Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A paper list of object detection using deep learning.
| Date | Stars |
|---|---|
| 2026-07-24 | 11385 |
| 2026-07-25 | 11385 |
| 2026-07-28 | 11385 |
| 2026-07-30 | 11385 |
| 2026-07-31 | 11385 |
| 2026-08-06 | 11387 |
Today
+2 stars today
This week
+2 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.02%/day
# 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: 2020/09/22* #### 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)](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
Excerpt of 61,547 characters
Read on GitHub
135
Jason Wu · Taiwan
2
1
Erjan K · Netherlands
1
Luiz Eduardo Amaral · @Bosch · Brazil
1
Google · Morocco
1
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a960f903564f7525, topic:object-detection, name:object detection, desc:object detection
matched fp:a960f903564f7525, topic:deep-learning