Labelai is an online tool designed to label images, useful for training AI models.

It's totally client-side, there is no request to any server. It is implemented with Next.js + Preact.

Annotations are saved as XML files in PASCAL VOC format, the format used by ImageNet. Besides, it also supports YOLO format.

It's strongly inspired by labelImg.

Labelai doesn't require any installation 😜. In order to start:

  • Open the web app
  • Click 'Open'. Here you can import 3 file types: Images (.png, .jpg, .gif, .svg...), XML (PascalVOC labels) and TXT (YOLO labels). Surely the first time you are only interested in importing images, since you will not have any label saved yet. Important: if you want to import already saved labels, you must import it together with the images at the same time. In the case of YOLO, you also must import the classes.txt file.
  • Click and release left mouse to select a region to annotate the rect box. Then, you can name that label or select one of the already used names.
  • Update rect boxes. Once you already labeled a part of the image, you can resize it by clicking on a box corner, move the box using drag & drop, duplicate it (Ctrl + d) or even remove it (⌫ Delete).
  • Move through images. Once you finish annotating all the labels of an image, you can press to the Next and Prev button to move through images and start anottating another image. You can also use the arrows → and ←.
  • Save your changes. You can save the labels of the current file or the labels of all files. At this point, you can decide in which format you want to save them (XML for ImageNet or TXT for YOLO). In the case of YOLO, a file named classes.txt is saved too and it defines the list of class names that your YOLO label refers to.

Don't forget to tag @aralroca in your comment, otherwise they may not be notified.

Authors community post
Preact - Next.js - SSR - SSG Open source Machine learning Books Sport Nature
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