RT-DETR

The RT_DETR class implements a wrapper for the RT_DETR model, which detects objects in images and videos using a real-time detection transformer.

boolean

default:"False"

If True, inference call is run on the local VM, else offloaded onto GRID-Cortex. Defaults to True.

Union[np.ndarray, str]

required

The image array or path to the video file for object detection.

float

required

Confidence threshold for filtering object detection results.

np.ndarray

Annotated image with bounding boxes and class labels.

from grid.model.perception.detection.rt_detr import RT_DETR
import rerun as rr
car = AirGenCar()

# We will be capturing an image from the AirGen simulator 
# and run model inference on it.

img =  car.getImage("front_center", "rgb").data

model = RT_DETR(use_local=False)
result = model.run(input=img.copy(), confidence_threshold=0.5)
rr.log("result",rr.Image(result))

License
Source

This code is licensed under the Apache 2.0 License.

from grid.model.perception.detection.rt_detr import RT_DETR import rerun as rr car = AirGenCar() # We will be capturing an image from the AirGen simulator # and run model inference on it. img = car.getImage("front_center", "rgb").data model = RT_DETR(use_local=False) result = model.run(input=img.copy(), confidence_threshold=0.5) rr.log("result",rr.Image(result))

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