alchemyannotate 1.2.0

Lightweight desktop image annotation tool for bounding box and polygon labeling with YOLO, Pascal VOC, and COCO export

AlchemyAnnotate - Lightweight Image Annotation Tool for Object Detection

AlchemyAnnotate is a free, open-source, desktop image annotation tool built with Python and PySide6. It provides a fast and intuitive interface for creating bounding box annotations used in training object detection models such as YOLO, Faster R-CNN, and SSD.

Export your annotations in YOLO, Pascal VOC, and COCO formats with a single click.

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Features

• Bounding Box Annotation - Draw, select, edit, and delete bounding boxes directly on images
• Multi-Format Export - Save annotations in YOLO, Pascal VOC (XML), and COCO (JSON) formats
• Separate Annotation Folders - Each format is stored in its own folder (annotations_yolo/, annotations_voc/, annotations_coco/) to prevent overwrites
• Class Management - Create, delete, and assign annotation classes through the UI with automatic color coding
• Autosave - Annotations are saved automatically after every change
• Format Switching - Switch between YOLO, VOC, and COCO at any time with automatic conversion of existing annotations
• Existing Annotation Detection - Automatically detects and loads previously saved annotations when opening a folder
• Project Session File - Remembers your last opened image, selected format, class list, and preferences across sessions
• Image Navigation - Browse images using sidebar, keyboard shortcuts, or arrow keys
• Zoom and Pan - Zoom with Ctrl+Scroll and pan with middle-click for precise annotation on large images
• Sidebar Status - Visual indicators show which images are labeled and which are still pending

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Supported Annotation Formats

Format	Output Folder	File Type	Used By
YOLO	annotations_yolo/	.txt per image + classes.txt	YOLOv5, YOLOv8, Ultralytics
Pascal VOC	annotations_voc/	.xml per image	Faster R-CNN, SSD, Detectron
COCO	annotations_coco/	Single annotations.json	COCO API, Detectron2, MMDetection

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Supported Image Formats

JPG, JPEG, PNG, BMP, TIFF, WEBP

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Installation

Install from PyPI (recommended)

pip install alchemyannotate

Install from source

git clone https://github.com/kouya-marino/AlchemyAnnotate.git
cd AlchemyAnnotate
pip install .

Development install

git clone https://github.com/kouya-marino/AlchemyAnnotate.git
cd AlchemyAnnotate
pip install -e ".[dev]"

Requirements

• Python 3.10 or higher
• PySide6 (installed automatically)

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How to Use

Launch the Application

python run.py

Or run as a Python module:

python -m alchemyannotate

Or if installed as a package:

alchemyannotate

Annotation Workflow

1. Open a Folder - Click File > Open Folder or press Ctrl+O to select a folder containing your images
2. Create Classes - In the right panel, type a class name (e.g., "car", "person") and click Add
3. Select a Class - Click on a class in the class list to make it active
4. Draw Bounding Boxes - Left-click and drag on the image to draw a box around an object
5. Navigate Images - Use the sidebar, or press A / D to go to the previous / next image
6. Edit Boxes - Click a box to select it, then change its class from the dropdown or press Delete to remove it
7. Save - Annotations autosave after every change. Press Ctrl+S to force a manual save
8. Switch Format - Use the format selector in the status bar to switch between YOLO, VOC, and COCO

Keyboard Shortcuts

Shortcut	Action
Ctrl+O	Open image folder
Ctrl+S	Save annotations
Ctrl+Q	Quit application
Delete	Delete selected bounding box
A or Left Arrow	Previous image
D or Right Arrow	Next image
Ctrl+0	Fit image to window
Ctrl+Scroll	Zoom in / out
Middle-click drag	Pan the image

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Project File

When you open a folder, AlchemyAnnotate creates an alchemyannotate_project.json file inside it. This file stores:

• Selected annotation format
• Class list
• Last opened image
• Autosave preferences
• Recently used class

This allows you to resume your annotation session exactly where you left off.

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Project Structure

AlchemyAnnotate/
├── alchemyannotate/
│   ├── models/          # Data models (BoundingBox, ImageAnnotation, ProjectConfig)
│   ├── views/           # UI components (Canvas, Sidebar, Panels, Dialogs)
│   ├── controllers/     # Application logic (App, Canvas, Navigation controllers)
│   ├── services/        # IO, autosave, format conversion, image loading
│   └── utils/           # Constants, geometry helpers
├── tests/               # Unit tests for models, IO formats, and store
├── run.py               # Quick launcher
├── pyproject.toml       # Build configuration
└── requirements.txt     # Dependencies

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Running Tests

pip install -e ".[dev]"
pytest tests/ -v

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Dependencies

Package	Version	Purpose
PySide6	>= 6.6	Qt6 GUI framework

No other external dependencies. XML and JSON handling use Python standard library modules.

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Frequently Asked Questions

What annotation format should I use?

• Use YOLO if you are training with Ultralytics YOLOv5, YOLOv8, or similar YOLO-based models
• Use Pascal VOC if you are training with Faster R-CNN, SSD, or frameworks that expect XML annotations
• Use COCO if you are using Detectron2, MMDetection, or the COCO evaluation API

Can I switch formats after annotating?

Yes. AlchemyAnnotate will prompt you to export existing annotations to the new format when you switch.

Where are my annotations saved?

Inside your image folder, in a subfolder named after the format:

• annotations_yolo/ for YOLO format
• annotations_voc/ for Pascal VOC format
• annotations_coco/ for COCO format

Does it support polygons or segmentation masks?

Yes. AlchemyAnnotate supports both bounding box and polygon annotations. Polygons export as COCO segmentation fields and YOLOv8-seg format. Note: Pascal VOC does not support polygons — a warning is shown when switching to VOC if polygon data exists.

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License

This project is open source. See the LICENSE file for details.

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Contributing

Contributions are welcome. Please open an issue or submit a pull request on GitHub.