VoTT (Visual Object Tagging Tool) is a popular open source annotation tool released my Microsoft. It lets you easily label image assets for use with object detection models. We have a full VoTT tutorial that shows the process of creating labels for your custom dataset.
The native format of VoTT is a proprietary JSON file that is not supported by any known models. Getting your VoTT-labeled dataset ready for training is where Roboflow comes in. Just import your images and annotations and select the format you want to convert it to and you'll be on your way to training a model in no time.
Below, learn the structure of VoTT JSON.
{
"asset": {
"format": "jpg",
"id": "0a2ac9053d4d842653d3ff9f988421a6",
"name": "img0001.jpg",
"path": "file:D:/HardHats/img0001.jpg",
"size": {
"width": 612,
"height": 408
},
"state": 2,
"type": 1
},
"regions": [
{
"id": "XEhNEKjZT",
"type": "RECTANGLE",
"tags": [
"helmet"
],
"boundingBox": {
"height": 204,
"width": 505.5652173913043,
"left": 32.06688963210702,
"top": 143.9598662207358
},
"points": [
{
"x": 32.06688963210702,
"y": 143.9598662207358
},
{
"x": 537.6321070234113,
"y": 143.9598662207358
},
{
"x": 537.6321070234113,
"y": 347.95986622073576
},
{
"x": 32.06688963210702,
"y": 347.95986622073576
}
]
}
],
"version": "2.1.0"
}With Roboflow supervision, an open source Python package with utilities for completing computer vision tasks, you can merge and split detections in VoTT JSON. Read our dedicated guides to learn how to merge and split VoTT JSON detections.
Below, see model architectures that require data in the VoTT JSON format when training a new model.
On each page below, you can find links to our guides that show how to plot predictions from the model, and complete other common tasks like detecting small objects with the model.