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Object Detection

607 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 )

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Greatest papers with code

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

Pooling Pyramid Network for Object Detection

9 Jul 2018tensorflow/models

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

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 SEMANTIC SEGMENTATION

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.

DENSE OBJECT DETECTION

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

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.

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.

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.

IMAGE CLASSIFICATION OBJECT DETECTION 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