RTDETR_detect - ComfyUI Built-in Node Documentation

The RT-DETR Detect node performs object detection on input images using an RT-DETR model. It identifies objects, draws bounding boxes around them, and labels them according to the COCO dataset classes. You can filter the results by confidence score, object class, and limit the total number of detections.

Inputs

Parameter	Description	Data Type	Required	Range
`model`	The RT-DETR model used for object detection.	MODEL	Yes	N/A
`image`	The input image(s) to detect objects in. The node processes images in batches of up to 32.	IMAGE	Yes	N/A
`threshold`	The minimum confidence score a detection must have to be included in the results (default: 0.5).	FLOAT	No	N/A
`class_name`	Filter detections by class. Set to ‘all’ to disable filtering (default: “all”).	COMBO	No	`"all"` `"person"` `"bicycle"` `"car"` `"motorcycle"` `"airplane"` `"bus"` `"train"` `"truck"` `"boat"` `"traffic light"` `"fire hydrant"` `"stop sign"` `"parking meter"` `"bench"` `"bird"` `"cat"` `"dog"` `"horse"` `"sheep"` `"cow"` `"elephant"` `"bear"` `"zebra"` `"giraffe"` `"backpack"` `"umbrella"` `"handbag"` `"tie"` `"suitcase"` `"frisbee"` `"skis"` `"snowboard"` `"sports ball"` `"kite"` `"baseball bat"` `"baseball glove"` `"skateboard"` `"surfboard"` `"tennis racket"` `"bottle"` `"wine glass"` `"cup"` `"fork"` `"knife"` `"spoon"` `"bowl"` `"banana"` `"apple"` `"sandwich"` `"orange"` `"broccoli"` `"carrot"` `"hot dog"` `"pizza"` `"donut"` `"cake"` `"chair"` `"couch"` `"potted plant"` `"bed"` `"dining table"` `"toilet"` `"tv"` `"laptop"` `"mouse"` `"remote"` `"keyboard"` `"cell phone"` `"microwave"` `"oven"` `"toaster"` `"sink"` `"refrigerator"` `"book"` `"clock"` `"vase"` `"scissors"` `"teddy bear"` `"hair drier"` `"toothbrush"`
`max_detections`	Maximum number of detections to return per image. In order of descending confidence score (default: 100).	INT	No	N/A

Outputs

Output Name	Description	Data Type
`bboxes`	A list of bounding boxes for each input image. Each box contains coordinates (x, y, width, height), a class label, and a confidence score.	BOUNDINGBOX

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​Inputs

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Inputs

Outputs