Real-time object detection is the task of doing object detection in real-time with fast inference while maintaining a base level of accuracy.
( Image credit: CenterNet )
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In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image.
#5 best model for Real-Time Object Detection on PASCAL VOC 2007
In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals.
#6 best model for Real-Time Object Detection on PASCAL VOC 2007
Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection.
#2 best model for Real-Time Object Detection on COCO
A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
#2 best model for Real-Time Object Detection on PASCAL VOC 2007
Here we aim to learn a better architecture of feature pyramid network for object detection.
#12 best model for Real-Time Object Detection on COCO (MAP metric)
Together these two variants address the two critical use cases in efficient object detection: improving efficiency without sacrificing accuracy, and improving accuracy at real-time efficiency.
#9 best model for Real-Time Object Detection on COCO