Object Detection

1443 papers with code • 42 benchmarks • 147 datasets

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 )

Greatest papers with code

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

tensorflow/tensorflow 17 Apr 2017

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

General Classification Image Classification +1

MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

tensorflow/models CVPR 2021

By incorporating regular convolutions in the search space and directly optimizing the network architectures for object detection, we obtain a family of object detection models, MobileDets, that achieve state-of-the-art results across mobile accelerators.

Neural Architecture Search Object Detection

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

tensorflow/models CVPR 2020

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

tensorflow/models CVPR 2020

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

Searching for MobileNetV3

tensorflow/models ICCV 2019

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

Ranked #57 on Semantic Segmentation on Cityscapes test (using extra training data)

Image Classification Neural Architecture Search +2

Pooling Pyramid Network for Object Detection

tensorflow/models 9 Jul 2018

We share box predictors across all scales, and replace convolution between scales with max pooling.

Object Detection

MobileNetV2: Inverted Residuals and Linear Bottlenecks

tensorflow/models CVPR 2018

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 +3

Mobile Video Object Detection with Temporally-Aware Feature Maps

tensorflow/models CVPR 2018

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

Focal Loss for Dense Object Detection

tensorflow/models ICCV 2017

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.

Dense Object Detection Long-tail Learning +2

Mask R-CNN

tensorflow/models ICCV 2017

Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.

3D Instance Segmentation Human Part Segmentation +7