Computer Vision
 
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Object Detection

3692 papers with code • 84 benchmarks • 256 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
12,029
open-mmlab/mmdetection
64 papers
27,708
rwightman/pytorch-image-models
39 papers
29,671
osmr/imgclsmob
20 papers
2,917
See all 40 libraries.

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

ZheC/Realtime_Multi-Person_Pose_Estimation • • CVPR 2017

We present an approach to efficiently detect the 2D pose of multiple people in an image.

61
Paper
Code

Searching for MobileNetV3

tensorflow/models • • ICCV 2019

We achieve new state of the art results for mobile classification, detection and segmentation.

61
Paper
Code

Towards Deep Learning Models Resistant to Adversarial Attacks

MadryLab/mnist_challenge • • ICLR 2018

Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal.

57
Paper
Code

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

chengyangfu/retinamask • • 10 Jan 2019

COCO test-dev results are up to 41. 4 mAP for RetinaMask-101 vs 39. 1mAP for RetinaNet-101, while the runtime is the same during evaluation.

52
Paper
Code

R-FCN: Object Detection via Region-based Fully Convolutional Networks

daijifeng001/r-fcn • • NeurIPS 2016

In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image.

49
Paper
Code

Masked Autoencoders Are Scalable Vision Learners

facebookresearch/mae • • CVPR 2022

Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels.

49
Paper
Code

An Implementation of Faster RCNN with Study for Region Sampling

endernewton/tf-faster-rcnn • • 7 Feb 2017

We adapted the join-training scheme of Faster RCNN framework from Caffe to TensorFlow as a baseline implementation for object detection.

48
Paper
Code

A ConvNet for the 2020s

facebookresearch/ConvNeXt • • CVPR 2022

The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model.

45
Paper
Code

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

qianguih/voxelnet • • CVPR 2018

Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.

44
Paper
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
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