Object Detection
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
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Subtasks
Greatest papers with code
Looking Fast and Slow: Memory-Guided Mobile Video Object Detection
Models and examples built with TensorFlow
Searching for MobileNetV3
ICCV 2019
• tensorflow/models
• 
We achieve new state of the art results for mobile classification, detection and segmentation.
#92 best model for
Image Classification
on ImageNet
IMAGE CLASSIFICATION NEURAL ARCHITECTURE SEARCH OBJECT DETECTION SEMANTIC SEGMENTATION
Pooling Pyramid Network for Object Detection
We share box predictors across all scales, and replace convolution between scales with max pooling.
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.
#97 best model for
Image Classification
on ImageNet
Focal Loss for Dense Object Detection
ICCV 2017
• tensorflow/models
•
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.
#42 best model for
Object Detection
on COCO test-dev
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
We present a class of efficient models called MobileNets for mobile and embedded vision applications.
#107 best model for
Image Classification
on ImageNet
Mask R-CNN
ICCV 2017
• tensorflow/models
•
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
SOTA for Instance Segmentation on Cityscapes test (using extra training data)
HUMAN PART SEGMENTATION INSTANCE SEGMENTATION KEYPOINT DETECTION MULTI-HUMAN PARSING NUCLEAR SEGMENTATION OBJECT DETECTION SEMANTIC SEGMENTATION
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.
#68 best model for
Object Detection
on COCO test-dev
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.
#8 best model for
Semantic Segmentation
on Cityscapes val
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).