaces

Create a write-enabled token at Hugging Face token settings, then run:

$env:HF_TOKEN="your_token"
python deploy_hf_space.py --repo-id your-username/detection-segmentation-studio

The public Space URL will be:

https://huggingface.co/spaces/your-username/detection-segmentation-studio

Notes

• Faster R-CNN and Mask R-CNN are real pretrained inference demos.
• R-CNN and Fast R-CNN are interactive teaching simulations based on the lecture pipeline.
• The benchmark panel mixes original paper-era VOC results and current TorchVision weight-card metrics, so the app shows each method's benchmark setting explicitly.
• The app uses lazy model loading so the interface can still start before heavy models are downloaded.