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1.1 KiB
Object Detection using Detectron2
author: filip-halt, fzliu
Description
This operator uses Facebook's Detectron2 library to compute bounding boxes, class labels, and class scores for detected objects in a given image.
Code Example
import towhee
towhee.glob('./towhee.jpg') \
.image_decode.cv2() \
.object_detection.detectron2(model_name='retinanet_resnet50') \
.show()
Factory Constructor
Create the operator via the following factory method
object_detection.detectron2(model_name='retinanet_resnet50', thresh=0.5, num_classes=1000, skip_preprocess=False)
Parameters:
model_name: str
A string indicating which model to use.
thresh: float
The threshold value for which an object is detected (default value: 0.5). Set this value lower to detect more objects at the expense of accuracy, or higher to reduce the total number of detections but increase the quality of detected objects.
1.1 KiB
Object Detection using Detectron2
author: filip-halt, fzliu
Description
This operator uses Facebook's Detectron2 library to compute bounding boxes, class labels, and class scores for detected objects in a given image.
Code Example
import towhee
towhee.glob('./towhee.jpg') \
.image_decode.cv2() \
.object_detection.detectron2(model_name='retinanet_resnet50') \
.show()
Factory Constructor
Create the operator via the following factory method
object_detection.detectron2(model_name='retinanet_resnet50', thresh=0.5, num_classes=1000, skip_preprocess=False)
Parameters:
model_name: str
A string indicating which model to use.
thresh: float
The threshold value for which an object is detected (default value: 0.5). Set this value lower to detect more objects at the expense of accuracy, or higher to reduce the total number of detections but increase the quality of detected objects.