deep_learning_object_detection README

The project's README from github.com/hoya012/deep_learning_object_detection. As one markdown file: README.md.

deep learning object detection

A paper list of object detection using deep learning. I wrote this page with reference to this survey paper and searching and searching..

Last updated: 2020/09/22

Update log

2018/9/18 - update all of recent papers and make some diagram about history of object detection using deep learning. 2018/9/26 - update codes of papers. (official and unofficial)
2018/october - update 5 papers and performance table.
2018/november - update 9 papers.
2018/december - update 8 papers and and performance table and add new diagram(2019 version!!).
2019/january - update 4 papers and and add commonly used datasets.
2019/february - update 3 papers.
2019/march - update figure and code links.
2019/april - remove author's names and update ICLR 2019 & CVPR 2019 papers.
2019/may - update CVPR 2019 papers.
2019/june - update CVPR 2019 papers and dataset paper.
2019/july - update BMVC 2019 papers and some of ICCV 2019 papers.
2019/september - update NeurIPS 2019 papers and ICCV 2019 papers.
2019/november - update some of AAAI 2020 papers and other papers.
2020/january - update ICLR 2020 papers and other papers.
2020/may - update CVPR 2020 papers and other papers.
2020/june - update arxiv papers.
2020/august - update paper links.

Table of Contents

Paper list from 2014 to now(2019)

The part highlighted with red characters means papers that i think "must-read". However, it is my personal opinion and other papers are important too, so I recommend to read them if you have time.

Performance table

FPS(Speed) index is related to the hardware spec(e.g. CPU, GPU, RAM, etc), so it is hard to make an equal comparison. The solution is to measure the performance of all models on hardware with equivalent specifications, but it is very difficult and time consuming.

Detector VOC07 (mAP@IoU=0.5) VOC12 (mAP@IoU=0.5) COCO (mAP@IoU=0.5:0.95) Published In
R-CNN 58.5 - - CVPR'14
SPP-Net 59.2 - - ECCV'14
MR-CNN 78.2 (07+12) 73.9 (07+12) - ICCV'15
Fast R-CNN 70.0 (07+12) 68.4 (07++12) 19.7 ICCV'15
Faster R-CNN 73.2 (07+12) 70.4 (07++12) 21.9 NIPS'15
YOLO v1 66.4 (07+12) 57.9 (07++12) - CVPR'16
G-CNN 66.8 66.4 (07+12) - CVPR'16
AZNet 70.4 - 22.3 CVPR'16
ION 80.1 77.9 33.1 CVPR'16
HyperNet 76.3 (07+12) 71.4 (07++12) - CVPR'16
OHEM 78.9 (07+12) 76.3 (07++12) 22.4 CVPR'16
MPN - - 33.2 BMVC'16
SSD 76.8 (07+12) 74.9 (07++12) 31.2 ECCV'16
GBDNet 77.2 (07+12) - 27.0 ECCV'16
CPF 76.4 (07+12) 72.6 (07++12) - ECCV'16
R-FCN 79.5 (07+12) 77.6 (07++12) 29.9 NIPS'16
DeepID-Net 69.0 - - PAMI'16
NoC 71.6 (07+12) 68.8 (07+12) 27.2 TPAMI'16
DSSD 81.5 (07+12) 80.0 (07++12) 33.2 arXiv'17
TDM - - 37.3 CVPR'17
FPN - - 36.2 CVPR'17
YOLO v2 78.6 (07+12) 73.4 (07++12) - CVPR'17
RON 77.6 (07+12) 75.4 (07++12) 27.4 CVPR'17
DeNet 77.1 (07+12) 73.9 (07++12) 33.8 ICCV'17
CoupleNet 82.7 (07+12) 80.4 (07++12) 34.4 ICCV'17
RetinaNet - - 39.1 ICCV'17
DSOD 77.7 (07+12) 76.3 (07++12) - ICCV'17
SMN 70.0 - - ICCV'17
Light-Head R-CNN - - 41.5 arXiv'17
YOLO v3 - - 33.0 arXiv'18
SIN 76.0 (07+12) 73.1 (07++12) 23.2 CVPR'18
STDN 80.9 (07+12) - - CVPR'18
RefineDet 83.8 (07+12) 83.5 (07++12) 41.8 CVPR'18
SNIP - - 45.7 CVPR'18
Relation-Network - - 32.5 CVPR'18
Cascade R-CNN - - 42.8 CVPR'18
MLKP 80.6 (07+12) 77.2 (07++12) 28.6 CVPR'18
Fitness-NMS - - 41.8 CVPR'18
RFBNet 82.2 (07+12) - - ECCV'18
CornerNet - - 42.1 ECCV'18
PFPNet 84.1 (07+12) 83.7 (07++12) 39.4 ECCV'18
Pelee 70.9 (07+12) - - NIPS'18
HKRM 78.8 (07+12) - 37.8 NIPS'18
M2Det - - 44.2 AAAI'19
R-DAD 81.2 (07++12) 82.0 (07++12) 43.1 AAAI'19
ScratchDet 84.1 (07++12) 83.6 (07++12) 39.1 CVPR'19
Libra R-CNN - - 43.0 CVPR'19
Reasoning-RCNN 82.5 (07++12) - 43.2 CVPR'19
FSAF - - 44.6 CVPR'19
AmoebaNet + NAS-FPN - - 47.0 CVPR'19
Cascade-RetinaNet - - 41.1 CVPR'19
HTC - - 47.2 CVPR'19
TridentNet - - 48.4 ICCV'19
DAFS 85.3 (07+12) 83.1 (07++12) 40.5 ICCV'19
Auto-FPN 81.8 (07++12) - 40.5 ICCV'19
FCOS - - 44.7 ICCV'19
FreeAnchor - - 44.8 NeurIPS'19
DetNAS 81.5 (07++12) - 42.0 NeurIPS'19
NATS - - 42.0 NeurIPS'19
AmoebaNet + NAS-FPN + AA - - 50.7 arXiv'19
SpineNet - - 52.1 arXiv'19
CBNet - - 53.3 AAAI'20
EfficientDet - - 52.6 CVPR'20
DetectoRS - - 54.7 arXiv'20

2014

2015

2016

2017

2018

2019

2020

Dataset Papers

Statistics of commonly used object detection datasets. The Table came from this survey paper.

Challenge Object Classes Number of Images Number of Annotated Images
Train Val Test Train Val
PASCAL VOC Object Detection Challenge
VOC07 20 2,501 2,510 4,952 6,301 (7,844) 6,307 (7,818)
VOC08 20 2,111 2,221 4,133 5,082 (6,337) 5,281 (6,347)
VOC09 20 3,473 3,581 6,650 8,505 (9,760) 8,713 (9,779)
VOC10 20 4,998 5,105 9,637 11,577 (13,339) 11,797 (13,352)
VOC11 20 5,717 5,823 10,994 13,609 (15,774) 13,841 (15,787)
VOC12 20 5,717 5,823 10,991 13,609 (15,774) 13,841 (15,787)
ILSVRC Object Detection Challenge
ILSVRC13 200 395,909 20,121 40,152 345,854 55,502
ILSVRC14 200 456,567 20,121 40,152 478,807 55,502
ILSVRC15 200 456,567 20,121 51,294 478,807 55,502
ILSVRC16 200 456,567 20,121 60,000 478,807 55,502
ILSVRC17 200 456,567 20,121 65,500 478,807 55,502
MS COCO Object Detection Challenge
MS COCO15 80 82,783 40,504 81,434 604,907 291,875
MS COCO16 80 82,783 40,504 81,434 604,907 291,875
MS COCO17 80 118,287 5,000 40,670 860,001 36,781
MS COCO18 80 118,287 5,000 40,670 860,001 36,781
Open Images Object Detection Challenge
OID18 500 1,743,042 41,620 125,436 12,195,144 ―

The papers related to datasets used mainly in Object Detection are as follows.

Contact & Feedback

If you have any suggestions about papers, feel free to mail me :)

deep_learning_object_detection (hoya012/deep_learning_object_detection)

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Deep_learning_object_detection
Что это такое?Это тщательно составленный список статей об обнаружении объектов с помощью глубокого обучения: он охватывает область с 2014 по 2020 год и содержит ссылки на PDF каждой статьи и её код.
Это программа, которую нужно установить, или просто страница для чтения?Просто страница: один длинный README на GitHub, который вы читаете. Устанавливать и запускать ничего не нужно — ценность в подобранных ссылках, а не в коде этого репозитория.
Насколько честно сравнивать скорости (FPS) в таблице?Не совсем честно: автор предупреждает, что FPS зависит от железа (CPU, GPU, RAM), поэтому скорости из разных статей трудно сравнивать на равных.
Какие детекторы есть в списке?Классика этой области: семейство R-CNN (Fast, Faster, Mask, Cascade), YOLO v1-v3, SSD и DSSD, RetinaNet, FPN, CornerNet, FCOS, RefineDet, EfficientDet, DetectoRS, а также детекторы на основе NAS.
Там есть и про датасеты?Да — есть отдельный раздел Dataset Papers, а в обновлении за январь 2019 года к статьям добавили часто используемые датасеты исследований по обнаружению объектов.
Зачем автор собрал эту страницу?Он опирался на обзорную статью и на собственный поиск материалов, а ещё сам нарисовал схемы, показывающие историю обнаружения объектов с помощью глубокого обучения.
What are similar projects?techniques, awesome-deep-vision, AI-Papers-of-the-Week, DeepLearning, awesome-deep-learning-papers, awesome-semantic-segmentation, Deep-Learning-Papers-Reading-Roadmap, transferlearning
Which topics is it in?deeplearning, cnn, datasets, deep-neural-networks, arxiv, object-detection, papers, academic-research, reference, computer-vision, learning-resources, benchmark
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hoya012
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