maskrcnn-vision-suite-detection-segmentation

A complete Mask R-CNN pipeline for object detection and instance segmentation on the COCO dataset, with visualization and mAP evaluation using PyTorch.

🧠 Mask R-CNN Object Detection and Instance Segmentation with mAP Evaluation

Welcome to the Mask R-CNN Vision Suite! This project demonstrates object detection and instance segmentation using the pretrained Mask R-CNN (ResNet-50 FPN) model on the COCO val2017 dataset. The system visualizes predictions, applies segmentation masks, and evaluates performance using mAP (mean Average Precision) via the COCO evaluation API.

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📌 Overview

The Mask R-CNN Vision Suite project aims to showcase a complete end-to-end pipeline for object detection and instance segmentation using a pretrained model. It loads the COCO validation dataset, performs inference, visualizes results with masks and bounding boxes, and computes evaluation metrics using pycocotools.

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🧱 Project Structure

The project is organized into clear, functional components for a smooth workflow:

• Notebook: A Jupyter Notebook that guides you through loading data, running inference, and evaluating results.
• Setup: Instructions for installing dependencies and downloading the COCO dataset.
• Model: Utilizes PyTorch's pretrained maskrcnn_resnet50_fpn from torchvision.models.detection.
• Evaluation: Uses COCOeval from pycocotools for calculating mAP scores.
• Visualization: Displays bounding boxes and masks over real COCO images using OpenCV and Matplotlib.

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🌐 Key Features

• Object Detection with bounding boxes
• Instance Segmentation using mask overlays
• mAP Evaluation using COCO API (pycocotools)
• Pretrained Mask R-CNN (ResNet-50 FPN backbone)
• Visualization with OpenCV + Matplotlib
• Auto-download COCO annotations & images

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⚙️ Setup

Follow these steps to get started:

1. Clone the Repository

git clone "https://github.com/nshahmeer-ai/maskrcnn-vision-suite-detection-segmentation"
cd maskrcnn-object-detection-segmentation

2. Create Virtual Environment (Recommended)

python -m venv maskrcnn-env
source maskrcnn-env/bin/activate  # for Linux/macOS
maskrcnn-env\Scripts\activate     # for Windows

3. Install Requirements

pip install -r requirements.txt

4. Launch Notebook

jupyter notebook

Open MaskRCNN_COCO_Inference.ipynb and follow the cells step by step.

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📂 Dataset Download

The notebook will automatically download:

• COCO 2017 Annotations (instances_val2017.json)
• COCO Validation Images (val2017)

If already present, the download is skipped.

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📊 mAP Evaluation

After inference, the results are saved in coco_results.json.
Then COCOeval from pycocotools is used to calculate:

• AP@[IoU=.50:.95] (mAP)
• AP@IoU=0.5
• AR (Average Recall)

These are standard metrics used for benchmarking models on COCO.

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📸 Visualization Sample

Bounding boxes and masks are drawn over original images:

• Green rectangles: Detected objects
• Red masks: Segmented regions
• White text: Class IDs

Matplotlib is used to render the final visualization.

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🧠 Technologies Used

• PyTorch and TorchVision
• Mask R-CNN (ResNet-50 FPN)
• pycocotools (COCO evaluation API)
• OpenCV, Matplotlib, Pillow, NumPy

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🧪 Usage

To test your own image or a different COCO image, change the image ID in the notebook:

img_ids = coco.getImgIds()
img_info = coco.loadImgs(img_ids[0])[0]  # change index for different image

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Contributing

Star this repo Fork and PR improvements Report issues or bugs Add new model integrations or features Open to:

Remote roles (ML/DL Engineer, CV Specialist) Research internships or collaborations Open-source AI/ML contributions

About the Author Shahmeer Nawaz Master's Student in Artificial Intelligence

Email: shahmeernawazai@gmail.com GitHub: https://github.com/nshahmeer-ai LinkedIn: https://www.linkedin.com/in/shahmeernawazai