RF-DETR is a real-time object detection transformer-based architecture designed to transfer well to both a wide variety of domains and to datasets big and small.
RF-DETR is the first real-time model to exceed 60 AP on the Microsoft COCO benchmark alongside competitive performance at base sizes. It also achieves state-of-the-art performance on RF100-VL, an object detection benchmark that measures model domain adaptability to real world problems. RF-DETR is comparable speed to current real-time objection models.
RF-DETR uses the Microsoft COCO JSON format.
Below, learn the structure of RF-DETR.
{
"info": {
"year": "2020",
"version": "1",
"description": "Exported from roboflow.ai",
"contributor": "Roboflow",
"url": "https://app.roboflow.ai/datasets/hard-hat-sample/1",
"date_created": "2000-01-01T00:00:00+00:00"
},
"licenses": [
{
"id": 1,
"url": "https://creativecommons.org/publicdomain/zero/1.0/",
"name": "Public Domain"
}
],
"categories": [
{
"id": 0,
"name": "Workers",
"supercategory": "none"
},
{
"id": 1,
"name": "head",
"supercategory": "Workers"
},
{
"id": 2,
"name": "helmet",
"supercategory": "Workers"
},
{
"id": 3,
"name": "person",
"supercategory": "Workers"
}
],
"images": [
{
"id": 0,
"license": 1,
"file_name": "0001.jpg",
"height": 275,
"width": 490,
"date_captured": "2020-07-20T19:39:26+00:00"
}
],
"annotations": [
{
"id": 0,
"image_id": 0,
"category_id": 2,
"bbox": [
45,
2,
85,
85
],
"area": 7225,
"segmentation": [],
"iscrowd": 0
},
{
"id": 1,
"image_id": 0,
"category_id": 2,
"bbox": [
324,
29,
72,
81
],
"area": 5832,
"segmentation": [],
"iscrowd": 0
}
]
}
With Roboflow supervision, an open source Python package with utilities for completing computer vision tasks, you can merge and split detections in RF-DETR. Read our dedicated guides to learn how to merge and split RF-DETR detections.
Below, see model architectures that require data in the RF-DETR 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.