From 0adb3cbcb521fbaf6f4c930837d9cff6a76277cb Mon Sep 17 00:00:00 2001 From: wangxinxin08 <69842442+wangxinxin08@users.noreply.github.com> Date: Fri, 22 Oct 2021 11:00:51 +0800 Subject: [PATCH] modify doc of ppyolo, test=document_fix (#3749) --- configs/ppyolo/README.md | 11 ++++++++++- configs/ppyolo/README_cn.md | 11 ++++++++++- docs/images/ppyolo_map_fps.png | Bin 135040 -> 124234 bytes docs/tutorials/PrepareDataSet.md | 19 +++++++++++++++++++ 4 files changed, 39 insertions(+), 2 deletions(-) diff --git a/configs/ppyolo/README.md b/configs/ppyolo/README.md index eca5216cd..b567e3c92 100644 --- a/configs/ppyolo/README.md +++ b/configs/ppyolo/README.md @@ -19,7 +19,7 @@ PP-YOLO reached mmAP(IoU=0.5:0.95) as 45.9% on COCO test-dev2017 dataset, and in -PP-YOLO improved performance and speed of YOLOv3 with following methods: +PP-YOLO and PP-YOLOv2 improved performance and speed of YOLOv3 with following methods: - Better backbone: ResNet50vd-DCN - Larger training batch size: 8 GPUs and mini-batch size as 24 on each GPU @@ -31,6 +31,9 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods: - [CoordConv](https://arxiv.org/abs/1807.03247) - [Spatial Pyramid Pooling](https://arxiv.org/abs/1406.4729) - Better ImageNet pretrain weights +- [PAN](https://arxiv.org/abs/1803.01534) +- Iou aware Loss +- larger input size ## Model Zoo @@ -219,6 +222,12 @@ Optimizing method and ablation experiments of PP-YOLO compared with YOLOv3. ## Citation ``` +@article{huang2021pp, + title={PP-YOLOv2: A Practical Object Detector}, + author={Huang, Xin and Wang, Xinxin and Lv, Wenyu and Bai, Xiaying and Long, Xiang and Deng, Kaipeng and Dang, Qingqing and Han, Shumin and Liu, Qiwen and Hu, Xiaoguang and others}, + journal={arXiv preprint arXiv:2104.10419}, + year={2021} +} @misc{long2020ppyolo, title={PP-YOLO: An Effective and Efficient Implementation of Object Detector}, author={Xiang Long and Kaipeng Deng and Guanzhong Wang and Yang Zhang and Qingqing Dang and Yuan Gao and Hui Shen and Jianguo Ren and Shumin Han and Errui Ding and Shilei Wen}, diff --git a/configs/ppyolo/README_cn.md b/configs/ppyolo/README_cn.md index 9eafa8d19..43b241612 100644 --- a/configs/ppyolo/README_cn.md +++ b/configs/ppyolo/README_cn.md @@ -19,7 +19,7 @@ PP-YOLO在[COCO](http://cocodataset.org) test-dev2017数据集上精度达到45. -PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度: +PP-YOLO和PP-YOLOv2从如下方面优化和提升YOLOv3模型的精度和速度: - 更优的骨干网络: ResNet50vd-DCN - 更大的训练batch size: 8 GPUs,每GPU batch_size=24,对应调整学习率和迭代轮数 @@ -31,6 +31,9 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度: - [CoordConv](https://arxiv.org/abs/1807.03247) - [Spatial Pyramid Pooling](https://arxiv.org/abs/1406.4729) - 更优的预训练模型 +- [PAN](https://arxiv.org/abs/1803.01534) +- Iou aware Loss +- 更大的输入尺寸 ## 模型库 @@ -212,6 +215,12 @@ PP-YOLO模型相对于YOLOv3模型优化项消融实验数据如下表所示。 ## 引用 ``` +@article{huang2021pp, + title={PP-YOLOv2: A Practical Object Detector}, + author={Huang, Xin and Wang, Xinxin and Lv, Wenyu and Bai, Xiaying and Long, Xiang and Deng, Kaipeng and Dang, Qingqing and Han, Shumin and Liu, Qiwen and Hu, Xiaoguang and others}, + journal={arXiv preprint arXiv:2104.10419}, + year={2021} +} @misc{long2020ppyolo, title={PP-YOLO: An Effective and Efficient Implementation of Object Detector}, author={Xiang Long and Kaipeng Deng and Guanzhong Wang and Yang Zhang and Qingqing Dang and Yuan Gao and Hui Shen and Jianguo Ren and Shumin Han and Errui Ding and Shilei Wen}, diff --git a/docs/images/ppyolo_map_fps.png b/docs/images/ppyolo_map_fps.png index c66ad2fb490d661fa9a773aa382ea5911957994e..f860d220d1c831e42a23e38fc78732426c23e2cc 100644 GIT binary patch literal 124234 zcmdqJbzD?m-!_V(pnxEXgv0=&gyhgM3`i?Th=HUqQqqI;42Y6L42TlaB?i*nJ#;EV zcXxNZd-%nDKfn8&Kc45D^L{?(b$Qu&`aCloSwq2fwdy=_4iM_jf5^X83(>ZQ4upyH^Fi zew}(*e>x_niyASzo<+_@1VR&8x{rPx?#-S3eo_p*N=(2(IrA#hJ3IhE zEWzX;KWRk}Dj{T3%&jX_Gs5u$-A$w!@yKqGsw!@L`a$D$Hiu+Zq(&dnTN$IghHnHK zZCzqge&Oi8q)W+G;As+Xj=ALJnu)v;j-n(eyP%-uJ5RX*vUZvaGHHoz%BL#rzp8$# 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