汇总近年来图像分类目标检测语义分割自然场景文字识别与检测超分辨率生成对抗网络图像理解等相关论文.zip

# deep learning object detection A paper list of object detection using deep learning. I wrote this page with reference to [this survey paper](https://arxiv.org/pdf/1809.02165v1.pdf) and searching and searching.. *Last updated: 2020/07/17* #### 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. ## ## Table of Contents - [Paper list from 2014 to now(2019)](https://github.com/hoya012/deep_learning_object_detection/blob/master/README.md#paper-list-from-2014-to-now2019) - [Performance table](https://github.com/hoya012/deep_learning_object_detection/blob/master/README.md#performance-table) - Papers - [2014](https://github.com/hoya012/deep_learning_object_detection/blob/master/README.md#2014) - [2015](https://github.com/hoya012/deep_learning_object_detection/blob/master/README.md#2015) - [2016](https://github.com/hoya012/deep_learning_object_detection/blob/master/README.md#2016) - [2017](https://github.com/hoya012/deep_learning_object_detection/blob/master/README.md#2017) - [2018](https://github.com/hoya012/deep_learning_object_detection/blob/master/README.md#2018) - [2019](https://github.com/hoya012/deep_learning_object_detection/blob/master/README.md#2019) - [2020](https://github.com/hoya012/deep_learning_object_detection/blob/master/README.md#2020) - [Dataset Papers](https://github.com/hoya012/deep_learning_object_detection/blob/master/README.md#dataset-papers) ## ## 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. <p align="center"> <img width="1000" src="/assets/deep_learning_object_detection_history.PNG" "Example of object detection."> </p> ## ## 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