CenterNet : A Machine Learning Model for Anchorless Object Detection
This is an introduction to「CenterNet」, a machine learning model that can be used with ailia SDK. You can easily use this model to create AI applications using ailia SDK as well as many other ready-to-use ailia MODELS.
CenterNet is a machine learning model for anchorless object detection published in April 2019.
Objects as Points
Detection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly…
CenterNet can be used to calculate the bounding boxes for 80 categories of the COCO dataset.
By using heatmaps, as in other systems such as OpenPose, for object detection, CenterNet can perform detection without using anchors used in YOLOv2 and later.
An anchor is a bounding box, defined by several boxes with different aspect ratios. Object detection for each bounding box increases the number of objects that can be detected simultaneously. Introduced in YOLOv2, it increases the number of objects that can be detected simultaneously by performing object detection for each bounding box.
CenterNet infers a heatmap of the object’s center coordinates, the offset of the center coordinates, and the object’s size.
CenterNet is capable of more accurate inference than YOLOv3 and RetinaNet.
You can run CenterNet on the webcam video stream in ailia SDK with the following command.
python3 centernet.py -v 0