About

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 )

Benchmarks

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

Libraries

Subtasks

Datasets

Greatest papers with code

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

17 Apr 2017tensorflow/tensorflow

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

IMAGE CLASSIFICATION OBJECT DETECTION

MobileNetV2: Inverted Residuals and Linear Bottlenecks

CVPR 2018 tensorflow/models

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.

IMAGE CLASSIFICATION OBJECT DETECTION PERSON RE-IDENTIFICATION RETINAL OCT DISEASE CLASSIFICATION SEMANTIC SEGMENTATION

Deep Residual Learning for Image Recognition

CVPR 2016 tensorflow/models

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.

DOMAIN GENERALIZATION FINE-GRAINED IMAGE CLASSIFICATION IMAGE-TO-IMAGE TRANSLATION OBJECT DETECTION PEDESTRIAN ATTRIBUTE RECOGNITION PEDESTRIAN TRAJECTORY PREDICTION PERSON RE-IDENTIFICATION RETINAL OCT DISEASE CLASSIFICATION SEMANTIC SEGMENTATION

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

CVPR 2020 tensorflow/models

We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search.

IMAGE CLASSIFICATION INSTANCE SEGMENTATION NEURAL ARCHITECTURE SEARCH REAL-TIME OBJECT DETECTION

Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection

CVPR 2020 tensorflow/models

In this paper we propose a method that leverages temporal context from the unlabeled frames of a novel camera to improve performance at that camera.

VIDEO OBJECT DETECTION VIDEO UNDERSTANDING

EfficientDet: Scalable and Efficient Object Detection

CVPR 2020 tensorflow/models

Model efficiency has become increasingly important in computer vision.

 Ranked #1 on Object Detection on COCO minival (AP50 metric)

AUTOML REAL-TIME OBJECT DETECTION

Objects as Points

16 Apr 2019tensorflow/models

We model an object as a single point --- the center point of its bounding box.

Ranked #13 on Real-Time Object Detection on COCO (using extra training data)

KEYPOINT DETECTION REAL-TIME OBJECT DETECTION

Speed/accuracy trade-offs for modern convolutional object detectors

CVPR 2017 tensorflow/models

The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform.

OBJECT DETECTION