Computer Vision
 
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Object Detection

3722 papers with code • 91 benchmarks • 262 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
12,085
open-mmlab/mmdetection
64 papers
27,845
rwightman/pytorch-image-models
39 papers
29,826
osmr/imgclsmob
20 papers
2,918
See all 40 libraries.

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

Deep High-Resolution Representation Learning for Visual Recognition

open-mmlab/mmdetection • • 20 Aug 2019

High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection.

42
Paper
Code

YOLOX: Exceeding YOLO Series in 2021

Megvii-BaseDetection/YOLOX • • 18 Jul 2021

In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX.

41
Paper
Code

Deep High-Resolution Representation Learning for Human Pose Estimation

leoxiaobin/deep-high-resolution-net.pytorch • • CVPR 2019

We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks in parallel.

39
Paper
Code

High-Resolution Representations for Labeling Pixels and Regions

leoxiaobin/deep-high-resolution-net.pytorch • • 9 Apr 2019

The proposed approach achieves superior results to existing single-model networks on COCO object detection.

39
Paper
Code

Deformable Convolutional Networks

msracver/Deformable-ConvNets • • ICCV 2017

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules.

38
Paper
Code

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

tensorpack/tensorpack • • CVPR 2018

We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e. g., 10-150 MFLOPs).

37
Paper
Code

End-to-End Object Detection with Transformers

facebookresearch/detr • • ECCV 2020

We present a new method that views object detection as a direct set prediction problem.

37
Paper
Code

Spatial Memory for Context Reasoning in Object Detection

endernewton/tf-faster-rcnn • • ICCV 2017

On the other hand, modeling object-object relationships requires {\bf spatial} reasoning -- not only do we need a memory to store the spatial layout, but also a effective reasoning module to extract spatial patterns.

36
Paper
Code

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

pytorch/vision • • ECCV 2018

Datasets, Transforms and Models specific to Computer Vision

35
Paper
Code

ResNeSt: Split-Attention Networks

zhanghang1989/ResNeSt • • 19 Apr 2020

It is well known that featuremap attention and multi-path representation are important for visual recognition.

35
Paper
Code
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