Full Code of amusi/awesome-object-detection for AI

master 6d39aede560d cached
2 files
46.7 KB
14.6k tokens
1 requests
Download .txt 
Repository: amusi/awesome-object-detection
Branch: master
Commit: 6d39aede560d
Files: 2
Total size: 46.7 KB

Directory structure:
gitextract_t3iari6u/

├── Date.md
└── README.md

================================================
FILE CONTENTS
================================================

================================================
FILE: Date.md
================================================
2019-10-11 Update 1 Project

**Detectron2**

- intro: Detectron2 is FAIR's next-generation research platform for object detection and segmentation.
- [blog](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/)
- code: <https://github.com/facebookresearch/detectron2>

2019-09-06 Update 1 paper

**Imbalance Problems in Object Detection: A Review**

- intro: under review at TPAMI
- arXiv: <https://arxiv.org/abs/1909.00169>

2019-08-14 Update 1 paper

**Recent Advances in Deep Learning for Object Detection**

- intro: From 2013 (OverFeat) to 2019 (DetNAS)
- arXiv: <https://arxiv.org/abs/1908.03673>

2019-07-24 Update 1 paper

**A Survey of Deep Learning-based Object Detection**

- intro：From Fast R-CNN to NAS-FPN

- arXiv：<https://arxiv.org/abs/1907.09408>

2019-05-17 Update 1 paper

**Object Detection in 20 Years: A Survey**

- intro：This work has been submitted to the IEEE TPAMI for possible publication
- arXiv：<https://arxiv.org/abs/1905.05055>

2019-04-05 Update 1 paper

**Comparison Network for One-Shot Conditional Object Detection**

- arXiv: https://arxiv.org/abs/1904.02317

2019-03-05 Update 1 paper

**Feature Selective Anchor-Free Module for Single-Shot Object Detection**

- intro: CVPR 2019
- arXiv: https://arxiv.org/abs/1903.00621

2019-02-15 Update 3 detection toolbox

- [Detectron(FAIR)](https://github.com/facebookresearch/Detectron): Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including [Mask R-CNN](https://arxiv.org/abs/1703.06870). It is written in Python and powered by the [Caffe2](https://github.com/caffe2/caffe2) deep learning framework.

- [maskrcnn-benchmark(FAIR)](https://github.com/facebookresearch/maskrcnn-benchmark): Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

- [mmdetection(SenseTime&CUHK)](https://github.com/open-mmlab/mmdetection): mmdetection is an open source object detection toolbox based on PyTorch. It is a part of the open-mmlab project developed by [Multimedia Laboratory, CUHK](http://mmlab.ie.cuhk.edu.hk/).

2019-01-25 Update 5 papers

**3D Backbone Network for 3D Object Detection**

- arXiv: https://arxiv.org/abs/1901.08373

**Object Detection based on Region Decomposition and Assembly**

- intro: AAAI 2019

- arXiv: https://arxiv.org/abs/1901.08225

**Bottom-up Object Detection by Grouping Extreme and Center Points**

- intro: one stage 43.2% on COCO test-dev
- arXiv: https://arxiv.org/abs/1901.08043
- github: https://github.com/xingyizhou/ExtremeNet

**ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features**

- intro: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

- arXiv: https://arxiv.org/abs/1901.07925

**Consistent Optimization for Single-Shot Object Detection**

- intro: improves RetinaNet from 39.1 AP to 40.1 AP on COCO datase

- arXiv: https://arxiv.org/abs/1901.06563

2019-01-15 Update 1 paper

**Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes**

- arXiv: https://arxiv.org/abs/1901.03796

2019-01-14 Update 1 paper

**RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free**

- arXiv: https://arxiv.org/abs/1901.03353
- github: https://github.com/chengyangfu/retinamask

2019-01-12 Update 1 paper

**Region Proposal by Guided Anchoring**

- intro: CUHK - SenseTime Joint Lab
- arXiv: https://arxiv.org/abs/1901.03278

2019-01-08 Update 1 paper

**Scale-Aware Trident Networks for Object Detection**

- intro: mAP of **48.4** on the COCO dataset
- arXiv: https://arxiv.org/abs/1901.01892

2019-01-04 Update 1 paper

**Large-Scale Object Detection of Images from Network Cameras in Variable Ambient Lighting Conditions**

- arXiv: https://arxiv.org/abs/1812.11901

2018-12-13 Update 1 paper

**Strong-Weak Distribution Alignment for Adaptive Object Detection**

- arXiv: https://arxiv.org/abs/1812.04798

2018-12-05 Update 3 papers

**AutoFocus: Efficient Multi-Scale Inference**

- intro: AutoFocus obtains an **mAP of 47.9%** (68.3% at 50% overlap) on the **COCO test-dev** set while processing **6.4 images per second on a Titan X (Pascal) GPU** 
- arXiv: https://arxiv.org/abs/1812.01600

**NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection**

- intro: Google Could
- arXiv: https://arxiv.org/abs/1812.00124

**SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection**

- intro: UC Berkeley
- arXiv: https://arxiv.org/abs/1812.00929

2018-12-04 Update 10 papers

**Grid R-CNN**

- intro: SenseTime
- arXiv: https://arxiv.org/abs/1811.12030

**Deformable ConvNets v2: More Deformable, Better Results**

- intro: Microsoft Research Asia

- arXiv: https://arxiv.org/abs/1811.11168

**Anchor Box Optimization for Object Detection**

- intro: Microsoft Research
- arXiv: https://arxiv.org/abs/1812.00469

**Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects**

- intro: https://arxiv.org/abs/1811.12152

**NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection**

- arXiv: https://arxiv.org/abs/1812.00124

**Learning RoI Transformer for Detecting Oriented Objects in Aerial Images**

- arXiv: https://arxiv.org/abs/1812.00155

**Integrated Object Detection and Tracking with Tracklet-Conditioned Detection**

- intro: Microsoft Research Asia
- arXiv: https://arxiv.org/abs/1811.11167

**Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection**

- arXiv: https://arxiv.org/abs/1811.11318

 **Gradient Harmonized Single-stage Detector**

- intro: AAAI 2019
- arXiv: https://arxiv.org/abs/1811.05181

**CFENet: Object Detection with Comprehensive Feature Enhancement Module**

- intro: ACCV 2018
- github: https://github.com/qijiezhao/CFENet

2018-11-19

**DeRPN: Taking a further step toward more general object detection**

- intro: AAAI 2019
- arXiv: https://arxiv.org/abs/1811.06700
- github: https://github.com/HCIILAB/DeRPN

2018-11-14

**M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network**

- intro: AAAI 2019
- arXiv: https://arxiv.org/abs/1811.04533
- github: https://github.com/qijiezhao/M2Det

2018-10-31

**Hybrid Knowledge Routed Modules for Large-scale Object Detection**

- intro: Sun Yat-Sen University & Huawei Noah’s Ark Lab
- arXiv: https://arxiv.org/abs/1810.12681
- github: https://github.com/chanyn/HKRM

2018-10-08

**Weakly Supervised Object Detection in Artworks**

- intro: ECCV 2018 Workshop Computer Vision for Art Analysis
- arXiv: https://arxiv.org/abs/1810.02569
- Datasets: https://wsoda.telecom-paristech.fr/downloads/dataset/IconArt_v1.zip

**Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation**

- intro: CVPR 2018
- arXiv: https://arxiv.org/abs/1803.11365
- homepage: https://naoto0804.github.io/cross_domain_detection/
- paper: http://openaccess.thecvf.com/content_cvpr_2018/html/Inoue_Cross-Domain_Weakly-Supervised_Object_CVPR_2018_paper.html
- github: https://github.com/naoto0804/cross-domain-detection

2018-09-26

**Object Detection from Scratch with Deep Supervision**

- intro: This is an extended version of DSOD
- arXiv: https://arxiv.org/abs/1809.09294

2018-09-25

**《Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection》**

- intro: CMU & Face++
- arXiv: https://arxiv.org/abs/1809.08545
- github: https://github.com/yihui-he/softer-NMS

2018-09-21

**《Receptive Field Block Net for Accurate and Fast Object Detection》**

- intro: ECCV 2018
- arXiv: [https://arxiv.org/abs/1711.07767](https://arxiv.org/abs/1711.07767)
- github: [https://github.com/ruinmessi/RFBNet](https://github.com/ruinmessi/RFBNet)

2018-09-11

**《Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks》**

- intro: awesome


- arXiv: https://arxiv.org/abs/1809.03193

2018-09-10

**《Deep Learning for Generic Object Detection: A Survey》**

- intro: Submitted to IJCV 2018
- arXiv: https://arxiv.org/abs/1809.02165

2018-08-27

**Deep Feature Pyramid Reconfiguration for Object Detection**

- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1808.07993

2018-08-17

**R3-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos**

- arxiv: https://arxiv.org/abs/1808.05560
- youtube: https://youtu.be/xCYD-tYudN0

2018-08-14

**《Unsupervised Hard Example Mining from Videos for Improved Object Detection》**

- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1808.04285

2018-08-10

**CornerNet: Detecting Objects as Paired Keypoints**

- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1808.01244

2018-07-30

**Acquisition of Localization Confidence for Accurate Object Detection**

- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1807.11590
- github: https://github.com/vacancy/PreciseRoIPooling

================================================
FILE: README.md
================================================
# object-detection

[TOC]

This is a list of awesome articles about object detection. If you want to read the paper according to time, you can refer to [Date](Date.md).

- R-CNN
- Fast R-CNN
- Faster R-CNN
- Mask R-CNN
- Light-Head R-CNN
- Cascade R-CNN
- SPP-Net
- YOLO
- YOLOv2
- YOLOv3
- YOLT
- SSD
- DSSD
- FSSD
- ESSD
- MDSSD
- Pelee
- Fire SSD
- R-FCN
- FPN
- DSOD
- RetinaNet
- MegDet
- RefineNet
- DetNet
- SSOD
- CornerNet
- M2Det
- 3D Object Detection
- ZSD（Zero-Shot Object Detection）
- OSD（One-Shot object Detection）
- Weakly Supervised Object Detection
- Softer-NMS
- 2018
- 2019
- Other

Based on handong1587's github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

# Survey

**Imbalance Problems in Object Detection: A Review**

- intro: under review at TPAMI
- arXiv: <https://arxiv.org/abs/1909.00169>

**Recent Advances in Deep Learning for Object Detection**

- intro: From 2013 (OverFeat) to 2019 (DetNAS)
- arXiv: <https://arxiv.org/abs/1908.03673>

**A Survey of Deep Learning-based Object Detection**

- intro：From Fast R-CNN to NAS-FPN

- arXiv：<https://arxiv.org/abs/1907.09408>

**Object Detection in 20 Years: A Survey**

- intro：This work has been submitted to the IEEE TPAMI for possible publication
- arXiv：<https://arxiv.org/abs/1905.05055>

**《Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks》**

- intro: awesome


- arXiv: https://arxiv.org/abs/1809.03193

**《Deep Learning for Generic Object Detection: A Survey》**

- intro: Submitted to IJCV 2018
- arXiv: https://arxiv.org/abs/1809.02165

# Papers&Codes

## R-CNN

**Rich feature hierarchies for accurate object detection and semantic segmentation**

- intro: R-CNN
- arxiv: <http://arxiv.org/abs/1311.2524>
- supp: <http://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr-supp.pdf>
- slides: <http://www.image-net.org/challenges/LSVRC/2013/slides/r-cnn-ilsvrc2013-workshop.pdf>
- slides: <http://www.cs.berkeley.edu/~rbg/slides/rcnn-cvpr14-slides.pdf>
- github: <https://github.com/rbgirshick/rcnn>
- notes: <http://zhangliliang.com/2014/07/23/paper-note-rcnn/>
- caffe-pr("Make R-CNN the Caffe detection example"): <https://github.com/BVLC/caffe/pull/482>

## Fast R-CNN

**Fast R-CNN**

- arxiv: <http://arxiv.org/abs/1504.08083>
- slides: <http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf>
- github: <https://github.com/rbgirshick/fast-rcnn>
- github(COCO-branch): <https://github.com/rbgirshick/fast-rcnn/tree/coco>
- webcam demo: <https://github.com/rbgirshick/fast-rcnn/pull/29>
- notes: <http://zhangliliang.com/2015/05/17/paper-note-fast-rcnn/>
- notes: <http://blog.csdn.net/linj_m/article/details/48930179>
- github("Fast R-CNN in MXNet"): <https://github.com/precedenceguo/mx-rcnn>
- github: <https://github.com/mahyarnajibi/fast-rcnn-torch>
- github: <https://github.com/apple2373/chainer-simple-fast-rnn>
- github: <https://github.com/zplizzi/tensorflow-fast-rcnn>

**A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection**

- intro: CVPR 2017
- arxiv: <https://arxiv.org/abs/1704.03414>
- paper: <http://abhinavsh.info/papers/pdfs/adversarial_object_detection.pdf>
- github(Caffe): <https://github.com/xiaolonw/adversarial-frcnn>

## Faster R-CNN

**Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks**

- intro: NIPS 2015
- arxiv: <http://arxiv.org/abs/1506.01497>
- gitxiv: <http://www.gitxiv.com/posts/8pfpcvefDYn2gSgXk/faster-r-cnn-towards-real-time-object-detection-with-region>
- slides: <http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w05-FasterR-CNN.pdf>
- github(official, Matlab): <https://github.com/ShaoqingRen/faster_rcnn>
- github(Caffe): <https://github.com/rbgirshick/py-faster-rcnn>
- github(MXNet): <https://github.com/msracver/Deformable-ConvNets/tree/master/faster_rcnn>
- github(PyTorch--recommend): <https://github.com//jwyang/faster-rcnn.pytorch>
- github: <https://github.com/mitmul/chainer-faster-rcnn>
- github(Torch):: <https://github.com/andreaskoepf/faster-rcnn.torch>
- github(Torch):: <https://github.com/ruotianluo/Faster-RCNN-Densecap-torch>
- github(TensorFlow): <https://github.com/smallcorgi/Faster-RCNN_TF>
- github(TensorFlow): <https://github.com/CharlesShang/TFFRCNN>
- github(C++ demo): <https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus>
- github(Keras): <https://github.com/yhenon/keras-frcnn>
- github: <https://github.com/Eniac-Xie/faster-rcnn-resnet>
- github(C++): <https://github.com/D-X-Y/caffe-faster-rcnn/tree/dev>

**R-CNN minus R**

- intro: BMVC 2015
- arxiv: <http://arxiv.org/abs/1506.06981>

**Faster R-CNN in MXNet with distributed implementation and data parallelization**

- github: <https://github.com/dmlc/mxnet/tree/master/example/rcnn>

**Contextual Priming and Feedback for Faster R-CNN**

- intro: ECCV 2016. Carnegie Mellon University
- paper: <http://abhinavsh.info/context_priming_feedback.pdf>
- poster: <http://www.eccv2016.org/files/posters/P-1A-20.pdf>

**An Implementation of Faster RCNN with Study for Region Sampling**

- intro: Technical Report, 3 pages. CMU
- arxiv: <https://arxiv.org/abs/1702.02138>
- github: <https://github.com/endernewton/tf-faster-rcnn>
- github: https://github.com/ruotianluo/pytorch-faster-rcnn

**Interpretable R-CNN**

- intro: North Carolina State University & Alibaba
- keywords: AND-OR Graph (AOG)
- arxiv: <https://arxiv.org/abs/1711.05226>

**Domain Adaptive Faster R-CNN for Object Detection in the Wild**

- intro: CVPR 2018. ETH Zurich & ESAT/PSI
- arxiv: <https://arxiv.org/abs/1803.03243>

## Mask R-CNN

- arxiv: <http://arxiv.org/abs/1703.06870>
- github(Keras): https://github.com/matterport/Mask_RCNN
- github(Caffe2): https://github.com/facebookresearch/Detectron
- github(Pytorch): <https://github.com/wannabeOG/Mask-RCNN>
- github(MXNet): https://github.com/TuSimple/mx-maskrcnn
- github(Chainer): https://github.com/DeNA/Chainer_Mask_R-CNN

## Light-Head R-CNN

**Light-Head R-CNN: In Defense of Two-Stage Object Detector**

- intro: Tsinghua University & Megvii Inc
- arxiv: <https://arxiv.org/abs/1711.07264>
- github(offical): https://github.com/zengarden/light_head_rcnn
- github: <https://github.com/terrychenism/Deformable-ConvNets/blob/master/rfcn/symbols/resnet_v1_101_rfcn_light.py#L784>

## Cascade R-CNN

**Cascade R-CNN: Delving into High Quality Object Detection**

- arxiv: <https://arxiv.org/abs/1712.00726>
- github: <https://github.com/zhaoweicai/cascade-rcnn>

## SPP-Net

**Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition**

- intro: ECCV 2014 / TPAMI 2015
- arxiv: <http://arxiv.org/abs/1406.4729>
- github: <https://github.com/ShaoqingRen/SPP_net>
- notes: <http://zhangliliang.com/2014/09/13/paper-note-sppnet/>

**DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection**

- intro: PAMI 2016
- intro: an extension of R-CNN. box pre-training, cascade on region proposals, deformation layers and context representations
- project page: <http://www.ee.cuhk.edu.hk/%CB%9Cwlouyang/projects/imagenetDeepId/index.html>
- arxiv: <http://arxiv.org/abs/1412.5661>

**Object Detectors Emerge in Deep Scene CNNs**

- intro: ICLR 2015
- arxiv: <http://arxiv.org/abs/1412.6856>
- paper: <https://www.robots.ox.ac.uk/~vgg/rg/papers/zhou_iclr15.pdf>
- paper: <https://people.csail.mit.edu/khosla/papers/iclr2015_zhou.pdf>
- slides: <http://places.csail.mit.edu/slide_iclr2015.pdf>

**segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection**

- intro: CVPR 2015
- project(code+data): <https://www.cs.toronto.edu/~yukun/segdeepm.html>
- arxiv: <https://arxiv.org/abs/1502.04275>
- github: <https://github.com/YknZhu/segDeepM>

**Object Detection Networks on Convolutional Feature Maps**

- intro: TPAMI 2015
- keywords: NoC
- arxiv: <http://arxiv.org/abs/1504.06066>

**Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction**

- arxiv: <http://arxiv.org/abs/1504.03293>
- slides: <http://www.ytzhang.net/files/publications/2015-cvpr-det-slides.pdf>
- github: <https://github.com/YutingZhang/fgs-obj>

**DeepBox: Learning Objectness with Convolutional Networks**

- keywords: DeepBox
- arxiv: <http://arxiv.org/abs/1505.02146>
- github: <https://github.com/weichengkuo/DeepBox>

## YOLO

**You Only Look Once: Unified, Real-Time Object Detection**

[![img](https://camo.githubusercontent.com/e69d4118b20a42de4e23b9549f9a6ec6dbbb0814/687474703a2f2f706a7265646469652e636f6d2f6d656469612f66696c65732f6461726b6e65742d626c61636b2d736d616c6c2e706e67)](https://camo.githubusercontent.com/e69d4118b20a42de4e23b9549f9a6ec6dbbb0814/687474703a2f2f706a7265646469652e636f6d2f6d656469612f66696c65732f6461726b6e65742d626c61636b2d736d616c6c2e706e67)

- arxiv: <http://arxiv.org/abs/1506.02640>
- code: <https://pjreddie.com/darknet/yolov1/>
- github: <https://github.com/pjreddie/darknet>
- blog: <https://pjreddie.com/darknet/yolov1/>
- slides: <https://docs.google.com/presentation/d/1aeRvtKG21KHdD5lg6Hgyhx5rPq_ZOsGjG5rJ1HP7BbA/pub?start=false&loop=false&delayms=3000&slide=id.p>
- reddit: <https://www.reddit.com/r/MachineLearning/comments/3a3m0o/realtime_object_detection_with_yolo/>
- github: <https://github.com/gliese581gg/YOLO_tensorflow>
- github: <https://github.com/xingwangsfu/caffe-yolo>
- github: <https://github.com/frankzhangrui/Darknet-Yolo>
- github: <https://github.com/BriSkyHekun/py-darknet-yolo>
- github: <https://github.com/tommy-qichang/yolo.torch>
- github: <https://github.com/frischzenger/yolo-windows>
- github: <https://github.com/AlexeyAB/yolo-windows>
- github: <https://github.com/nilboy/tensorflow-yolo>

**darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++**

- blog: <https://thtrieu.github.io/notes/yolo-tensorflow-graph-buffer-cpp>
- github: <https://github.com/thtrieu/darkflow>

**Start Training YOLO with Our Own Data**

[![img](https://camo.githubusercontent.com/2f99b692dd7ce47d7832385f3e8a6654e680d92a/687474703a2f2f6775616e6768616e2e696e666f2f626c6f672f656e2f77702d636f6e74656e742f75706c6f6164732f323031352f31322f696d616765732d34302e6a7067)](https://camo.githubusercontent.com/2f99b692dd7ce47d7832385f3e8a6654e680d92a/687474703a2f2f6775616e6768616e2e696e666f2f626c6f672f656e2f77702d636f6e74656e742f75706c6f6164732f323031352f31322f696d616765732d34302e6a7067)

- intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.
- blog: <http://guanghan.info/blog/en/my-works/train-yolo/>
- github: <https://github.com/Guanghan/darknet>

**YOLO: Core ML versus MPSNNGraph**

- intro: Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API.
- blog: <http://machinethink.net/blog/yolo-coreml-versus-mps-graph/>
- github: <https://github.com/hollance/YOLO-CoreML-MPSNNGraph>

**TensorFlow YOLO object detection on Android**

- intro: Real-time object detection on Android using the YOLO network with TensorFlow
- github: <https://github.com/natanielruiz/android-yolo>

**Computer Vision in iOS – Object Detection**

- blog: <https://sriraghu.com/2017/07/12/computer-vision-in-ios-object-detection/>
- github:<https://github.com/r4ghu/iOS-CoreML-Yolo>

## YOLOv2

**YOLO9000: Better, Faster, Stronger**

- arxiv: <https://arxiv.org/abs/1612.08242>
- code: <http://pjreddie.com/yolo9000/>    https://pjreddie.com/darknet/yolov2/
- github(Chainer): <https://github.com/leetenki/YOLOv2>
- github(Keras): <https://github.com/allanzelener/YAD2K>
- github(PyTorch): <https://github.com/longcw/yolo2-pytorch>
- github(Tensorflow): <https://github.com/hizhangp/yolo_tensorflow>
- github(Windows): <https://github.com/AlexeyAB/darknet>
- github: <https://github.com/choasUp/caffe-yolo9000>
- github: <https://github.com/philipperemy/yolo-9000>
- github(TensorFlow): <https://github.com/KOD-Chen/YOLOv2-Tensorflow>
- github(Keras): <https://github.com/yhcc/yolo2>
- github(Keras): <https://github.com/experiencor/keras-yolo2>
- github(TensorFlow): <https://github.com/WojciechMormul/yolo2>

**darknet_scripts**

- intro: Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors?
- github: <https://github.com/Jumabek/darknet_scripts>

**Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2**

- github: <https://github.com/AlexeyAB/Yolo_mark>

**LightNet: Bringing pjreddie's DarkNet out of the shadows**

<https://github.com//explosion/lightnet>

**YOLO v2 Bounding Box Tool**

- intro: Bounding box labeler tool to generate the training data in the format YOLO v2 requires.
- github: <https://github.com/Cartucho/yolo-boundingbox-labeler-GUI>

**Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors**

- intro: **LRM** is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded.
- arxiv: https://arxiv.org/abs/1804.04606

**Object detection at 200 Frames Per Second**

- intro: faster than Tiny-Yolo-v2
- arxiv: https://arxiv.org/abs/1805.06361

**Event-based Convolutional Networks for Object Detection in Neuromorphic Cameras**

- intro: YOLE--Object Detection in Neuromorphic Cameras
- arxiv:https://arxiv.org/abs/1805.07931

**OmniDetector: With Neural Networks to Bounding Boxes**

- intro: a person detector on n fish-eye images of indoor scenes（NIPS 2018）
- arxiv:https://arxiv.org/abs/1805.08503
- datasets:https://gitlab.com/omnidetector/omnidetector

## YOLOv3

**YOLOv3: An Incremental Improvement**

- arxiv:https://arxiv.org/abs/1804.02767
- paper:https://pjreddie.com/media/files/papers/YOLOv3.pdf
- code: <https://pjreddie.com/darknet/yolo/>
- github(Official):https://github.com/pjreddie/darknet
- github:https://github.com/mystic123/tensorflow-yolo-v3
- github:https://github.com/experiencor/keras-yolo3
- github:https://github.com/qqwweee/keras-yolo3
- github:https://github.com/marvis/pytorch-yolo3
- github:https://github.com/ayooshkathuria/pytorch-yolo-v3
- github:https://github.com/ayooshkathuria/YOLO_v3_tutorial_from_scratch
- github:https://github.com/eriklindernoren/PyTorch-YOLOv3
- github:https://github.com/ultralytics/yolov3
- github:https://github.com/BobLiu20/YOLOv3_PyTorch
- github:https://github.com/andy-yun/pytorch-0.4-yolov3
- github:https://github.com/DeNA/PyTorch_YOLOv3

## YOLT

**You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery**

- intro: Small Object Detection


- arxiv:https://arxiv.org/abs/1805.09512
- github:https://github.com/avanetten/yolt

## SSD

**SSD: Single Shot MultiBox Detector**

[![img](https://camo.githubusercontent.com/ad9b147ed3a5f48ffb7c3540711c15aa04ce49c6/687474703a2f2f7777772e63732e756e632e6564752f7e776c69752f7061706572732f7373642e706e67)](https://camo.githubusercontent.com/ad9b147ed3a5f48ffb7c3540711c15aa04ce49c6/687474703a2f2f7777772e63732e756e632e6564752f7e776c69752f7061706572732f7373642e706e67)

- intro: ECCV 2016 Oral
- arxiv: <http://arxiv.org/abs/1512.02325>
- paper: <http://www.cs.unc.edu/~wliu/papers/ssd.pdf>
- slides: [http://www.cs.unc.edu/%7Ewliu/papers/ssd_eccv2016_slide.pdf](http://www.cs.unc.edu/~wliu/papers/ssd_eccv2016_slide.pdf)
- github(Official): <https://github.com/weiliu89/caffe/tree/ssd>
- video: <http://weibo.com/p/2304447a2326da963254c963c97fb05dd3a973>
- github: <https://github.com/zhreshold/mxnet-ssd>
- github: <https://github.com/zhreshold/mxnet-ssd.cpp>
- github: <https://github.com/rykov8/ssd_keras>
- github: <https://github.com/balancap/SSD-Tensorflow>
- github: <https://github.com/amdegroot/ssd.pytorch>
- github(Caffe): <https://github.com/chuanqi305/MobileNet-SSD>

**What's the diffience in performance between this new code you pushed and the previous code? #327**

<https://github.com/weiliu89/caffe/issues/327>

## DSSD

**DSSD : Deconvolutional Single Shot Detector**

- intro: UNC Chapel Hill & Amazon Inc
- arxiv: <https://arxiv.org/abs/1701.06659>
- github: <https://github.com/chengyangfu/caffe/tree/dssd>
- github: <https://github.com/MTCloudVision/mxnet-dssd>
- demo: <http://120.52.72.53/www.cs.unc.edu/c3pr90ntc0td/~cyfu/dssd_lalaland.mp4>

**Enhancement of SSD by concatenating feature maps for object detection**

- intro: rainbow SSD (R-SSD)
- arxiv: <https://arxiv.org/abs/1705.09587>

**Context-aware Single-Shot Detector**

- keywords: CSSD, DiCSSD, DeCSSD, effective receptive fields (ERFs), theoretical receptive fields (TRFs)
- arxiv: <https://arxiv.org/abs/1707.08682>

**Feature-Fused SSD: Fast Detection for Small Objects**

<https://arxiv.org/abs/1709.05054>

## FSSD

**FSSD: Feature Fusion Single Shot Multibox Detector**

<https://arxiv.org/abs/1712.00960>

**Weaving Multi-scale Context for Single Shot Detector**

- intro: WeaveNet
- keywords: fuse multi-scale information
- arxiv: <https://arxiv.org/abs/1712.03149>

## ESSD

**Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network**

<https://arxiv.org/abs/1801.05918>

**Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection**

<https://arxiv.org/abs/1802.06488>

## MDSSD

**MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects**

- arxiv: https://arxiv.org/abs/1805.07009

## Pelee

**Pelee: A Real-Time Object Detection System on Mobile Devices**

https://github.com/Robert-JunWang/Pelee

- intro: (ICLR 2018 workshop track)


- arxiv: https://arxiv.org/abs/1804.06882
- github: https://github.com/Robert-JunWang/Pelee

## Fire SSD

**Fire SSD: Wide Fire Modules based Single Shot Detector on Edge Device**

- intro:low cost, fast speed and high mAP on  factor edge computing devices


- arxiv:https://arxiv.org/abs/1806.05363

## R-FCN

**R-FCN: Object Detection via Region-based Fully Convolutional Networks**

- arxiv: <http://arxiv.org/abs/1605.06409>
- github: <https://github.com/daijifeng001/R-FCN>
- github(MXNet): <https://github.com/msracver/Deformable-ConvNets/tree/master/rfcn>
- github: <https://github.com/Orpine/py-R-FCN>
- github: <https://github.com/PureDiors/pytorch_RFCN>
- github: <https://github.com/bharatsingh430/py-R-FCN-multiGPU>
- github: <https://github.com/xdever/RFCN-tensorflow>

**R-FCN-3000 at 30fps: Decoupling Detection and Classification**

<https://arxiv.org/abs/1712.01802>

**Recycle deep features for better object detection**

- arxiv: <http://arxiv.org/abs/1607.05066>

## FPN

**Feature Pyramid Networks for Object Detection**

- intro: Facebook AI Research
- arxiv: <https://arxiv.org/abs/1612.03144>

**Action-Driven Object Detection with Top-Down Visual Attentions**

- arxiv: <https://arxiv.org/abs/1612.06704>

**Beyond Skip Connections: Top-Down Modulation for Object Detection**

- intro: CMU & UC Berkeley & Google Research
- arxiv: <https://arxiv.org/abs/1612.06851>

**Wide-Residual-Inception Networks for Real-time Object Detection**

- intro: Inha University
- arxiv: <https://arxiv.org/abs/1702.01243>

**Attentional Network for Visual Object Detection**

- intro: University of Maryland & Mitsubishi Electric Research Laboratories
- arxiv: <https://arxiv.org/abs/1702.01478>

**Learning Chained Deep Features and Classifiers for Cascade in Object Detection**

- keykwords: CC-Net
- intro: chained cascade network (CC-Net). 81.1% mAP on PASCAL VOC 2007
- arxiv: <https://arxiv.org/abs/1702.07054>

**DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling**

- intro: ICCV 2017 (poster)
- arxiv: <https://arxiv.org/abs/1703.10295>

**Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries**

- intro: CVPR 2017
- arxiv: <https://arxiv.org/abs/1704.03944>

**Spatial Memory for Context Reasoning in Object Detection**

- arxiv: <https://arxiv.org/abs/1704.04224>

**Accurate Single Stage Detector Using Recurrent Rolling Convolution**

- intro: CVPR 2017. SenseTime
- keywords: Recurrent Rolling Convolution (RRC)
- arxiv: <https://arxiv.org/abs/1704.05776>
- github: <https://github.com/xiaohaoChen/rrc_detection>

**Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection**

<https://arxiv.org/abs/1704.05775>

**LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems**

- intro: Embedded Vision Workshop in CVPR. UC San Diego & Qualcomm Inc
- arxiv: <https://arxiv.org/abs/1705.05922>

**Point Linking Network for Object Detection**

- intro: Point Linking Network (PLN)
- arxiv: <https://arxiv.org/abs/1706.03646>

**Perceptual Generative Adversarial Networks for Small Object Detection**

<https://arxiv.org/abs/1706.05274>

**Few-shot Object Detection**

<https://arxiv.org/abs/1706.08249>

**Yes-Net: An effective Detector Based on Global Information**

<https://arxiv.org/abs/1706.09180>

**SMC Faster R-CNN: Toward a scene-specialized multi-object detector**

<https://arxiv.org/abs/1706.10217>

**Towards lightweight convolutional neural networks for object detection**

<https://arxiv.org/abs/1707.01395>

**RON: Reverse Connection with Objectness Prior Networks for Object Detection**

- intro: CVPR 2017
- arxiv: <https://arxiv.org/abs/1707.01691>
- github: <https://github.com/taokong/RON>

**Mimicking Very Efficient Network for Object Detection**

- intro: CVPR 2017. SenseTime & Beihang University
- paper: <http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Mimicking_Very_Efficient_CVPR_2017_paper.pdf>

**Residual Features and Unified Prediction Network for Single Stage Detection**

<https://arxiv.org/abs/1707.05031>

**Deformable Part-based Fully Convolutional Network for Object Detection**

- intro: BMVC 2017 (oral). Sorbonne Universités & CEDRIC
- arxiv: <https://arxiv.org/abs/1707.06175>

**Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors**

- intro: ICCV 2017
- arxiv: <https://arxiv.org/abs/1707.06399>

**Recurrent Scale Approximation for Object Detection in CNN**

- intro: ICCV 2017
- keywords: Recurrent Scale Approximation (RSA)
- arxiv: <https://arxiv.org/abs/1707.09531>
- github: <https://github.com/sciencefans/RSA-for-object-detection>

## DSOD

**DSOD: Learning Deeply Supervised Object Detectors from Scratch**

![img](https://user-images.githubusercontent.com/3794909/28934967-718c9302-78b5-11e7-89ee-8b514e53e23c.png)

- intro: ICCV 2017. Fudan University & Tsinghua University & Intel Labs China
- arxiv: <https://arxiv.org/abs/1708.01241>
- github: <https://github.com/szq0214/DSOD>
- github:https://github.com/Windaway/DSOD-Tensorflow
- github:https://github.com/chenyuntc/dsod.pytorch

**Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids**

- arxiv:https://arxiv.org/abs/1712.00886
- github:https://github.com/szq0214/GRP-DSOD

**Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages**

- intro: BMVC 2018
- arXiv: https://arxiv.org/abs/1807.11013

**Object Detection from Scratch with Deep Supervision**

- intro: This is an extended version of DSOD
- arXiv: https://arxiv.org/abs/1809.09294

## RetinaNet

**Focal Loss for Dense Object Detection**

- intro: ICCV 2017 Best student paper award. Facebook AI Research
- keywords: RetinaNet
- arxiv: <https://arxiv.org/abs/1708.02002>

**CoupleNet: Coupling Global Structure with Local Parts for Object Detection**

- intro: ICCV 2017
- arxiv: <https://arxiv.org/abs/1708.02863>

**Incremental Learning of Object Detectors without Catastrophic Forgetting**

- intro: ICCV 2017. Inria
- arxiv: <https://arxiv.org/abs/1708.06977>

**Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection**

<https://arxiv.org/abs/1709.04347>

**StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection**

<https://arxiv.org/abs/1709.05788>

**Dynamic Zoom-in Network for Fast Object Detection in Large Images**

<https://arxiv.org/abs/1711.05187>

**Zero-Annotation Object Detection with Web Knowledge Transfer**

- intro: NTU, Singapore & Amazon
- keywords: multi-instance multi-label domain adaption learning framework
- arxiv: <https://arxiv.org/abs/1711.05954>

## MegDet

**MegDet: A Large Mini-Batch Object Detector**

- intro: Peking University & Tsinghua University & Megvii Inc
- arxiv: <https://arxiv.org/abs/1711.07240>

**Receptive Field Block Net for Accurate and Fast Object Detection**

- intro: RFBNet
- arxiv: <https://arxiv.org/abs/1711.07767>
- github: <https://github.com//ruinmessi/RFBNet>

**An Analysis of Scale Invariance in Object Detection - SNIP**

- arxiv: <https://arxiv.org/abs/1711.08189>
- github: <https://github.com/bharatsingh430/snip>

**Feature Selective Networks for Object Detection**

<https://arxiv.org/abs/1711.08879>

**Learning a Rotation Invariant Detector with Rotatable Bounding Box**

- arxiv: <https://arxiv.org/abs/1711.09405>
- github: <https://github.com/liulei01/DRBox>

**Scalable Object Detection for Stylized Objects**

- intro: Microsoft AI & Research Munich
- arxiv: <https://arxiv.org/abs/1711.09822>

**Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids**

- arxiv: <https://arxiv.org/abs/1712.00886>
- github: <https://github.com/szq0214/GRP-DSOD>

**Deep Regionlets for Object Detection**

- keywords: region selection network, gating network
- arxiv: <https://arxiv.org/abs/1712.02408>

**Training and Testing Object Detectors with Virtual Images**

- intro: IEEE/CAA Journal of Automatica Sinica
- arxiv: <https://arxiv.org/abs/1712.08470>

**Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video**

- keywords: object mining, object tracking, unsupervised object discovery by appearance-based clustering, self-supervised detector adaptation
- arxiv: <https://arxiv.org/abs/1712.08832>

**Spot the Difference by Object Detection**

- intro: Tsinghua University & JD Group
- arxiv: <https://arxiv.org/abs/1801.01051>

**Localization-Aware Active Learning for Object Detection**

- arxiv: <https://arxiv.org/abs/1801.05124>

**Object Detection with Mask-based Feature Encoding**

- arxiv: <https://arxiv.org/abs/1802.03934>

**LSTD: A Low-Shot Transfer Detector for Object Detection**

- intro: AAAI 2018
- arxiv: <https://arxiv.org/abs/1803.01529>

**Pseudo Mask Augmented Object Detection**

<https://arxiv.org/abs/1803.05858>

**Revisiting RCNN: On Awakening the Classification Power of Faster RCNN**

<https://arxiv.org/abs/1803.06799>

**Learning Region Features for Object Detection**

- intro: Peking University & MSRA
- arxiv: <https://arxiv.org/abs/1803.07066>

**Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection**

- intro: Singapore Management University & Zhejiang University
- arxiv: <https://arxiv.org/abs/1803.08208>

**Object Detection for Comics using Manga109 Annotations**

- intro: University of Tokyo & National Institute of Informatics, Japan
- arxiv: <https://arxiv.org/abs/1803.08670>

**Task-Driven Super Resolution: Object Detection in Low-resolution Images**

- arxiv: <https://arxiv.org/abs/1803.11316>

**Transferring Common-Sense Knowledge for Object Detection**

- arxiv: <https://arxiv.org/abs/1804.01077>

**Multi-scale Location-aware Kernel Representation for Object Detection**

- intro: CVPR 2018
- arxiv: <https://arxiv.org/abs/1804.00428>
- github: <https://github.com/Hwang64/MLKP>


**Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors**

- intro: National University of Defense Technology
- arxiv: https://arxiv.org/abs/1804.04606

**Robust Physical Adversarial Attack on Faster R-CNN Object Detector**

- arxiv: https://arxiv.org/abs/1804.05810

## RefineNet

**Single-Shot Refinement Neural Network for Object Detection**

- intro: CVPR 2018

- arxiv: <https://arxiv.org/abs/1711.06897>
- github: <https://github.com/sfzhang15/RefineDet>
- github: https://github.com/lzx1413/PytorchSSD
- github: https://github.com/ddlee96/RefineDet_mxnet
- github: https://github.com/MTCloudVision/RefineDet-Mxnet

## DetNet

**DetNet: A Backbone network for Object Detection**

- intro: Tsinghua University & Face++
- arxiv: https://arxiv.org/abs/1804.06215


## SSOD

**Self-supervisory Signals for Object Discovery and Detection**

- Google Brain
- arxiv:https://arxiv.org/abs/1806.03370

## CornerNet

**CornerNet: Detecting Objects as Paired Keypoints**

- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1808.01244
- github: <https://github.com/umich-vl/CornerNet>

## M2Det

**M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network**

- intro: AAAI 2019
- arXiv: https://arxiv.org/abs/1811.04533
- github: https://github.com/qijiezhao/M2Det

## 3D Object Detection

**3D Backbone Network for 3D Object Detection**

- arXiv: https://arxiv.org/abs/1901.08373

**LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDARs**

- arxiv: https://arxiv.org/abs/1805.04902
- github: https://github.com/CPFL/Autoware/tree/feature/cnn_lidar_detection


## ZSD（Zero-Shot Object Detection）

**Zero-Shot Detection**

- intro: Australian National University
- keywords: YOLO
- arxiv: <https://arxiv.org/abs/1803.07113>

**Zero-Shot Object Detection**

- arxiv: https://arxiv.org/abs/1804.04340

**Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts**

- arxiv: https://arxiv.org/abs/1803.06049

**Zero-Shot Object Detection by Hybrid Region Embedding**

- arxiv: https://arxiv.org/abs/1805.06157

## OSD（One-Shot Object Detection）

**Comparison Network for One-Shot Conditional Object Detection**

- arXiv: https://arxiv.org/abs/1904.02317

**One-Shot Object Detection**

RepMet: Representative-based metric learning for classification and one-shot object detection

- intro: IBM Research AI
- arxiv:https://arxiv.org/abs/1806.04728
- github: TODO

## Weakly Supervised Object Detection

**Weakly Supervised Object Detection in Artworks**

- intro: ECCV 2018 Workshop Computer Vision for Art Analysis
- arXiv: https://arxiv.org/abs/1810.02569
- Datasets: https://wsoda.telecom-paristech.fr/downloads/dataset/IconArt_v1.zip

**Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation**

- intro: CVPR 2018
- arXiv: https://arxiv.org/abs/1803.11365
- homepage: https://naoto0804.github.io/cross_domain_detection/
- paper: http://openaccess.thecvf.com/content_cvpr_2018/html/Inoue_Cross-Domain_Weakly-Supervised_Object_CVPR_2018_paper.html
- github: https://github.com/naoto0804/cross-domain-detection

## Softer-NMS

**《Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection》**

- intro: CMU & Face++
- arXiv: https://arxiv.org/abs/1809.08545
- github: https://github.com/yihui-he/softer-NMS

## 2019

**Feature Selective Anchor-Free Module for Single-Shot Object Detection**

- intro: CVPR 2019

- arXiv: https://arxiv.org/abs/1903.00621

**Object Detection based on Region Decomposition and Assembly**

- intro: AAAI 2019

- arXiv: https://arxiv.org/abs/1901.08225

**Bottom-up Object Detection by Grouping Extreme and Center Points**

- intro: one stage 43.2% on COCO test-dev
- arXiv: https://arxiv.org/abs/1901.08043
- github: https://github.com/xingyizhou/ExtremeNet

**ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features**

- intro: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

- arXiv: https://arxiv.org/abs/1901.07925

**Consistent Optimization for Single-Shot Object Detection**

- intro: improves RetinaNet from 39.1 AP to 40.1 AP on COCO datase

- arXiv: https://arxiv.org/abs/1901.06563

**Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes**

- arXiv: https://arxiv.org/abs/1901.03796

**RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free**

- arXiv: https://arxiv.org/abs/1901.03353
- github: https://github.com/chengyangfu/retinamask

**Region Proposal by Guided Anchoring**

- intro: CUHK - SenseTime Joint Lab
- arXiv: https://arxiv.org/abs/1901.03278

**Scale-Aware Trident Networks for Object Detection**

- intro: mAP of **48.4** on the COCO dataset
- arXiv: https://arxiv.org/abs/1901.01892

## 2018

**Large-Scale Object Detection of Images from Network Cameras in Variable Ambient Lighting Conditions**

- arXiv: https://arxiv.org/abs/1812.11901

**Strong-Weak Distribution Alignment for Adaptive Object Detection**

- arXiv: https://arxiv.org/abs/1812.04798

**AutoFocus: Efficient Multi-Scale Inference**

- intro: AutoFocus obtains an **mAP of 47.9%** (68.3% at 50% overlap) on the **COCO test-dev** set while processing **6.4 images per second on a Titan X (Pascal) GPU** 
- arXiv: https://arxiv.org/abs/1812.01600

**NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection**

- intro: Google Could
- arXiv: https://arxiv.org/abs/1812.00124

**SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection**

- intro: UC Berkeley
- arXiv: https://arxiv.org/abs/1812.00929

**Grid R-CNN**

- intro: SenseTime
- arXiv: https://arxiv.org/abs/1811.12030

**Deformable ConvNets v2: More Deformable, Better Results**

- intro: Microsoft Research Asia

- arXiv: https://arxiv.org/abs/1811.11168

**Anchor Box Optimization for Object Detection**

- intro: Microsoft Research
- arXiv: https://arxiv.org/abs/1812.00469

**Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects**

- intro: https://arxiv.org/abs/1811.12152

**NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection**

- arXiv: https://arxiv.org/abs/1812.00124

**Learning RoI Transformer for Detecting Oriented Objects in Aerial Images**

- arXiv: https://arxiv.org/abs/1812.00155

**Integrated Object Detection and Tracking with Tracklet-Conditioned Detection**

- intro: Microsoft Research Asia
- arXiv: https://arxiv.org/abs/1811.11167

**Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection**

- arXiv: https://arxiv.org/abs/1811.11318

 **Gradient Harmonized Single-stage Detector**

- intro: AAAI 2019
- arXiv: https://arxiv.org/abs/1811.05181

**CFENet: Object Detection with Comprehensive Feature Enhancement Module**

- intro: ACCV 2018
- github: https://github.com/qijiezhao/CFENet

**DeRPN: Taking a further step toward more general object detection**

- intro: AAAI 2019
- arXiv: https://arxiv.org/abs/1811.06700
- github: https://github.com/HCIILAB/DeRPN

**Hybrid Knowledge Routed Modules for Large-scale Object Detection**

- intro: Sun Yat-Sen University & Huawei Noah’s Ark Lab
- arXiv: https://arxiv.org/abs/1810.12681
- github: https://github.com/chanyn/HKRM

**《Receptive Field Block Net for Accurate and Fast Object Detection》**

- intro: ECCV 2018
- arXiv: [https://arxiv.org/abs/1711.07767](https://arxiv.org/abs/1711.07767)
- github: [https://github.com/ruinmessi/RFBNet](https://github.com/ruinmessi/RFBNet)

**Deep Feature Pyramid Reconfiguration for Object Detection**

- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1808.07993

**Unsupervised Hard Example Mining from Videos for Improved Object Detection**

- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1808.04285

**Acquisition of Localization Confidence for Accurate Object Detection**

- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1807.11590
- github: https://github.com/vacancy/PreciseRoIPooling

**Toward Scale-Invariance and Position-Sensitive Region Proposal Networks**

- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1807.09528

**MetaAnchor: Learning to Detect Objects with Customized Anchors**

- arxiv: https://arxiv.org/abs/1807.00980

**Relation Network for Object Detection**

- intro: CVPR 2018
- arxiv: https://arxiv.org/abs/1711.11575
- github:https://github.com/msracver/Relation-Networks-for-Object-Detection

**Quantization Mimic: Towards Very Tiny CNN for Object Detection**

- Tsinghua University1 & The Chinese University of Hong Kong2 &SenseTime3
- arxiv: https://arxiv.org/abs/1805.02152

**Learning Rich Features for Image Manipulation Detection**

- intro: CVPR 2018 Camera Ready
- arxiv: https://arxiv.org/abs/1805.04953

**SNIPER: Efficient Multi-Scale Training**

- arxiv:https://arxiv.org/abs/1805.09300
- github:https://github.com/mahyarnajibi/SNIPER

**Soft Sampling for Robust Object Detection**

- intro: the robustness of object detection under the presence of missing annotations
- arxiv:https://arxiv.org/abs/1806.06986

**Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria**

- intro: TNNLS 2018
- arxiv:https://arxiv.org/abs/1807.00147
- code: http://kezewang.com/codes/ASM_ver1.zip

## Other

**R3-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos**

- arxiv: https://arxiv.org/abs/1808.05560
- youtube: https://youtu.be/xCYD-tYudN0

# Detection Toolbox

- [Detectron(FAIR)](https://github.com/facebookresearch/Detectron): Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including [Mask R-CNN](https://arxiv.org/abs/1703.06870). It is written in Python and powered by the [Caffe2](https://github.com/caffe2/caffe2) deep learning framework.
- [Detectron2](https://github.com/facebookresearch/detectron2): Detectron2 is FAIR's next-generation research platform for object detection and segmentation.
- [maskrcnn-benchmark(FAIR)](https://github.com/facebookresearch/maskrcnn-benchmark): Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.
- [mmdetection(SenseTime&CUHK)](https://github.com/open-mmlab/mmdetection): mmdetection is an open source object detection toolbox based on PyTorch. It is a part of the open-mmlab project developed by [Multimedia Laboratory, CUHK](http://mmlab.ie.cuhk.edu.hk/).
Download .txt 
gitextract_t3iari6u/

├── Date.md
└── README.md
Download .json
Condensed preview — 2 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (50K chars). 
[
  {
    "path": "Date.md",
    "chars": 8891,
    "preview": "2019-10-11 Update 1 Project\n\n**Detectron2**\n\n- intro: Detectron2 is FAIR's next-generation research platform for object "
  },
  {
    "path": "README.md",
    "chars": 38903,
    "preview": "# object-detection\r\n\r\n[TOC]\r\n\r\nThis is a list of awesome articles about object detection. If you want to read the paper "
  }
]

About this extraction

This page contains the full source code of the amusi/awesome-object-detection GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 2 files (46.7 KB), approximately 14.6k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.

Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.

Extract another repo
Copied to clipboard!