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 this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented.
The rapid growth of real-time huge data capturing has pushed the deep learning and data analytic computing to the edge systems.
We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search.
#2 best model for Instance Segmentation on COCO minival
Instead, the mixture components are automatically learned to represent the distribution of the bounding box through density estimation.
Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection.
#9 best model for Real-Time Object Detection on COCO
We hope that CenterMask and VoVNetV2 can serve as a solid baseline of real-time instance segmentation and backbone network for various vision tasks, respectively.
#3 best model for Instance Segmentation on COCO test-dev (AP50 metric)
We propose a Harmonic Densely Connected Network to achieve high efficiency in terms of both low MACs and memory traffic.
Experiments on MS COCO show that our TTFNet has great advantages in balancing training time, inference speed, and accuracy.
#7 best model for Real-Time Object Detection on COCO
Drones or general Unmanned Aerial Vehicles (UAVs), endowed with computer vision function by on-board cameras and embedded systems, have become popular in a wide range of applications.