From 73ec917344dbe5d396a67e05f144e59d3871041e Mon Sep 17 00:00:00 2001 From: wangguanzhong Date: Thu, 19 May 2022 14:21:07 +0800 Subject: [PATCH] refine paddlesmrt, test=document_fix (#6011) --- configs/fcos/_base_/fcos_r50_fpn.yml | 2 -- {ppsmrt => configs/smrt}/DataAnalysis.md | 0 {ppsmrt => configs/smrt}/README.md | 30 ++++++++++-------- configs/smrt/images/00362.jpg | Bin 0 -> 83702 bytes .../smrt}/images/Board_diaojiao_1591.png | Bin .../smrt}/images/UpCoa_liewen_163.png | Bin {ppsmrt => configs/smrt}/images/lvjian1_0.jpg | Bin .../smrt}/images/lvjian1_10.jpg | Bin .../smrt}/images/renche_00002.jpg | Bin .../smrt}/images/renche_00204.jpg | Bin .../picodet_l_1024_coco_lcnet_battery.yml | 0 .../picodet_l_1024_coco_lcnet_lvjian1.yml | 0 .../picodet_l_1024_coco_lcnet_renche.yml | 0 .../picodet_l_640_coco_lcnet_battery.yml | 0 .../picodet_l_640_coco_lcnet_lvjian1.yml | 0 .../picodet_l_640_coco_lcnet_renche.yml | 0 .../ppyolov2_r101vd_dcn_365e_battery.yml | 0 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a/configs/fcos/_base_/fcos_r50_fpn.yml +++ b/configs/fcos/_base_/fcos_r50_fpn.yml @@ -30,7 +30,6 @@ FCOSHead: num_convs: 4 norm_type: "gn" use_dcn: false - num_classes: 80 fpn_stride: [8, 16, 32, 64, 128] prior_prob: 0.01 fcos_loss: FCOSLoss @@ -46,7 +45,6 @@ FCOSLoss: FCOSPostProcess: decode: name: FCOSBox - num_classes: 80 nms: name: MultiClassNMS nms_top_k: 1000 diff --git a/ppsmrt/DataAnalysis.md b/configs/smrt/DataAnalysis.md similarity index 100% rename from ppsmrt/DataAnalysis.md rename to configs/smrt/DataAnalysis.md diff --git a/ppsmrt/README.md b/configs/smrt/README.md similarity index 70% rename from ppsmrt/README.md rename to configs/smrt/README.md index 1fd41c537..1eb62f152 100644 --- a/ppsmrt/README.md +++ b/configs/smrt/README.md @@ -1,16 +1,16 @@ -# 飞桨产业模型选型工具PP-SMRT +# 飞桨产业模型选型工具PaddleSMRT ## 一、项目介绍 -PP-SMRT (PaddlePaddle Sense Model Recommend Tool) 是飞桨结合产业落地经验推出的产业模型选型工具,在项目落地过程中,用户根据自身的实际情况,输入自己的需求,即可以得到对应在算法模型、部署硬件以及教程文档的信息。同时为了更加精准的推荐,增加了数据分析功能,用户上传自己的标注文件,系统可以自动分析数据特点,例如数据分布不均衡、小目标、密集型等,从而提供更加精准的模型以及优化策略,更好的符合场景的需求。 +PaddleSMRT (Paddle Sense Model Recommend Tool) 是飞桨结合产业落地经验推出的产业模型选型工具,在项目落地过程中,用户根据自身的实际情况,输入自己的需求,即可以得到对应在算法模型、部署硬件以及教程文档的信息。同时为了更加精准的推荐,增加了数据分析功能,用户上传自己的标注文件,系统可以自动分析数据特点,例如数据分布不均衡、小目标、密集型等,从而提供更加精准的模型以及优化策略,更好的符合场景的需求。 -PP-SMRT用户图 +飞桨官网使用[链接](https://www.paddlepaddle.org.cn/smrt) -本文档主要介绍PP-SMRT在检测方向上是如何进行模型选型推荐,以及推荐模型的使用方法。分割方向模型介绍请参考[文档](https://github.com/PaddlePaddle/PaddleSeg) +本文档主要介绍PaddleSMRT在检测方向上是如何进行模型选型推荐,以及推荐模型的使用方法。分割方向模型介绍请参考[文档](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.5/configs/smrt) ## 二、数据介绍 -PP-SMRT结合产业真实场景,通过比较检测算法效果,向用户推荐最适合的模型。目前PP-SMRT覆盖工业质检、城市安防两大场景,下面介绍PP-SMRT进行算法对比所使用的数据集 +PaddleSMRT结合产业真实场景,通过比较检测算法效果,向用户推荐最适合的模型。目前PaddleSMRT覆盖工业质检、城市安防两大场景,下面介绍PaddleSMRT进行算法对比所使用的数据集 ### 1. 新能源电池质检数据集 @@ -84,11 +84,15 @@ PP-SMRT结合产业真实场景,通过比较检测算法效果,向用户推 ``` -以上数据集特点通过数据分析工具实现,当前支持多种检测、分割数据标注格式,详细文档说明请参考[链接](DataAnalysis.md) +为了更好的帮助用户选择模型,我们也提供了丰富的数据分析功能,用户只需要上传标注文件(不需要原图)即可了解数据特点分布和模型优化建议 + +
+ +
## 三、推荐模型使用全流程 -通过模型选型工具会得到对应场景和数据特点的检测模型配置,例如[PP-YOLOE](./configs/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml) +通过模型选型工具会得到对应场景和数据特点的检测模型配置,例如[PP-YOLOE](./ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml) 该配置文件的使用方法如下 @@ -145,25 +149,25 @@ battery_mini 使用经过模型选型工具推荐的模型进行训练,目前所推荐的模型均使用**单卡训练**,可以在训练的过程中进行评估,模型默认保存在`./output`下 ```bash -python tools/train.py -c configs/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml --eval +python tools/train.py -c configs/smrt/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml --eval ``` 如果训练过程出现中断,可以使用-r命令恢复训练 ```bash -python tools/train.py -c configs/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml --eval -r output/ppyoloe_crn_m_300e_battery_1024/9.pdparams +python tools/train.py -c configs/smrt/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml --eval -r output/ppyoloe_crn_m_300e_battery_1024/9.pdparams ``` 如果期望单独评估模型训练精度,可以使用`tools/eval.py` ```bash -python tools/eval.py -c configs/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml -o weights=output/ppyoloe_crn_m_300e_battery_1024/model_final.pdparams +python tools/eval.py -c configs/smrt/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml -o weights=output/ppyoloe_crn_m_300e_battery_1024/model_final.pdparams ``` 完成训练后,可以使用`tools/infer.py`可视化训练效果 ```bash -python tools/infer.py -c configs/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml -o weights=output/ppyoloe_crn_m_300e_battery_1024/model_final.pdparams --infer_img=images/Board_diaojiao_1591.png +python tools/infer.py -c configs/smrt/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml -o weights=output/ppyoloe_crn_m_300e_battery_1024/model_final.pdparams --infer_img=images/Board_diaojiao_1591.png ``` 更多模型训练参数请参考[文档](../docs/tutorials/GETTING_STARTED_cn.md) @@ -175,7 +179,7 @@ python tools/infer.py -c configs/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml -o 首先需要将模型导出为部署时使用的模型和配置文件 ```bash -python tools/export_model.py -c configs/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml -o weights=output/ppyoloe_crn_m_300e_battery_1024/model_final.pdparams +python tools/export_model.py -c configs/smrt/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml -o weights=output/ppyoloe_crn_m_300e_battery_1024/model_final.pdparams ``` 接下来可以使用PaddleDetection中的部署代码实现C++部署,详细步骤请参考[文档](../deploy/cpp/README.md) @@ -200,7 +204,7 @@ python tools/export_model.py -c configs/ppyoloe/ppyoloe_crn_m_300e_battery_1024. ## 五、场景范例 -为了更方便大家更好的进行产业落地,PP-SMRT也提供了详细的应用范例,欢迎大家使用。 +为了更方便大家更好的进行产业落地,PaddleSMRT也提供了详细的应用范例,欢迎大家使用。 * 工业视觉 * [工业缺陷检测](https://aistudio.baidu.com/aistudio/projectdetail/2598319) diff --git a/configs/smrt/images/00362.jpg b/configs/smrt/images/00362.jpg new file mode 100644 index 0000000000000000000000000000000000000000..da4ab37d5cb5501e3c1471b30a3f465dd9b0a88f GIT binary patch literal 83702 zcmbSy1y~gC_wNGI2%>biw8SDEigb5(?SdekA|N8LbiT?IBf5`=i;GA8@DTwKH3cmVH3bzF9TWFsItETgDk@e% zHclR1Fc?hxSXf+$PmG%%%!ix=1sfY14;PQ@{(Uk&dMbLp|L4!2E)XFW>I6Cn4TS-O 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