Image Annotation Tool
Upload an image, draw bounding boxes, polygons, or brush masks, assign class labels, and export annotations in common training-data formats.
Why annotation matters
Before a model can learn to detect or segment anything, humans usually create the training labels first. Boxes, polygons, and masks become the ground truth a model is asked to imitate.
What students practise
This page lets students create bounding boxes for detection, polygons for segmentation boundaries, and brush masks for pixel-level regions, then export the labels in common dataset formats.
What to compare
Boxes are quick but coarse, polygons trace shape boundaries, and masks capture pixel-level detail. Seeing all three makes the tradeoff between speed and precision much easier to understand.
Boxes
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Polygons
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Masks
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Exports
Download the dataset labels
YOLO exports one normalized box per line, COCO exports a structured JSON file with categories and annotations, and CSV gives a simple table that students can read directly.
Class Labels
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Annotation List
Learning Notes
- Bounding boxes are common for object detection because they are fast to create and easy to train on.
- Polygons trace shape boundaries more precisely, which is helpful when students need region-level segmentation labels.
- Brush masks are closest to pixel-wise segmentation, but they take the most annotation time.
- Exporting the same scene in YOLO, COCO, and CSV helps students see how one visual annotation becomes structured training data.