Coco Annotator

Abstract

1. Introduction

COCO Annotator is a web-based image annotation tool designed for versatility and efficiently label images to create training data for image localization and object detection. It provides many distinct features including the ability to label an image segment (or part of a segment), track object instances, labeling objects with disconnected visible parts, efficiently storing and export annotations in the well-known COCO format. The annotation process is delivered through an intuitive and customizable interface and provides many tools for creating accurate datasets.

Checkout the video for a basic guide on installing and using COCO Annotator.

Several annotation tools are currently available, with most applications as a desktop installation. Once installed, users can manually define regions in an image and creating a textual description. Generally, objects can be marked by a bounding box, either directly, through a masking tool, or by marking points to define the containing area. _COCO Annotator_ allows users to annotate images using free-form curves or polygons and provides many additional features were other annotations tool fall short.

2. Objective

:pencil2: Web-based image segmentation tool for object detection, localization, and keypoints

This project demonstrates how Vue, Machine Learning, Computer Vision can be applied to a real-world AI & Machine Learning problem.

3. Key Features / Modules

• Directly export to COCO format
• Segmentation of objects
• Ability to add key points
• Useful API endpoints to analyze data
• Import datasets already annotated in COCO format
• Annotate disconnect objects as a single instance
• Labeling image segments with any number of labels simultaneously
• Allow custom metadata for each instance or object
• Advanced selection tools such as, DEXTR, MaskRCNN and Magic Wand
• Annotate images with semi-trained models

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Node.js (LTS) and npm
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/jsbroks/coco-annotator.git
cd coco-annotator

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by jsbroks and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.