Semi-Auto-Image-Annotation-Tool

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Anno-Mage is an open-source semi-automatic image annotation tool that helps users annotate images by suggesting annotations using user-defined labels. It is powered by PyTorch object detection models, including open-vocabulary zero-shot detection via OWL-v2.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is semi-auto-image-annotation-tool?

Anno-Mage is a semi-automatic image annotation tool designed to streamline the process of labeling images for machine learning applications. It leverages advanced PyTorch object detection models, including the OWL-v2 for open-vocabulary zero-shot detection, to suggest annotations based on user-defined labels. This significantly reduces the manual effort required for data labeling. The tool is available as a web application, built with FastAPI for the backend and React for the frontend, offering a user-friendly interface. It supports output in both CSV and Pascal VOC XML formats, making it compatible with various machine learning workflows. Anno-Mage can be installed via PyPI or run locally, providing flexibility for developers and researchers.

Best used for

Ideal for developers and data scientists who need to efficiently label images, create high-quality datasets for machine learning, and accelerate the annotation process. Especially valuable for those working with object detection models and requiring flexible output formats like CSV and Pascal VOC.

Common actions

annotate images

label data

train models

create datasets

deepfakeautomated workflowcollaborationworkflowslow-code/no-codeface swappingopen-sourcegithub copilot"AI Agents"

Capabilities

Key features

Semi-automatic image annotation
User-defined labels
PyTorch object detection models
OWL-v2 zero-shot detection
Web application interface
CSV and Pascal VOC output

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of object detection models does Anno-Mage use?

Anno-Mage is powered by PyTorch object detection models, including the RetinaNet implementation. It also incorporates open-vocabulary zero-shot detection capabilities through the OWL-v2 model from HuggingFace Transformers, allowing for flexible annotation with user-defined labels.

How do I install and run Anno-Mage?

You can install Anno-Mage from PyPI using `pip install anno-mage` and then run `anno-mage`. Alternatively, you can run the web application locally by navigating to the `web` directory and executing `bash start.sh` after setting up the backend and frontend dependencies.

What output formats are supported for annotations?

Anno-Mage produces annotations in two primary formats. It generates a CSV file (`annotations.csv`) containing image path, bounding box coordinates (x1, y1, x2, y2), and the label for each annotation. Additionally, it creates Pascal VOC XML files, with one XML file per image, stored in `annotations_voc/`.

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