Click here to try! (any images you "upload" stay on your machine, nothing is really uploaded)
This is a fairly basic tool to label object detection datasets.
Despite running in a webbrowser, the app works entirely local.
Do note that things might break, it's not well-tested yet. Please let me know if you find issues or have suggestions for improvement.
There are no dependencies on docker or 42 circularily dependent python packages.
The only thing you need is a not completely-outdated webbrowser. Needless to say, Internet Explorer is not supported, but Edge should work fine.
The tool looks like this:
You may:
- Customize what classes to use
- Upload an additional CSV file that displays accompanying classification data
- Import existing bounding boxes via MegaDetector or COCO file format
It supports:
- Copy the labels of the previous image (useful for image sequences with similar pics)
- Filter bounding boxes according to their confidences
- Mark images as unseen/needs review/seen (preserved when re-importing a previously generated COCO export)
- Extensive use of hotkeys
- Moving and Zooming on the image
- Export to COCO file format
- Fuzzy search class names (useful for big number of classes)
- Priority sorting class names according to their frequency in the dataset (useful for big number of classes)
- Attributes
Limitations:
- Everything is local. If you want to label images from some cloud, you'll probably have to download them
- Attributes are fixed to sex (female/undefined/male) and age (juvenile/unknown/adult)
In order to use it, you need any kind of webserver to serve the files.
For convenience, the tool ships with a server, which you may inspect in serve.py.
It depends on the aiohttp python module, which may be installed using conda or pip.
Start the server:
python -m aiohttp.web -H localhost -P 8080 serve:init_funcNow open localhost:8080/index.html and you're good to go. If you explicitly turnoff JavaScript when surfing the internet, please make sure to turn it on for this webpage.