Computer Vision
 
Edit

Object Detection

3706 papers with code • 91 benchmarks • 261 datasets

Object Detection is a computer vision task in which the goal is to detect and locate objects of interest in an image or video. The task involves identifying the position and boundaries of objects in an image, and classifying the objects into different categories. It forms a crucial part of vision recognition, alongside image classification and retrieval.

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
PaddlePaddle/PaddleDetection
71 papers
12,059
open-mmlab/mmdetection
64 papers
27,790
rwightman/pytorch-image-models
39 papers
29,758
osmr/imgclsmob
20 papers
2,917
See all 40 libraries.

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

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.

469
Paper
Code

YOLOv3: An Incremental Improvement

open-mmlab/mmdetection • • 8 Apr 2018

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

316
Paper
Code

YOLO9000: Better, Faster, Stronger

AlexeyAB/darknet • • CVPR 2017

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

233
Paper
Code

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.

231
Paper
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.

227
Paper
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.

223
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.

195
Paper
Code

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
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.

153
Paper
Code

MobileNetV2: Inverted Residuals and Linear Bottlenecks

tensorflow/models • • CVPR 2018

In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes.

148
Paper
Code
Contact us on: hello@paperswithcode.com . Papers With Code is a free resource with all data licensed under CC-BY-SA.
Terms Data policy Cookies policy