Browse SoTA > Computer Vision > Object Detection

Object Detection

728 papers with code · Computer Vision

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 )

Leaderboards

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

Greatest papers with code

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 SEMANTIC SEGMENTATION

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

17 Apr 2017tensorflow/models

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

IMAGE CLASSIFICATION OBJECT DETECTION

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 MULTI-LABEL CLASSIFICATION OBJECT DETECTION PERSON RE-IDENTIFICATION SEMANTIC SEGMENTATION

Going Deeper with Convolutions

CVPR 2015 tensorflow/models

We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014).

IMAGE CLASSIFICATION OBJECT DETECTION OBJECT RECOGNITION

Searching for MobileNetV3

ICCV 2019 tensorflow/models

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

IMAGE CLASSIFICATION NEURAL ARCHITECTURE SEARCH OBJECT DETECTION SEMANTIC SEGMENTATION

Mobile Video Object Detection with Temporally-Aware Feature Maps

CVPR 2018 tensorflow/models

This paper introduces an online model for object detection in videos designed to run in real-time on low-powered mobile and embedded devices.

VIDEO OBJECT DETECTION

MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

30 Apr 2020tensorflow/models

MobileDets also outperform MobileNetV2+SSDLite by 1. 9 mAP on mobile CPUs, 3. 7 mAP on EdgeTPUs and 3. 4 mAP on DSPs while running equally fast.

NEURAL ARCHITECTURE SEARCH 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

MnasFPN: Learning Latency-aware Pyramid Architecture for Object Detection on Mobile Devices

CVPR 2020 tensorflow/models

We propose Mnasfpn, a mobile-friendly search space for the detection head, and combine it with latency-aware architecture search to produce efficient object detection models.

OBJECT DETECTION