2953 papers with code • 71 benchmarks • 234 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.
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 )
These leaderboards are used to track progress in Object Detection
LibrariesUse these libraries to find Object Detection models and implementations
- 3D Object Detection
- Real-Time Object Detection
- RGB Salient Object Detection
- Few-Shot Object Detection
- Few-Shot Object Detection
- Video Object Detection
- RGB-D Salient Object Detection
- Object Detection In Aerial Images
- Weakly Supervised Object Detection
- Small Object Detection
- Robust Object Detection
- Open Vocabulary Object Detection
- Medical Object Detection
- Video Salient Object Detection
- Object Proposal Generation
- Zero-Shot Object Detection
- Dense Object Detection
- Co-Salient Object Detection
- License Plate Detection
- Head Detection
- Open World Object Detection
- Camouflaged Object Segmentation
- One-Shot Object Detection
- Surgical tool detection
- Moving Object Detection
- Multiview Detection
- 3D Object Detection From Monocular Images
- Object Detection In Indoor Scenes
- Semantic Part Detection
- Class-agnostic Object Detection
- Body Detection
- Object Skeleton Detection
- Multiple Affordance Detection
- Fish Detection
Most implemented papers
Deep Residual Learning for Image Recognition
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.
YOLOv3: An Incremental Improvement
At 320x320 YOLOv3 runs in 22 ms at 28. 2 mAP, as accurate as SSD but three times faster.
YOLO9000: Better, Faster, Stronger
On the 156 classes not in COCO, YOLO9000 gets 16. 0 mAP.
YOLOv4: Optimal Speed and Accuracy of Object Detection
There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy.
SSD: Single Shot MultiBox Detector
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.
Focal Loss for Dense Object Detection
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.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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.
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
We present a class of efficient models called MobileNets for mobile and embedded vision applications.
MMDetection: Open MMLab Detection Toolbox and Benchmark
In this paper, we introduce the various features of this toolbox.