Object

Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.

231

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YOLOv4: Optimal Speed and Accuracy of Object Detection

AlexeyAB/darknet • • 23 Apr 2020

There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy.

227

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SSD: Single Shot MultiBox Detector

weiliu89/caffe • 8 Dec 2015

Experimental results on the PASCAL VOC, MS COCO, and ILSVRC datasets confirm that SSD has comparable accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference.

223

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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

rbgirshick/py-faster-rcnn • • NeurIPS 2015

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.

195

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Mask R-CNN

tensorflow/models • • ICCV 2017

Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.

172

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You Only Look Once: Unified, Real-Time Object Detection

AlexeyAB/darknet • • CVPR 2016

A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.

144

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FCOS: Fully Convolutional One-Stage Object Detection

tianzhi0549/FCOS • • ICCV 2019

By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training.

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