Computer Vision
 
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Object Detection

2180 papers with code • 61 benchmarks • 207 datasets

Object detection is the task of detecting instances of objects of a certain class within an image. The state-of-the-art methods can be categorized into two main types: one-stage methods and two stage-methods. One-stage methods prioritize inference speed, and example models include YOLO, SSD and RetinaNet. Two-stage methods prioritize detection accuracy, and example models include Faster R-CNN, Mask R-CNN and Cascade R-CNN.

The most popular benchmark is the MSCOCO dataset. Models are typically evaluated according to a Mean Average Precision metric.

( Image credit: Detectron )

Libraries

Use these libraries to find Object Detection models and implementations

Datasets

Subtasks

Most implemented papers

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Deep Residual Learning for Image Recognition

tensorflow/models • • CVPR 2016

Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

398
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Code

YOLOv3: An Incremental Improvement

tensorflow/models • • 8 Apr 2018

At 320x320 YOLOv3 runs in 22 ms at 28. 2 mAP, as accurate as SSD but three times faster.

320
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Code

YOLO9000: Better, Faster, Stronger

AlexeyAB/darknet • • CVPR 2017

On the 156 classes not in COCO, YOLO9000 gets 16. 0 mAP.

234
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Code

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.

225
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Code

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.

218
Paper
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Focal Loss for Dense Object Detection

facebookresearch/detectron • • ICCV 2017

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.

217
Paper
Code

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.

162
Paper
Code

Mask R-CNN

matterport/Mask_RCNN • • ICCV 2017

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

154
Paper
Code

MMDetection: Open MMLab Detection Toolbox and Benchmark

open-mmlab/mmdetection • • 17 Jun 2019

In this paper, we introduce the various features of this toolbox.

143
Paper
Code

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

tensorflow/tensorflow • • 17 Apr 2017

We present a class of efficient models called MobileNets for mobile and embedded vision applications.

137
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Code
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