object-detection
2072 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in object-detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Libraries
Use these libraries to find object-detection models and implementations
COCO
Most implemented papers
Deep Residual Learning for Image Recognition
tensorflow/models
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CVPR 2016
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.
YOLO9000: Better, Faster, Stronger
AlexeyAB/darknet
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CVPR 2017
On the 156 classes not in COCO, YOLO9000 gets 16. 0 mAP.
YOLOv4: Optimal Speed and Accuracy of Object Detection
AlexeyAB/darknet
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There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy.
Focal Loss for Dense Object Detection
facebookresearch/detectron
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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.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
rbgirshick/py-faster-rcnn
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NeurIPS 2015
In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals.
Mask R-CNN
matterport/Mask_RCNN
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ICCV 2017
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
MMDetection: Open MMLab Detection Toolbox and Benchmark
In this paper, we introduce the various features of this toolbox.
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
We present a class of efficient models called MobileNets for mobile and embedded vision applications.
You Only Look Once: Unified, Real-Time Object Detection
AlexeyAB/darknet
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CVPR 2016
A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
AlexeyAB/darknet
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Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection.
