更换文档检测模型
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paddle_detection/configs/smrt/DataAnalysis.md
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paddle_detection/configs/smrt/DataAnalysis.md
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# 数据分析功能说明
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为了更好的帮助用户进行数据分析，从推荐更合适的模型，我们推出了**数据分析**功能，用户不需要上传原图，只需要上传标注好的文件格式即可进一步分析数据特点。
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当前支持格式有：
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* LabelMe标注数据格式
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* 精灵标注数据格式
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* LabelImg标注数据格式
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* VOC数据格式
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* COCO数据格式
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* Seg数据格式
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## LabelMe标注数据格式
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1. 需要选定包含标注文件的zip格式压缩包。zip格式压缩包中包含一个annotations文件夹，文件夹中的内容为与标注图像相同数量的json文件，每一个json文件除后缀外与对应的图像同名。
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2. 支持检测与分割任务。若提供的标注信息与所选择的任务类型不匹配，则将提示错误。
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3. 对于检测任务，需提供rectangle类型标注信息；对于分割任务，需提供polygon类型标注信息。
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<div align="center">
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<img src="https://user-images.githubusercontent.com/48433081/169194724-c3fff1db-78b0-4013-925b-b99e5f51e5f2.png" width = "600" />
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</div>
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## 精灵标注数据格式
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1. 需要选定包含标注文件的zip格式压缩包。zip格式压缩包中包含一个annotations文件夹，文件夹中的内容为与标注图像相同数量的json文件，每一个json文件除后缀外与对应的图像同名。
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2. 支持检测与分割任务。若提供的标注信息与所选择的任务类型不匹配，则将提示错误。
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3. 对于检测任务，需提供bndbox或polygon类型标注信息；对于分割任务，需提供polygon类型标注信息。
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<div align="center">
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<img src="https://user-images.githubusercontent.com/48433081/169194724-c3fff1db-78b0-4013-925b-b99e5f51e5f2.png" width = "600" />
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</div>
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## LabelImg标注数据格式
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1. 需要选定包含标注文件的zip格式压缩包。zip格式压缩包中包含一个annotations文件夹，文件夹中的内容为与标注图像相同数量的xml文件，每一个xml文件除后缀外与对应的图像同名。
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2. 仅支持检测任务。
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3. 标注文件中必须提供bndbox字段信息；segmentation字段是可选的。
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<div align="center">
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<img src="https://user-images.githubusercontent.com/48433081/169195232-2ccd4c07-8203-44a5-9911-97c092a228d8.png" width = "600" />
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</div>
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## VOC数据格式
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1. 需要选定包含标注文件的zip格式压缩包。zip格式压缩包中包含一个annotations文件夹，文件夹中的内容为与标注图像相同数量的xml文件，每一个xml文件除后缀外与对应的图像同名。
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2. 仅支持检测任务。
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3. 标注文件中必须提供bndbox字段信息；segmentation字段是可选的。
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<div align="center">
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<img src="https://user-images.githubusercontent.com/48433081/169195232-2ccd4c07-8203-44a5-9911-97c092a228d8.png" width = "600" />
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</div>
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## COCO数据格式
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1. 需要选定包含标注文件的zip格式压缩包。zip格式压缩包中包含一个annotations文件夹，文件夹中仅存在一个名为annotation.json的文件。
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2. 支持检测与分割任务。若提供的标注信息与所选择的任务类型不匹配，则将提示错误。
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3. 对于检测任务，标注文件中必须包含bbox字段，segmentation字段是可选的；对于分割任务，标注文件中必须包含segmentation字段。
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<div align="center">
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<img src="https://user-images.githubusercontent.com/48433081/169195416-eb12f1bb-6d18-4354-bad5-c18961aa049d.png" width = "600" />
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</div>
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## Seg数据格式
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1. 需要选定包含标注文件的zip格式压缩包。zip格式压缩包中包含一个annotations文件夹，文件夹中的内容为与标注图像相同数量的png文件，每一个png文件除后缀外与对应的图像同名。
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2. 仅支持分割任务。
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3. 标注文件需要与原始图像在像素上严格保持一一对应，格式只可为png（后缀为.png或.PNG）。标注文件中的每个像素值为[0,255]区间内从0开始依序递增的整数ID，除255外，标注ID值的增加不能跳跃。在标注文件中，使用255表示需要忽略的像素，使用0表示背景类标注。
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<div align="center">
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<img src="https://user-images.githubusercontent.com/48433081/169195389-85a9bda2-282b-452f-a809-d0100291f86f.png" width = "600" />
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</div>
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216
paddle_detection/configs/smrt/README.md
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# 飞桨产业模型选型工具PaddleSMRT
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## 一、项目介绍
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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。
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飞桨官网使用[链接](https://www.paddlepaddle.org.cn/smrt)
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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)
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## 二、数据介绍
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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
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### 1. 新能源电池质检数据集
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数据集为新能源电池电池组件质检数据集，包含15021张图片，包含22045个标注框，覆盖45种缺陷类型，例如掉胶，裂纹，划痕等。
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新能源电池数据展示图:
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<div align="center">
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<img src="images/Board_diaojiao_1591.png" width = "200" />
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<img src="images/UpCoa_liewen_163.png" width = "200" />
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</div>
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数据集特点为：
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1. 类别分布均衡
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2. 属于小目标数据
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3. 非密集型数据
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### 2. 铝件质检数据集
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数据集为铝件生产过程中的质检数据集，包含11293张图片，包含43157个标注框，覆盖5种缺陷类型，例如划伤，压伤，起皮等。
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铝件质检数据展示图:
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<div align="center">
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<img src="images/lvjian1_0.jpg" width = "200" />
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<img src="images/lvjian1_10.jpg" width = "200" />
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</div>
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数据集特点为：
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1. 类别分布不均衡
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2. 属于小目标数据
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3. 非密集型数据
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### 3. 人车数据集
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数据集包含2600张人工标注的两点anchor box标签。标签包括以下人和车的类别共22种：
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其中行人包括普通行人、3D 假人、坐着的人、骑车的人；车辆包括两厢车、三厢车、小型客车、小货车、皮卡车、轻卡、厢式货车、牵引车、水泥车、工程车辆、校车、中小型客车、大型单层客车、小型电动车、摩托车、自行车、三轮车以及其它特殊车辆。
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人车数据展示图:
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<div align="center">
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<img src="images/renche_00002.jpg" width = "200" />
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<img src="images/renche_00204.jpg" width = "200" />
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</div>
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数据集特点为：
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1. 类别分布不均衡
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2. 属于小目标数据
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3. 非密集型数据
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**说明：**
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数据集特点判断依据如下：
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- 数据分布不均衡：采样1000张图片，不同类别样本个数标准差大于400
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- 小目标数据集：相对大小小于0.1或绝对大小小于32像素的样本个数比例大于30%
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- 密集型数据集：
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```
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密集目标定义：周围目标距离小于自身大小两倍的个数大于2；
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密集图片定义：密集目标个数占图片目标总数50%以上；
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密集数据集定义：密集图片个数占总个数30%以上
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```
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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
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<div align="center">
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<img src="https://user-images.githubusercontent.com/48433081/169062411-7940d1cb-2742-4d7d-8679-73a00c07247e.png" width = "800" />
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</div>
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## 三、推荐模型使用全流程
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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)
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该配置文件的使用方法如下
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### 1. 环境配置
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首先需要安装PaddlePaddle
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```bash
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# CUDA10.2
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pip install paddlepaddle-gpu==2.2.2 -i https://mirror.baidu.com/pypi/simple
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# CPU
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pip install paddlepaddle==2.2.2 -i https://mirror.baidu.com/pypi/simple
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```
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然后安装PaddleDetection和相关依赖
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```bash
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# 克隆PaddleDetection仓库
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cd <path/to/clone/PaddleDetection>
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git clone https://github.com/PaddlePaddle/PaddleDetection.git
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# 安装其他依赖
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cd PaddleDetection
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pip install -r requirements.txt
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```
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详细安装文档请参考[文档](../../docs/tutorials/INSTALL_cn.md)
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### 2. 数据准备
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用户需要准备训练数据集，建议标注文件使用COCO数据格式。如果使用lableme或者VOC数据格式，先使用[格式转换脚本](../../tools/x2coco.py)将标注格式转化为COCO，详细数据准备文档请参考[文档](../../docs/tutorials/data/PrepareDataSet.md)
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本文档以新能源电池工业质检子数据集为例展开，数据下载[链接](https://bj.bcebos.com/v1/paddle-smrt/data/battery_mini.zip)
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数据储存格式如下：
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```
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battery_mini
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├── annotations
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│ ├── test.json
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│ └── train.json
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└── images
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├── Board_daowen_101.png
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├── Board_daowen_109.png
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├── Board_daowen_117.png
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...
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```
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### 3. 模型训练/评估/预测
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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
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```bash
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python tools/train.py -c configs/smrt/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml --eval
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```
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如果训练过程出现中断，可以使用-r命令恢复训练
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```bash
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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
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```
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如果期望单独评估模型训练精度，可以使用`tools/eval.py`
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```bash
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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
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```
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完成训练后，可以使用`tools/infer.py`可视化训练效果
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```bash
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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
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```
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更多模型训练参数请参考[文档](../../docs/tutorials/GETTING_STARTED_cn.md)
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### 4. 模型导出部署
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完成模型训练后，需要将模型部署到1080Ti，2080Ti或其他服务器设备上，使用Paddle Inference完成C++部署
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首先需要将模型导出为部署时使用的模型和配置文件
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```bash
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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
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```
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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)
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如果期望使用可视化界面的方式进行部署，可以参考下面部分的内容。
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## 四、部署demo
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为了更方便大家部署，我们也提供了完备的可视化部署Demo，欢迎尝试使用
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* [Windows Demo下载地址](https://github.com/PaddlePaddle/PaddleX/tree/develop/deploy/cpp/docs/csharp_deploy)
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<div align="center">
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<img src="https://user-images.githubusercontent.com/48433081/169064583-c931f4c0-dfd6-4bfa-85f1-be68eb351e4a.png" width = "800" />
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</div>
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* [Linux Demo下载地址](https://github.com/cjh3020889729/The-PaddleX-QT-Visualize-GUI)
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<div align="center">
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<img src="https://user-images.githubusercontent.com/48433081/169065951-147f8d51-bf3e-4a28-9197-d717968de73f.png" width = "800" />
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</div>
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## 五、场景范例
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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。
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* 工业视觉
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* [工业缺陷检测](https://aistudio.baidu.com/aistudio/projectdetail/2598319)
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* [表计读数](https://aistudio.baidu.com/aistudio/projectdetail/2598327)
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* [钢筋计数](https://aistudio.baidu.com/aistudio/projectdetail/2404188)
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* 城市
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* [行人计数](https://aistudio.baidu.com/aistudio/projectdetail/2421822)
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* [车辆计数](https://aistudio.baidu.com/aistudio/projectdetail/3391734?contributionType=1)
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* [安全帽检测](https://aistudio.baidu.com/aistudio/projectdetail/3944737?contributionType=1)
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|
||||
weights: output/picodet_l_1024_coco_lcnet_battery/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/picodet_l_640_coco_lcnet.pdparams
|
||||
|
||||
worker_num: 2
|
||||
eval_height: &eval_height 1024
|
||||
eval_width: &eval_width 1024
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
epoch: 50
|
||||
LearningRate:
|
||||
base_lr: 0.006
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 50
|
||||
- !LinearWarmup
|
||||
start_factor: 0.001
|
||||
steps: 300
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomCrop: {}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- RandomDistort: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 8
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 8
|
||||
shuffle: false
|
||||
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [1, 3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 10
|
||||
print_flops: false
|
||||
find_unused_parameters: True
|
||||
use_ema: true
|
||||
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.00004
|
||||
type: L2
|
||||
|
||||
architecture: PicoDet
|
||||
|
||||
PicoDet:
|
||||
backbone: LCNet
|
||||
neck: LCPAN
|
||||
head: PicoHeadV2
|
||||
|
||||
LCNet:
|
||||
scale: 2.0
|
||||
feature_maps: [3, 4, 5]
|
||||
|
||||
LCPAN:
|
||||
out_channels: 160
|
||||
use_depthwise: True
|
||||
num_features: 4
|
||||
|
||||
PicoHeadV2:
|
||||
conv_feat:
|
||||
name: PicoFeat
|
||||
feat_in: 160
|
||||
feat_out: 160
|
||||
num_convs: 4
|
||||
num_fpn_stride: 4
|
||||
norm_type: bn
|
||||
share_cls_reg: True
|
||||
use_se: True
|
||||
fpn_stride: [8, 16, 32, 64]
|
||||
feat_in_chan: 160
|
||||
prior_prob: 0.01
|
||||
reg_max: 7
|
||||
cell_offset: 0.5
|
||||
grid_cell_scale: 5.0
|
||||
static_assigner_epoch: 100
|
||||
use_align_head: True
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
force_gt_matching: False
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
loss_class:
|
||||
name: VarifocalLoss
|
||||
use_sigmoid: False
|
||||
iou_weighted: True
|
||||
loss_weight: 1.0
|
||||
loss_dfl:
|
||||
name: DistributionFocalLoss
|
||||
loss_weight: 0.5
|
||||
loss_bbox:
|
||||
name: GIoULoss
|
||||
loss_weight: 2.5
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.025
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,162 @@
|
||||
weights: output/picodet_l_1024_coco_lcnet_lvjian1/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/picodet_l_640_coco_lcnet.pdparams
|
||||
|
||||
worker_num: 2
|
||||
eval_height: &eval_height 1024
|
||||
eval_width: &eval_width 1024
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
epoch: 50
|
||||
LearningRate:
|
||||
base_lr: 0.006
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 50
|
||||
- !LinearWarmup
|
||||
start_factor: 0.001
|
||||
steps: 300
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomCrop: {}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- RandomDistort: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 8
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 8
|
||||
shuffle: false
|
||||
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [1, 3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 10
|
||||
print_flops: false
|
||||
find_unused_parameters: True
|
||||
use_ema: true
|
||||
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.00004
|
||||
type: L2
|
||||
|
||||
architecture: PicoDet
|
||||
|
||||
PicoDet:
|
||||
backbone: LCNet
|
||||
neck: LCPAN
|
||||
head: PicoHeadV2
|
||||
|
||||
LCNet:
|
||||
scale: 2.0
|
||||
feature_maps: [3, 4, 5]
|
||||
|
||||
LCPAN:
|
||||
out_channels: 160
|
||||
use_depthwise: True
|
||||
num_features: 4
|
||||
|
||||
PicoHeadV2:
|
||||
conv_feat:
|
||||
name: PicoFeat
|
||||
feat_in: 160
|
||||
feat_out: 160
|
||||
num_convs: 4
|
||||
num_fpn_stride: 4
|
||||
norm_type: bn
|
||||
share_cls_reg: True
|
||||
use_se: True
|
||||
fpn_stride: [8, 16, 32, 64]
|
||||
feat_in_chan: 160
|
||||
prior_prob: 0.01
|
||||
reg_max: 7
|
||||
cell_offset: 0.5
|
||||
grid_cell_scale: 5.0
|
||||
static_assigner_epoch: 100
|
||||
use_align_head: True
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
force_gt_matching: False
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
loss_class:
|
||||
name: VarifocalLoss
|
||||
use_sigmoid: False
|
||||
iou_weighted: True
|
||||
loss_weight: 1.0
|
||||
loss_dfl:
|
||||
name: DistributionFocalLoss
|
||||
loss_weight: 0.5
|
||||
loss_bbox:
|
||||
name: GIoULoss
|
||||
loss_weight: 2.5
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.025
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,162 @@
|
||||
weights: output/picodet_l_1024_coco_lcnet_renche/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/picodet_l_640_coco_lcnet.pdparams
|
||||
|
||||
worker_num: 2
|
||||
eval_height: &eval_height 1024
|
||||
eval_width: &eval_width 1024
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
epoch: 50
|
||||
LearningRate:
|
||||
base_lr: 0.006
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 50
|
||||
- !LinearWarmup
|
||||
start_factor: 0.001
|
||||
steps: 300
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomCrop: {}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- RandomDistort: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 8
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 8
|
||||
shuffle: false
|
||||
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [1, 3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 10
|
||||
print_flops: false
|
||||
find_unused_parameters: True
|
||||
use_ema: true
|
||||
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.00004
|
||||
type: L2
|
||||
|
||||
architecture: PicoDet
|
||||
|
||||
PicoDet:
|
||||
backbone: LCNet
|
||||
neck: LCPAN
|
||||
head: PicoHeadV2
|
||||
|
||||
LCNet:
|
||||
scale: 2.0
|
||||
feature_maps: [3, 4, 5]
|
||||
|
||||
LCPAN:
|
||||
out_channels: 160
|
||||
use_depthwise: True
|
||||
num_features: 4
|
||||
|
||||
PicoHeadV2:
|
||||
conv_feat:
|
||||
name: PicoFeat
|
||||
feat_in: 160
|
||||
feat_out: 160
|
||||
num_convs: 4
|
||||
num_fpn_stride: 4
|
||||
norm_type: bn
|
||||
share_cls_reg: True
|
||||
use_se: True
|
||||
fpn_stride: [8, 16, 32, 64]
|
||||
feat_in_chan: 160
|
||||
prior_prob: 0.01
|
||||
reg_max: 7
|
||||
cell_offset: 0.5
|
||||
grid_cell_scale: 5.0
|
||||
static_assigner_epoch: 100
|
||||
use_align_head: True
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
force_gt_matching: False
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
loss_class:
|
||||
name: VarifocalLoss
|
||||
use_sigmoid: False
|
||||
iou_weighted: True
|
||||
loss_weight: 1.0
|
||||
loss_dfl:
|
||||
name: DistributionFocalLoss
|
||||
loss_weight: 0.5
|
||||
loss_bbox:
|
||||
name: GIoULoss
|
||||
loss_weight: 2.5
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.025
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,162 @@
|
||||
weights: output/picodet_l_640_coco_lcnet_battery/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/picodet_l_640_coco_lcnet.pdparams
|
||||
|
||||
worker_num: 2
|
||||
eval_height: &eval_height 640
|
||||
eval_width: &eval_width 640
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
epoch: 50
|
||||
LearningRate:
|
||||
base_lr: 0.006
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 50
|
||||
- !LinearWarmup
|
||||
start_factor: 0.001
|
||||
steps: 300
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomCrop: {}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- RandomDistort: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [576, 608, 640, 672, 704], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 8
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 8
|
||||
shuffle: false
|
||||
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [1, 3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 10
|
||||
print_flops: false
|
||||
find_unused_parameters: True
|
||||
use_ema: true
|
||||
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.00004
|
||||
type: L2
|
||||
|
||||
architecture: PicoDet
|
||||
|
||||
PicoDet:
|
||||
backbone: LCNet
|
||||
neck: LCPAN
|
||||
head: PicoHeadV2
|
||||
|
||||
LCNet:
|
||||
scale: 2.0
|
||||
feature_maps: [3, 4, 5]
|
||||
|
||||
LCPAN:
|
||||
out_channels: 160
|
||||
use_depthwise: True
|
||||
num_features: 4
|
||||
|
||||
PicoHeadV2:
|
||||
conv_feat:
|
||||
name: PicoFeat
|
||||
feat_in: 160
|
||||
feat_out: 160
|
||||
num_convs: 4
|
||||
num_fpn_stride: 4
|
||||
norm_type: bn
|
||||
share_cls_reg: True
|
||||
use_se: True
|
||||
fpn_stride: [8, 16, 32, 64]
|
||||
feat_in_chan: 160
|
||||
prior_prob: 0.01
|
||||
reg_max: 7
|
||||
cell_offset: 0.5
|
||||
grid_cell_scale: 5.0
|
||||
static_assigner_epoch: 100
|
||||
use_align_head: True
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
force_gt_matching: False
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
loss_class:
|
||||
name: VarifocalLoss
|
||||
use_sigmoid: False
|
||||
iou_weighted: True
|
||||
loss_weight: 1.0
|
||||
loss_dfl:
|
||||
name: DistributionFocalLoss
|
||||
loss_weight: 0.5
|
||||
loss_bbox:
|
||||
name: GIoULoss
|
||||
loss_weight: 2.5
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.025
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,162 @@
|
||||
weights: output/picodet_l_640_coco_lcnet_lvjian1/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/picodet_l_640_coco_lcnet.pdparams
|
||||
|
||||
worker_num: 2
|
||||
eval_height: &eval_height 640
|
||||
eval_width: &eval_width 640
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: /paddle/dataset/model-select/gongye/lvjian1/slice_lvjian1_data/train/
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: /paddle/dataset/model-select/gongye/lvjian1/slice_lvjian1_data/eval/
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
epoch: 50
|
||||
LearningRate:
|
||||
base_lr: 0.006
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 50
|
||||
- !LinearWarmup
|
||||
start_factor: 0.001
|
||||
steps: 300
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomCrop: {}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- RandomDistort: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [576, 608, 640, 672, 704], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 8
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 8
|
||||
shuffle: false
|
||||
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [1, 3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 10
|
||||
print_flops: false
|
||||
find_unused_parameters: True
|
||||
use_ema: true
|
||||
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.00004
|
||||
type: L2
|
||||
|
||||
architecture: PicoDet
|
||||
|
||||
PicoDet:
|
||||
backbone: LCNet
|
||||
neck: LCPAN
|
||||
head: PicoHeadV2
|
||||
|
||||
LCNet:
|
||||
scale: 2.0
|
||||
feature_maps: [3, 4, 5]
|
||||
|
||||
LCPAN:
|
||||
out_channels: 160
|
||||
use_depthwise: True
|
||||
num_features: 4
|
||||
|
||||
PicoHeadV2:
|
||||
conv_feat:
|
||||
name: PicoFeat
|
||||
feat_in: 160
|
||||
feat_out: 160
|
||||
num_convs: 4
|
||||
num_fpn_stride: 4
|
||||
norm_type: bn
|
||||
share_cls_reg: True
|
||||
use_se: True
|
||||
fpn_stride: [8, 16, 32, 64]
|
||||
feat_in_chan: 160
|
||||
prior_prob: 0.01
|
||||
reg_max: 7
|
||||
cell_offset: 0.5
|
||||
grid_cell_scale: 5.0
|
||||
static_assigner_epoch: 100
|
||||
use_align_head: True
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
force_gt_matching: False
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
loss_class:
|
||||
name: VarifocalLoss
|
||||
use_sigmoid: False
|
||||
iou_weighted: True
|
||||
loss_weight: 1.0
|
||||
loss_dfl:
|
||||
name: DistributionFocalLoss
|
||||
loss_weight: 0.5
|
||||
loss_bbox:
|
||||
name: GIoULoss
|
||||
loss_weight: 2.5
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.025
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,162 @@
|
||||
weights: output/picodet_l_640_coco_lcnet_renche/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/picodet_l_640_coco_lcnet.pdparams
|
||||
|
||||
worker_num: 2
|
||||
eval_height: &eval_height 640
|
||||
eval_width: &eval_width 640
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
epoch: 50
|
||||
LearningRate:
|
||||
base_lr: 0.006
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 50
|
||||
- !LinearWarmup
|
||||
start_factor: 0.001
|
||||
steps: 300
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomCrop: {}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- RandomDistort: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [576, 608, 640, 672, 704], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 8
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 8
|
||||
shuffle: false
|
||||
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [1, 3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 10
|
||||
print_flops: false
|
||||
find_unused_parameters: True
|
||||
use_ema: true
|
||||
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.00004
|
||||
type: L2
|
||||
|
||||
architecture: PicoDet
|
||||
|
||||
PicoDet:
|
||||
backbone: LCNet
|
||||
neck: LCPAN
|
||||
head: PicoHeadV2
|
||||
|
||||
LCNet:
|
||||
scale: 2.0
|
||||
feature_maps: [3, 4, 5]
|
||||
|
||||
LCPAN:
|
||||
out_channels: 160
|
||||
use_depthwise: True
|
||||
num_features: 4
|
||||
|
||||
PicoHeadV2:
|
||||
conv_feat:
|
||||
name: PicoFeat
|
||||
feat_in: 160
|
||||
feat_out: 160
|
||||
num_convs: 4
|
||||
num_fpn_stride: 4
|
||||
norm_type: bn
|
||||
share_cls_reg: True
|
||||
use_se: True
|
||||
fpn_stride: [8, 16, 32, 64]
|
||||
feat_in_chan: 160
|
||||
prior_prob: 0.01
|
||||
reg_max: 7
|
||||
cell_offset: 0.5
|
||||
grid_cell_scale: 5.0
|
||||
static_assigner_epoch: 100
|
||||
use_align_head: True
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
force_gt_matching: False
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
loss_class:
|
||||
name: VarifocalLoss
|
||||
use_sigmoid: False
|
||||
iou_weighted: True
|
||||
loss_weight: 1.0
|
||||
loss_dfl:
|
||||
name: DistributionFocalLoss
|
||||
loss_weight: 0.5
|
||||
loss_bbox:
|
||||
name: GIoULoss
|
||||
loss_weight: 2.5
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.025
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,154 @@
|
||||
architecture: YOLOv3
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolov2_r101vd_dcn_365e_coco.pdparams
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json # also support txt (like VOC's label_list.txt)
|
||||
dataset_dir: dataset/battery_mini # if set, anno_path will be 'dataset_dir/anno_path'
|
||||
|
||||
|
||||
epoch: 40
|
||||
LearningRate:
|
||||
base_lr: 0.0001
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones:
|
||||
- 80
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
steps: 1000
|
||||
|
||||
snapshot_epoch: 5
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
inputs_def:
|
||||
num_max_boxes: 100
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomDistort: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [576, 608, 640, 672, 704], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeBox: {}
|
||||
- PadBox: {num_max_boxes: 100}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- Gt2YoloTarget: {anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]], anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]], downsample_ratios: [32, 16, 8]}
|
||||
batch_size: 8
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 8
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, 640, 640]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
clip_grad_by_norm: 35.
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
|
||||
YOLOv3:
|
||||
backbone: ResNet
|
||||
neck: PPYOLOPAN
|
||||
yolo_head: YOLOv3Head
|
||||
post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 101
|
||||
variant: d
|
||||
return_idx: [1, 2, 3]
|
||||
dcn_v2_stages: [3]
|
||||
freeze_at: -1
|
||||
freeze_norm: false
|
||||
norm_decay: 0.
|
||||
|
||||
PPYOLOPAN:
|
||||
drop_block: true
|
||||
block_size: 3
|
||||
keep_prob: 0.9
|
||||
spp: true
|
||||
|
||||
YOLOv3Head:
|
||||
anchors: [[10, 13], [16, 30], [33, 23],
|
||||
[30, 61], [62, 45], [59, 119],
|
||||
[116, 90], [156, 198], [373, 326]]
|
||||
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
|
||||
loss: YOLOv3Loss
|
||||
iou_aware: true
|
||||
iou_aware_factor: 0.4
|
||||
|
||||
YOLOv3Loss:
|
||||
ignore_thresh: 0.7
|
||||
downsample: [32, 16, 8]
|
||||
label_smooth: false
|
||||
scale_x_y: 1.05
|
||||
iou_loss: IouLoss
|
||||
iou_aware_loss: IouAwareLoss
|
||||
|
||||
IouLoss:
|
||||
loss_weight: 2.5
|
||||
loss_square: true
|
||||
|
||||
IouAwareLoss:
|
||||
loss_weight: 1.0
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: YOLOBox
|
||||
conf_thresh: 0.01
|
||||
downsample_ratio: 32
|
||||
clip_bbox: true
|
||||
scale_x_y: 1.05
|
||||
nms:
|
||||
name: MatrixNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
post_threshold: 0.01
|
||||
nms_top_k: -1
|
||||
background_label: -1
|
||||
@@ -0,0 +1,154 @@
|
||||
architecture: YOLOv3
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolov2_r101vd_dcn_365e_coco.pdparams
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json # also support txt (like VOC's label_list.txt)
|
||||
dataset_dir: dataset/battery_mini # if set, anno_path will be 'dataset_dir/anno_path'
|
||||
|
||||
|
||||
epoch: 40
|
||||
LearningRate:
|
||||
base_lr: 0.0001
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones:
|
||||
- 80
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
steps: 1000
|
||||
|
||||
snapshot_epoch: 5
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
inputs_def:
|
||||
num_max_boxes: 100
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomDistort: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeBox: {}
|
||||
- PadBox: {num_max_boxes: 100}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- Gt2YoloTarget: {anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]], anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]], downsample_ratios: [32, 16, 8]}
|
||||
batch_size: 2
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [1024, 1024], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 8
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, 1024, 1024]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [1024, 1024], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
clip_grad_by_norm: 35.
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
|
||||
YOLOv3:
|
||||
backbone: ResNet
|
||||
neck: PPYOLOPAN
|
||||
yolo_head: YOLOv3Head
|
||||
post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 101
|
||||
variant: d
|
||||
return_idx: [1, 2, 3]
|
||||
dcn_v2_stages: [3]
|
||||
freeze_at: -1
|
||||
freeze_norm: false
|
||||
norm_decay: 0.
|
||||
|
||||
PPYOLOPAN:
|
||||
drop_block: true
|
||||
block_size: 3
|
||||
keep_prob: 0.9
|
||||
spp: true
|
||||
|
||||
YOLOv3Head:
|
||||
anchors: [[10, 13], [16, 30], [33, 23],
|
||||
[30, 61], [62, 45], [59, 119],
|
||||
[116, 90], [156, 198], [373, 326]]
|
||||
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
|
||||
loss: YOLOv3Loss
|
||||
iou_aware: true
|
||||
iou_aware_factor: 0.4
|
||||
|
||||
YOLOv3Loss:
|
||||
ignore_thresh: 0.7
|
||||
downsample: [32, 16, 8]
|
||||
label_smooth: false
|
||||
scale_x_y: 1.05
|
||||
iou_loss: IouLoss
|
||||
iou_aware_loss: IouAwareLoss
|
||||
|
||||
IouLoss:
|
||||
loss_weight: 2.5
|
||||
loss_square: true
|
||||
|
||||
IouAwareLoss:
|
||||
loss_weight: 1.0
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: YOLOBox
|
||||
conf_thresh: 0.01
|
||||
downsample_ratio: 32
|
||||
clip_bbox: true
|
||||
scale_x_y: 1.05
|
||||
nms:
|
||||
name: MatrixNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
post_threshold: 0.01
|
||||
nms_top_k: -1
|
||||
background_label: -1
|
||||
@@ -0,0 +1,155 @@
|
||||
architecture: YOLOv3
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolov2_r101vd_dcn_365e_coco.pdparams
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
|
||||
epoch: 20
|
||||
LearningRate:
|
||||
base_lr: 0.0002
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones:
|
||||
- 80
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
steps: 1000
|
||||
|
||||
|
||||
snapshot_epoch: 3
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
inputs_def:
|
||||
num_max_boxes: 100
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomDistort: {}
|
||||
- RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
|
||||
- RandomCrop: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeBox: {}
|
||||
- PadBox: {num_max_boxes: 100}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- Gt2YoloTarget: {anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]], anchors: [[8, 7], [24, 12], [14, 25], [37, 35], [30, 140], [89, 52], [93, 189], [226, 99], [264, 352]], downsample_ratios: [32, 16, 8]}
|
||||
batch_size: 2
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [1024, 1024], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 8
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, 1024, 1024]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [1024, 1024], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
OptimizerBuilder:
|
||||
clip_grad_by_norm: 35.
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
YOLOv3:
|
||||
backbone: ResNet
|
||||
neck: PPYOLOPAN
|
||||
yolo_head: YOLOv3Head
|
||||
post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 101
|
||||
variant: d
|
||||
return_idx: [1, 2, 3]
|
||||
dcn_v2_stages: [3]
|
||||
freeze_at: -1
|
||||
freeze_norm: false
|
||||
norm_decay: 0.
|
||||
|
||||
PPYOLOPAN:
|
||||
drop_block: true
|
||||
block_size: 3
|
||||
keep_prob: 0.9
|
||||
spp: true
|
||||
|
||||
YOLOv3Head:
|
||||
anchors: [[8, 7], [24, 12], [14, 25],
|
||||
[37, 35], [30, 140], [89, 52],
|
||||
[93, 189], [226, 99], [264, 352]]
|
||||
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
|
||||
loss: YOLOv3Loss
|
||||
iou_aware: true
|
||||
iou_aware_factor: 0.5
|
||||
|
||||
YOLOv3Loss:
|
||||
ignore_thresh: 0.7
|
||||
downsample: [32, 16, 8]
|
||||
label_smooth: false
|
||||
scale_x_y: 1.05
|
||||
iou_loss: IouLoss
|
||||
iou_aware_loss: IouAwareLoss
|
||||
|
||||
IouLoss:
|
||||
loss_weight: 2.5
|
||||
loss_square: true
|
||||
|
||||
IouAwareLoss:
|
||||
loss_weight: 1.0
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: YOLOBox
|
||||
conf_thresh: 0.01
|
||||
downsample_ratio: 32
|
||||
clip_bbox: true
|
||||
scale_x_y: 1.05
|
||||
nms:
|
||||
name: MatrixNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
post_threshold: 0.01
|
||||
nms_top_k: -1
|
||||
background_label: -1
|
||||
@@ -0,0 +1,155 @@
|
||||
architecture: YOLOv3
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolov2_r101vd_dcn_365e_coco.pdparams
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
|
||||
epoch: 20
|
||||
LearningRate:
|
||||
base_lr: 0.0002
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones:
|
||||
- 80
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
steps: 1000
|
||||
|
||||
|
||||
snapshot_epoch: 3
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
inputs_def:
|
||||
num_max_boxes: 100
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomDistort: {}
|
||||
- RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
|
||||
- RandomCrop: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [576, 608, 640, 672, 704], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeBox: {}
|
||||
- PadBox: {num_max_boxes: 100}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- Gt2YoloTarget: {anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]], anchors: [[8, 7], [24, 12], [14, 25], [37, 35], [30, 140], [89, 52], [93, 189], [226, 99], [264, 352]], downsample_ratios: [32, 16, 8]}
|
||||
batch_size: 2
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 8
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, 640, 640]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
OptimizerBuilder:
|
||||
clip_grad_by_norm: 35.
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
YOLOv3:
|
||||
backbone: ResNet
|
||||
neck: PPYOLOPAN
|
||||
yolo_head: YOLOv3Head
|
||||
post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 101
|
||||
variant: d
|
||||
return_idx: [1, 2, 3]
|
||||
dcn_v2_stages: [3]
|
||||
freeze_at: -1
|
||||
freeze_norm: false
|
||||
norm_decay: 0.
|
||||
|
||||
PPYOLOPAN:
|
||||
drop_block: true
|
||||
block_size: 3
|
||||
keep_prob: 0.9
|
||||
spp: true
|
||||
|
||||
YOLOv3Head:
|
||||
anchors: [[8, 7], [24, 12], [14, 25],
|
||||
[37, 35], [30, 140], [89, 52],
|
||||
[93, 189], [226, 99], [264, 352]]
|
||||
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
|
||||
loss: YOLOv3Loss
|
||||
iou_aware: true
|
||||
iou_aware_factor: 0.5
|
||||
|
||||
YOLOv3Loss:
|
||||
ignore_thresh: 0.7
|
||||
downsample: [32, 16, 8]
|
||||
label_smooth: false
|
||||
scale_x_y: 1.05
|
||||
iou_loss: IouLoss
|
||||
iou_aware_loss: IouAwareLoss
|
||||
|
||||
IouLoss:
|
||||
loss_weight: 2.5
|
||||
loss_square: true
|
||||
|
||||
IouAwareLoss:
|
||||
loss_weight: 1.0
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: YOLOBox
|
||||
conf_thresh: 0.01
|
||||
downsample_ratio: 32
|
||||
clip_bbox: true
|
||||
scale_x_y: 1.05
|
||||
nms:
|
||||
name: MatrixNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
post_threshold: 0.01
|
||||
nms_top_k: -1
|
||||
background_label: -1
|
||||
@@ -0,0 +1,156 @@
|
||||
architecture: YOLOv3
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolov2_r101vd_dcn_365e_coco.pdparams
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: dataset/renche/test.json
|
||||
|
||||
|
||||
epoch: 100
|
||||
LearningRate:
|
||||
base_lr: 0.0002
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones:
|
||||
- 80
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
steps: 1000
|
||||
|
||||
|
||||
snapshot_epoch: 3
|
||||
worker_num: 8
|
||||
TrainReader:
|
||||
inputs_def:
|
||||
num_max_boxes: 100
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomDistort: {}
|
||||
- RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
|
||||
- RandomCrop: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeBox: {}
|
||||
- PadBox: {num_max_boxes: 100}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- Gt2YoloTarget: {anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]], anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]], downsample_ratios: [32, 16, 8]}
|
||||
batch_size: 2
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [1024, 1024], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 8
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, 1024, 1024]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [1024, 1024], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
clip_grad_by_norm: 35.
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
|
||||
YOLOv3:
|
||||
backbone: ResNet
|
||||
neck: PPYOLOPAN
|
||||
yolo_head: YOLOv3Head
|
||||
post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 101
|
||||
variant: d
|
||||
return_idx: [1, 2, 3]
|
||||
dcn_v2_stages: [3]
|
||||
freeze_at: -1
|
||||
freeze_norm: false
|
||||
norm_decay: 0.
|
||||
|
||||
PPYOLOPAN:
|
||||
drop_block: true
|
||||
block_size: 3
|
||||
keep_prob: 0.9
|
||||
spp: true
|
||||
|
||||
YOLOv3Head:
|
||||
anchors: [[10, 13], [16, 30], [33, 23],
|
||||
[30, 61], [62, 45], [59, 119],
|
||||
[116, 90], [156, 198], [373, 326]]
|
||||
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
|
||||
loss: YOLOv3Loss
|
||||
iou_aware: true
|
||||
iou_aware_factor: 0.5
|
||||
|
||||
YOLOv3Loss:
|
||||
ignore_thresh: 0.7
|
||||
downsample: [32, 16, 8]
|
||||
label_smooth: false
|
||||
scale_x_y: 1.05
|
||||
iou_loss: IouLoss
|
||||
iou_aware_loss: IouAwareLoss
|
||||
|
||||
IouLoss:
|
||||
loss_weight: 2.5
|
||||
loss_square: true
|
||||
|
||||
IouAwareLoss:
|
||||
loss_weight: 1.0
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: YOLOBox
|
||||
conf_thresh: 0.01
|
||||
downsample_ratio: 32
|
||||
clip_bbox: true
|
||||
scale_x_y: 1.05
|
||||
nms:
|
||||
name: MatrixNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
post_threshold: 0.01
|
||||
nms_top_k: -1
|
||||
background_label: -1
|
||||
@@ -0,0 +1,156 @@
|
||||
architecture: YOLOv3
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolov2_r101vd_dcn_365e_coco.pdparams
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: dataset/renche/test.json
|
||||
|
||||
|
||||
epoch: 100
|
||||
LearningRate:
|
||||
base_lr: 0.0002
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones:
|
||||
- 80
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
steps: 1000
|
||||
|
||||
|
||||
snapshot_epoch: 3
|
||||
worker_num: 8
|
||||
TrainReader:
|
||||
inputs_def:
|
||||
num_max_boxes: 100
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomDistort: {}
|
||||
- RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
|
||||
- RandomCrop: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [576, 608, 640, 672, 704], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeBox: {}
|
||||
- PadBox: {num_max_boxes: 100}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- Gt2YoloTarget: {anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]], anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]], downsample_ratios: [32, 16, 8]}
|
||||
batch_size: 2
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 8
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, 640, 640]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
clip_grad_by_norm: 35.
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
|
||||
YOLOv3:
|
||||
backbone: ResNet
|
||||
neck: PPYOLOPAN
|
||||
yolo_head: YOLOv3Head
|
||||
post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 101
|
||||
variant: d
|
||||
return_idx: [1, 2, 3]
|
||||
dcn_v2_stages: [3]
|
||||
freeze_at: -1
|
||||
freeze_norm: false
|
||||
norm_decay: 0.
|
||||
|
||||
PPYOLOPAN:
|
||||
drop_block: true
|
||||
block_size: 3
|
||||
keep_prob: 0.9
|
||||
spp: true
|
||||
|
||||
YOLOv3Head:
|
||||
anchors: [[10, 13], [16, 30], [33, 23],
|
||||
[30, 61], [62, 45], [59, 119],
|
||||
[116, 90], [156, 198], [373, 326]]
|
||||
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
|
||||
loss: YOLOv3Loss
|
||||
iou_aware: true
|
||||
iou_aware_factor: 0.5
|
||||
|
||||
YOLOv3Loss:
|
||||
ignore_thresh: 0.7
|
||||
downsample: [32, 16, 8]
|
||||
label_smooth: false
|
||||
scale_x_y: 1.05
|
||||
iou_loss: IouLoss
|
||||
iou_aware_loss: IouAwareLoss
|
||||
|
||||
IouLoss:
|
||||
loss_weight: 2.5
|
||||
loss_square: true
|
||||
|
||||
IouAwareLoss:
|
||||
loss_weight: 1.0
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: YOLOBox
|
||||
conf_thresh: 0.01
|
||||
downsample_ratio: 32
|
||||
clip_bbox: true
|
||||
scale_x_y: 1.05
|
||||
nms:
|
||||
name: MatrixNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
post_threshold: 0.01
|
||||
nms_top_k: -1
|
||||
background_label: -1
|
||||
@@ -0,0 +1,154 @@
|
||||
architecture: YOLOv3
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json # also support txt (like VOC's label_list.txt)
|
||||
dataset_dir: dataset/battery_mini # if set, anno_path will be 'dataset_dir/anno_path'
|
||||
|
||||
|
||||
epoch: 40
|
||||
LearningRate:
|
||||
base_lr: 0.0001
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones:
|
||||
- 80
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
steps: 1000
|
||||
|
||||
snapshot_epoch: 5
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
inputs_def:
|
||||
num_max_boxes: 100
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomDistort: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [576, 608, 640, 672, 704], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeBox: {}
|
||||
- PadBox: {num_max_boxes: 100}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- Gt2YoloTarget: {anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]], anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]], downsample_ratios: [32, 16, 8]}
|
||||
batch_size: 8
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 8
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, 640, 640]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
clip_grad_by_norm: 35.
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
|
||||
YOLOv3:
|
||||
backbone: ResNet
|
||||
neck: PPYOLOPAN
|
||||
yolo_head: YOLOv3Head
|
||||
post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 50
|
||||
variant: d
|
||||
return_idx: [1, 2, 3]
|
||||
dcn_v2_stages: [3]
|
||||
freeze_at: -1
|
||||
freeze_norm: false
|
||||
norm_decay: 0.
|
||||
|
||||
PPYOLOPAN:
|
||||
drop_block: true
|
||||
block_size: 3
|
||||
keep_prob: 0.9
|
||||
spp: true
|
||||
|
||||
YOLOv3Head:
|
||||
anchors: [[10, 13], [16, 30], [33, 23],
|
||||
[30, 61], [62, 45], [59, 119],
|
||||
[116, 90], [156, 198], [373, 326]]
|
||||
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
|
||||
loss: YOLOv3Loss
|
||||
iou_aware: true
|
||||
iou_aware_factor: 0.4
|
||||
|
||||
YOLOv3Loss:
|
||||
ignore_thresh: 0.7
|
||||
downsample: [32, 16, 8]
|
||||
label_smooth: false
|
||||
scale_x_y: 1.05
|
||||
iou_loss: IouLoss
|
||||
iou_aware_loss: IouAwareLoss
|
||||
|
||||
IouLoss:
|
||||
loss_weight: 2.5
|
||||
loss_square: true
|
||||
|
||||
IouAwareLoss:
|
||||
loss_weight: 1.0
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: YOLOBox
|
||||
conf_thresh: 0.01
|
||||
downsample_ratio: 32
|
||||
clip_bbox: true
|
||||
scale_x_y: 1.05
|
||||
nms:
|
||||
name: MatrixNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
post_threshold: 0.01
|
||||
nms_top_k: -1
|
||||
background_label: -1
|
||||
@@ -0,0 +1,154 @@
|
||||
architecture: YOLOv3
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json # also support txt (like VOC's label_list.txt)
|
||||
dataset_dir: dataset/battery_mini # if set, anno_path will be 'dataset_dir/anno_path'
|
||||
|
||||
|
||||
epoch: 40
|
||||
LearningRate:
|
||||
base_lr: 0.0001
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones:
|
||||
- 80
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
steps: 1000
|
||||
|
||||
snapshot_epoch: 5
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
inputs_def:
|
||||
num_max_boxes: 100
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomDistort: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeBox: {}
|
||||
- PadBox: {num_max_boxes: 100}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- Gt2YoloTarget: {anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]], anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]], downsample_ratios: [32, 16, 8]}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [1024, 1024], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 8
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, 1024, 1024]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [1024, 1024], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
clip_grad_by_norm: 35.
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
|
||||
YOLOv3:
|
||||
backbone: ResNet
|
||||
neck: PPYOLOPAN
|
||||
yolo_head: YOLOv3Head
|
||||
post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 50
|
||||
variant: d
|
||||
return_idx: [1, 2, 3]
|
||||
dcn_v2_stages: [3]
|
||||
freeze_at: -1
|
||||
freeze_norm: false
|
||||
norm_decay: 0.
|
||||
|
||||
PPYOLOPAN:
|
||||
drop_block: true
|
||||
block_size: 3
|
||||
keep_prob: 0.9
|
||||
spp: true
|
||||
|
||||
YOLOv3Head:
|
||||
anchors: [[10, 13], [16, 30], [33, 23],
|
||||
[30, 61], [62, 45], [59, 119],
|
||||
[116, 90], [156, 198], [373, 326]]
|
||||
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
|
||||
loss: YOLOv3Loss
|
||||
iou_aware: true
|
||||
iou_aware_factor: 0.4
|
||||
|
||||
YOLOv3Loss:
|
||||
ignore_thresh: 0.7
|
||||
downsample: [32, 16, 8]
|
||||
label_smooth: false
|
||||
scale_x_y: 1.05
|
||||
iou_loss: IouLoss
|
||||
iou_aware_loss: IouAwareLoss
|
||||
|
||||
IouLoss:
|
||||
loss_weight: 2.5
|
||||
loss_square: true
|
||||
|
||||
IouAwareLoss:
|
||||
loss_weight: 1.0
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: YOLOBox
|
||||
conf_thresh: 0.01
|
||||
downsample_ratio: 32
|
||||
clip_bbox: true
|
||||
scale_x_y: 1.05
|
||||
nms:
|
||||
name: MatrixNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
post_threshold: 0.01
|
||||
nms_top_k: -1
|
||||
background_label: -1
|
||||
@@ -0,0 +1,155 @@
|
||||
architecture: YOLOv3
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
|
||||
epoch: 20
|
||||
LearningRate:
|
||||
base_lr: 0.0002
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones:
|
||||
- 80
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
steps: 1000
|
||||
|
||||
|
||||
snapshot_epoch: 3
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
inputs_def:
|
||||
num_max_boxes: 100
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomDistort: {}
|
||||
- RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
|
||||
- RandomCrop: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeBox: {}
|
||||
- PadBox: {num_max_boxes: 100}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- Gt2YoloTarget: {anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]], anchors: [[8, 7], [24, 12], [14, 25], [37, 35], [30, 140], [89, 52], [93, 189], [226, 99], [264, 352]], downsample_ratios: [32, 16, 8]}
|
||||
batch_size: 2
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [1024, 1024], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 8
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, 1024, 1024]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [1024, 1024], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
OptimizerBuilder:
|
||||
clip_grad_by_norm: 35.
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
YOLOv3:
|
||||
backbone: ResNet
|
||||
neck: PPYOLOPAN
|
||||
yolo_head: YOLOv3Head
|
||||
post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 50
|
||||
variant: d
|
||||
return_idx: [1, 2, 3]
|
||||
dcn_v2_stages: [3]
|
||||
freeze_at: -1
|
||||
freeze_norm: false
|
||||
norm_decay: 0.
|
||||
|
||||
PPYOLOPAN:
|
||||
drop_block: true
|
||||
block_size: 3
|
||||
keep_prob: 0.9
|
||||
spp: true
|
||||
|
||||
YOLOv3Head:
|
||||
anchors: [[8, 7], [24, 12], [14, 25],
|
||||
[37, 35], [30, 140], [89, 52],
|
||||
[93, 189], [226, 99], [264, 352]]
|
||||
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
|
||||
loss: YOLOv3Loss
|
||||
iou_aware: true
|
||||
iou_aware_factor: 0.5
|
||||
|
||||
YOLOv3Loss:
|
||||
ignore_thresh: 0.7
|
||||
downsample: [32, 16, 8]
|
||||
label_smooth: false
|
||||
scale_x_y: 1.05
|
||||
iou_loss: IouLoss
|
||||
iou_aware_loss: IouAwareLoss
|
||||
|
||||
IouLoss:
|
||||
loss_weight: 2.5
|
||||
loss_square: true
|
||||
|
||||
IouAwareLoss:
|
||||
loss_weight: 1.0
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: YOLOBox
|
||||
conf_thresh: 0.01
|
||||
downsample_ratio: 32
|
||||
clip_bbox: true
|
||||
scale_x_y: 1.05
|
||||
nms:
|
||||
name: MatrixNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
post_threshold: 0.01
|
||||
nms_top_k: -1
|
||||
background_label: -1
|
||||
@@ -0,0 +1,155 @@
|
||||
architecture: YOLOv3
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
|
||||
epoch: 20
|
||||
LearningRate:
|
||||
base_lr: 0.0002
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones:
|
||||
- 80
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
steps: 1000
|
||||
|
||||
|
||||
snapshot_epoch: 3
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
inputs_def:
|
||||
num_max_boxes: 100
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomDistort: {}
|
||||
- RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
|
||||
- RandomCrop: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [576, 608, 640, 672, 704], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeBox: {}
|
||||
- PadBox: {num_max_boxes: 100}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- Gt2YoloTarget: {anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]], anchors: [[8, 7], [24, 12], [14, 25], [37, 35], [30, 140], [89, 52], [93, 189], [226, 99], [264, 352]], downsample_ratios: [32, 16, 8]}
|
||||
batch_size: 2
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 8
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, 640, 640]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
OptimizerBuilder:
|
||||
clip_grad_by_norm: 35.
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
YOLOv3:
|
||||
backbone: ResNet
|
||||
neck: PPYOLOPAN
|
||||
yolo_head: YOLOv3Head
|
||||
post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 50
|
||||
variant: d
|
||||
return_idx: [1, 2, 3]
|
||||
dcn_v2_stages: [3]
|
||||
freeze_at: -1
|
||||
freeze_norm: false
|
||||
norm_decay: 0.
|
||||
|
||||
PPYOLOPAN:
|
||||
drop_block: true
|
||||
block_size: 3
|
||||
keep_prob: 0.9
|
||||
spp: true
|
||||
|
||||
YOLOv3Head:
|
||||
anchors: [[8, 7], [24, 12], [14, 25],
|
||||
[37, 35], [30, 140], [89, 52],
|
||||
[93, 189], [226, 99], [264, 352]]
|
||||
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
|
||||
loss: YOLOv3Loss
|
||||
iou_aware: true
|
||||
iou_aware_factor: 0.5
|
||||
|
||||
YOLOv3Loss:
|
||||
ignore_thresh: 0.7
|
||||
downsample: [32, 16, 8]
|
||||
label_smooth: false
|
||||
scale_x_y: 1.05
|
||||
iou_loss: IouLoss
|
||||
iou_aware_loss: IouAwareLoss
|
||||
|
||||
IouLoss:
|
||||
loss_weight: 2.5
|
||||
loss_square: true
|
||||
|
||||
IouAwareLoss:
|
||||
loss_weight: 1.0
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: YOLOBox
|
||||
conf_thresh: 0.01
|
||||
downsample_ratio: 32
|
||||
clip_bbox: true
|
||||
scale_x_y: 1.05
|
||||
nms:
|
||||
name: MatrixNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
post_threshold: 0.01
|
||||
nms_top_k: -1
|
||||
background_label: -1
|
||||
@@ -0,0 +1,156 @@
|
||||
architecture: YOLOv3
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: dataset/renche/test.json
|
||||
|
||||
|
||||
epoch: 100
|
||||
LearningRate:
|
||||
base_lr: 0.0002
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones:
|
||||
- 80
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
steps: 1000
|
||||
|
||||
|
||||
snapshot_epoch: 3
|
||||
worker_num: 8
|
||||
TrainReader:
|
||||
inputs_def:
|
||||
num_max_boxes: 100
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomDistort: {}
|
||||
- RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
|
||||
- RandomCrop: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeBox: {}
|
||||
- PadBox: {num_max_boxes: 100}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- Gt2YoloTarget: {anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]], anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]], downsample_ratios: [32, 16, 8]}
|
||||
batch_size: 2
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [1024, 1024], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 8
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, 1024, 1024]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [1024, 1024], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
clip_grad_by_norm: 35.
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
|
||||
YOLOv3:
|
||||
backbone: ResNet
|
||||
neck: PPYOLOPAN
|
||||
yolo_head: YOLOv3Head
|
||||
post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 50
|
||||
variant: d
|
||||
return_idx: [1, 2, 3]
|
||||
dcn_v2_stages: [3]
|
||||
freeze_at: -1
|
||||
freeze_norm: false
|
||||
norm_decay: 0.
|
||||
|
||||
PPYOLOPAN:
|
||||
drop_block: true
|
||||
block_size: 3
|
||||
keep_prob: 0.9
|
||||
spp: true
|
||||
|
||||
YOLOv3Head:
|
||||
anchors: [[10, 13], [16, 30], [33, 23],
|
||||
[30, 61], [62, 45], [59, 119],
|
||||
[116, 90], [156, 198], [373, 326]]
|
||||
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
|
||||
loss: YOLOv3Loss
|
||||
iou_aware: true
|
||||
iou_aware_factor: 0.5
|
||||
|
||||
YOLOv3Loss:
|
||||
ignore_thresh: 0.7
|
||||
downsample: [32, 16, 8]
|
||||
label_smooth: false
|
||||
scale_x_y: 1.05
|
||||
iou_loss: IouLoss
|
||||
iou_aware_loss: IouAwareLoss
|
||||
|
||||
IouLoss:
|
||||
loss_weight: 2.5
|
||||
loss_square: true
|
||||
|
||||
IouAwareLoss:
|
||||
loss_weight: 1.0
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: YOLOBox
|
||||
conf_thresh: 0.01
|
||||
downsample_ratio: 32
|
||||
clip_bbox: true
|
||||
scale_x_y: 1.05
|
||||
nms:
|
||||
name: MatrixNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
post_threshold: 0.01
|
||||
nms_top_k: -1
|
||||
background_label: -1
|
||||
@@ -0,0 +1,156 @@
|
||||
architecture: YOLOv3
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/coco/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/coco/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: dataset/coco/renche/test.json
|
||||
|
||||
|
||||
epoch: 100
|
||||
LearningRate:
|
||||
base_lr: 0.0002
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones:
|
||||
- 80
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
steps: 1000
|
||||
|
||||
|
||||
snapshot_epoch: 3
|
||||
worker_num: 8
|
||||
TrainReader:
|
||||
inputs_def:
|
||||
num_max_boxes: 100
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomDistort: {}
|
||||
- RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
|
||||
- RandomCrop: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [576, 608, 640, 672, 704], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeBox: {}
|
||||
- PadBox: {num_max_boxes: 100}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- Gt2YoloTarget: {anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]], anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]], downsample_ratios: [32, 16, 8]}
|
||||
batch_size: 2
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 8
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, 640, 640]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
clip_grad_by_norm: 35.
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
|
||||
YOLOv3:
|
||||
backbone: ResNet
|
||||
neck: PPYOLOPAN
|
||||
yolo_head: YOLOv3Head
|
||||
post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 50
|
||||
variant: d
|
||||
return_idx: [1, 2, 3]
|
||||
dcn_v2_stages: [3]
|
||||
freeze_at: -1
|
||||
freeze_norm: false
|
||||
norm_decay: 0.
|
||||
|
||||
PPYOLOPAN:
|
||||
drop_block: true
|
||||
block_size: 3
|
||||
keep_prob: 0.9
|
||||
spp: true
|
||||
|
||||
YOLOv3Head:
|
||||
anchors: [[10, 13], [16, 30], [33, 23],
|
||||
[30, 61], [62, 45], [59, 119],
|
||||
[116, 90], [156, 198], [373, 326]]
|
||||
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
|
||||
loss: YOLOv3Loss
|
||||
iou_aware: true
|
||||
iou_aware_factor: 0.5
|
||||
|
||||
YOLOv3Loss:
|
||||
ignore_thresh: 0.7
|
||||
downsample: [32, 16, 8]
|
||||
label_smooth: false
|
||||
scale_x_y: 1.05
|
||||
iou_loss: IouLoss
|
||||
iou_aware_loss: IouAwareLoss
|
||||
|
||||
IouLoss:
|
||||
loss_weight: 2.5
|
||||
loss_square: true
|
||||
|
||||
IouAwareLoss:
|
||||
loss_weight: 1.0
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: YOLOBox
|
||||
conf_thresh: 0.01
|
||||
downsample_ratio: 32
|
||||
clip_bbox: true
|
||||
scale_x_y: 1.05
|
||||
nms:
|
||||
name: MatrixNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
post_threshold: 0.01
|
||||
nms_top_k: -1
|
||||
background_label: -1
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_l_300e_battery/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
|
||||
depth_mult: 1.0
|
||||
width_mult: 1.0
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 640
|
||||
eval_width: &eval_width 640
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0005
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [512, 544, 576, 608, 640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_l_300e_battery_1024/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
|
||||
depth_mult: 1.0
|
||||
width_mult: 1.0
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 1024
|
||||
eval_width: &eval_width 1024
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0005
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_l_300e_lvjian1/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
|
||||
depth_mult: 1.0
|
||||
width_mult: 1.0
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 640
|
||||
eval_width: &eval_width 640
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.001
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 8
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 1
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_l_300e_lvjian1_1024/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
|
||||
depth_mult: 1.0
|
||||
width_mult: 1.0
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 1024
|
||||
eval_width: &eval_width 1024
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.001
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 8
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 1
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_l_300e_renche/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
|
||||
depth_mult: 1.0
|
||||
width_mult: 1.0
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 640
|
||||
eval_width: &eval_width 640
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0005
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [512, 544, 576, 608, 640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_l_300e_renche_1024/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
|
||||
depth_mult: 1.0
|
||||
width_mult: 1.0
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 1024
|
||||
eval_width: &eval_width 1024
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0005
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_m_300e_battery/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams
|
||||
depth_mult: 0.67
|
||||
width_mult: 0.75
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 640
|
||||
eval_width: &eval_width 640
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0005
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_m_300e_battery_1024/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams
|
||||
depth_mult: 0.67
|
||||
width_mult: 0.75
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 1024
|
||||
eval_width: &eval_width 1024
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0005
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_m_300e_lvjian1/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams
|
||||
depth_mult: 0.67
|
||||
width_mult: 0.75
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 640
|
||||
eval_width: &eval_width 640
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.002
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 16
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 1
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_m_300e_lvjian1_1024/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams
|
||||
depth_mult: 0.67
|
||||
width_mult: 0.75
|
||||
|
||||
worker_num: 2
|
||||
eval_height: &eval_height 1024
|
||||
eval_width: &eval_width 1024
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0015
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 8
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 2
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_m_300e_renche/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams
|
||||
depth_mult: 0.67
|
||||
width_mult: 0.75
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 640
|
||||
eval_width: &eval_width 640
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0005
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_m_300e_renche_1024/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams
|
||||
depth_mult: 0.67
|
||||
width_mult: 0.75
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 1024
|
||||
eval_width: &eval_width 1024
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: /paddle/dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: /paddle/dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: test.json
|
||||
dataset_dir: /paddle/dataset/renche
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0005
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_s_300e_battery/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams
|
||||
depth_mult: 0.33
|
||||
width_mult: 0.50
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 640
|
||||
eval_width: &eval_width 640
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0005
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [512, 544, 576, 608, 640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_s_300e_battery_1024/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams
|
||||
depth_mult: 0.33
|
||||
width_mult: 0.50
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 1024
|
||||
eval_width: &eval_width 1024
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0005
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_s_300e_lvjian1/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams
|
||||
depth_mult: 0.33
|
||||
width_mult: 0.50
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 640
|
||||
eval_width: &eval_width 640
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.002
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 16
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 1
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_s_300e_lvjian1_1024/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams
|
||||
depth_mult: 0.33
|
||||
width_mult: 0.50
|
||||
|
||||
worker_num: 2
|
||||
eval_height: &eval_height 1024
|
||||
eval_width: &eval_width 1024
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.003
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 16
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 2
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_s_300e_renche/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams
|
||||
depth_mult: 0.33
|
||||
width_mult: 0.50
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 640
|
||||
eval_width: &eval_width 640
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0005
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [12, 544, 576, 608, 640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_s_300e_renche_1024/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams
|
||||
depth_mult: 0.33
|
||||
width_mult: 0.50
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 1024
|
||||
eval_width: &eval_width 1024
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0005
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_x_300e_battery/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams
|
||||
depth_mult: 1.33
|
||||
width_mult: 1.25
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 640
|
||||
eval_width: &eval_width 640
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0005
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [512, 544, 576, 608, 640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_x_300e_battery_1024/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams
|
||||
depth_mult: 1.33
|
||||
width_mult: 1.25
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 1024
|
||||
eval_width: &eval_width 1024
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0005
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_x_300e_lvjian1/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams
|
||||
depth_mult: 1.33
|
||||
width_mult: 1.25
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 640
|
||||
eval_width: &eval_width 640
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.001
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 8
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 1
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_x_300e_lvjian1/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams
|
||||
depth_mult: 1.33
|
||||
width_mult: 1.25
|
||||
|
||||
worker_num: 2
|
||||
eval_height: &eval_height 1024
|
||||
eval_width: &eval_width 1024
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0005
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 2
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_x_300e_renche/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams
|
||||
depth_mult: 1.33
|
||||
width_mult: 1.25
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 640
|
||||
eval_width: &eval_width 640
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0005
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [512, 544, 576, 608, 640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,140 @@
|
||||
weights: output/ppyoloe_crn_x_300e_renche_1024/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams
|
||||
depth_mult: 1.33
|
||||
width_mult: 1.25
|
||||
|
||||
worker_num: 4
|
||||
eval_height: &eval_height 1024
|
||||
eval_width: &eval_width 1024
|
||||
eval_size: &eval_size [*eval_height, *eval_width]
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
epoch: 30
|
||||
LearningRate:
|
||||
base_lr: 0.0005
|
||||
schedulers:
|
||||
- !CosineDecay
|
||||
max_epochs: 36
|
||||
- !LinearWarmup
|
||||
start_factor: 0.
|
||||
epochs: 3
|
||||
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomFlip: {}
|
||||
batch_transforms:
|
||||
- BatchRandomResize: {target_size: [960, 992, 1024, 1056, 1088], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
- PadGT: {}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
use_shared_memory: true
|
||||
collate_batch: true
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
|
||||
TestReader:
|
||||
inputs_def:
|
||||
image_shape: [3, *eval_height, *eval_width]
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
|
||||
# Exporting the model
|
||||
export:
|
||||
post_process: True # Whether post-processing is included in the network when export model.
|
||||
nms: True # Whether NMS is included in the network when export model.
|
||||
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0005
|
||||
type: L2
|
||||
|
||||
architecture: YOLOv3
|
||||
norm_type: sync_bn
|
||||
use_ema: true
|
||||
ema_decay: 0.9998
|
||||
|
||||
YOLOv3:
|
||||
backbone: CSPResNet
|
||||
neck: CustomCSPPAN
|
||||
yolo_head: PPYOLOEHead
|
||||
post_process: ~
|
||||
|
||||
CSPResNet:
|
||||
layers: [3, 6, 6, 3]
|
||||
channels: [64, 128, 256, 512, 1024]
|
||||
return_idx: [1, 2, 3]
|
||||
use_large_stem: True
|
||||
|
||||
CustomCSPPAN:
|
||||
out_channels: [768, 384, 192]
|
||||
stage_num: 1
|
||||
block_num: 3
|
||||
act: 'swish'
|
||||
spp: true
|
||||
|
||||
PPYOLOEHead:
|
||||
fpn_strides: [32, 16, 8]
|
||||
grid_cell_scale: 5.0
|
||||
grid_cell_offset: 0.5
|
||||
static_assigner_epoch: 100
|
||||
use_varifocal_loss: True
|
||||
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
|
||||
static_assigner:
|
||||
name: ATSSAssigner
|
||||
topk: 9
|
||||
assigner:
|
||||
name: TaskAlignedAssigner
|
||||
topk: 13
|
||||
alpha: 1.0
|
||||
beta: 6.0
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
nms_top_k: 1000
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.01
|
||||
nms_threshold: 0.6
|
||||
@@ -0,0 +1,168 @@
|
||||
weights: output/cascade_rcnn_r50_vd_fpn_ssld_2x_1500_battery/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.00025
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [12, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [1350,1425,1500,1575,1650], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
find_unused_parameters: True
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
architecture: CascadeRCNN
|
||||
|
||||
CascadeRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: CascadeHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 50
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 2000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
CascadeHead:
|
||||
head: CascadeTwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
cascade_iou: [0.5, 0.6, 0.7]
|
||||
use_random: True
|
||||
|
||||
CascadeTwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: RCNNBox
|
||||
prior_box_var: [30.0, 30.0, 15.0, 15.0]
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
@@ -0,0 +1,168 @@
|
||||
weights: output/cascade_rcnn_r50_vd_fpn_ssld_2x_1500_lvjian1/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train/
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.00025
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [12, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [1350,1425,1500,1575,1650], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
find_unused_parameters: True
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
architecture: CascadeRCNN
|
||||
|
||||
CascadeRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: CascadeHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 50
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 2000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
CascadeHead:
|
||||
head: CascadeTwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
cascade_iou: [0.5, 0.6, 0.7]
|
||||
use_random: True
|
||||
|
||||
CascadeTwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: RCNNBox
|
||||
prior_box_var: [30.0, 30.0, 15.0, 15.0]
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
@@ -0,0 +1,168 @@
|
||||
weights: output/cascade_rcnn_r50_vd_fpn_ssld_2x_1500_renche/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.00025
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [12, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [1350,1425,1500,1575,1650], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
find_unused_parameters: True
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
architecture: CascadeRCNN
|
||||
|
||||
CascadeRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: CascadeHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 50
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 2000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
CascadeHead:
|
||||
head: CascadeTwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
cascade_iou: [0.5, 0.6, 0.7]
|
||||
use_random: True
|
||||
|
||||
CascadeTwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: RCNNBox
|
||||
prior_box_var: [30.0, 30.0, 15.0, 15.0]
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
@@ -0,0 +1,168 @@
|
||||
weights: output/cascade_rcnn_r50_vd_fpn_ssld_2x_800_battery/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.00025
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [12, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
find_unused_parameters: True
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
architecture: CascadeRCNN
|
||||
|
||||
CascadeRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: CascadeHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 50
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 2000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
CascadeHead:
|
||||
head: CascadeTwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
cascade_iou: [0.5, 0.6, 0.7]
|
||||
use_random: True
|
||||
|
||||
CascadeTwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: RCNNBox
|
||||
prior_box_var: [30.0, 30.0, 15.0, 15.0]
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
@@ -0,0 +1,168 @@
|
||||
weights: output/cascade_rcnn_r50_vd_fpn_ssld_2x_800_lvjian1/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train/
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.00025
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [12, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
find_unused_parameters: True
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
architecture: CascadeRCNN
|
||||
|
||||
CascadeRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: CascadeHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 50
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 2000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
CascadeHead:
|
||||
head: CascadeTwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
cascade_iou: [0.5, 0.6, 0.7]
|
||||
use_random: True
|
||||
|
||||
CascadeTwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: RCNNBox
|
||||
prior_box_var: [30.0, 30.0, 15.0, 15.0]
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
@@ -0,0 +1,168 @@
|
||||
weights: output/cascade_rcnn_r50_vd_fpn_ssld_2x_800_renche/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.00025
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [12, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 5
|
||||
print_flops: false
|
||||
find_unused_parameters: True
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
architecture: CascadeRCNN
|
||||
|
||||
CascadeRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: CascadeHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
ResNet:
|
||||
depth: 50
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 2000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
CascadeHead:
|
||||
head: CascadeTwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
cascade_iou: [0.5, 0.6, 0.7]
|
||||
use_random: True
|
||||
|
||||
CascadeTwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode:
|
||||
name: RCNNBox
|
||||
prior_box_var: [30.0, 30.0, 15.0, 15.0]
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
@@ -0,0 +1,166 @@
|
||||
weights: output/faster_rcnn_r101_vd_fpn_ssld_2x_1500_battery/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/faster_rcnn_r101_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.001
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [16, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [[800, 800], [900, 900], [1000, 1000], [1200, 1200], [1400, 1400], [1500, 1500]], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 2
|
||||
print_flops: false
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
|
||||
architecture: FasterRCNN
|
||||
|
||||
FasterRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: BBoxHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
|
||||
ResNet:
|
||||
# index 0 stands for res2
|
||||
depth: 101
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 1000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
BBoxHead:
|
||||
head: TwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
use_random: True
|
||||
|
||||
TwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode: RCNNBox
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
@@ -0,0 +1,166 @@
|
||||
weights: output/faster_rcnn_r101_vd_fpn_ssld_2x_1500_lvjian1/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/faster_rcnn_r101_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train/
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.001
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [16, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [[800, 800], [900, 900], [1000, 1000], [1200, 1200], [1400, 1400], [1500, 1500]], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 2
|
||||
print_flops: false
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
|
||||
architecture: FasterRCNN
|
||||
|
||||
FasterRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: BBoxHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
|
||||
ResNet:
|
||||
# index 0 stands for res2
|
||||
depth: 101
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 1000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
BBoxHead:
|
||||
head: TwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
use_random: True
|
||||
|
||||
TwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode: RCNNBox
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
@@ -0,0 +1,166 @@
|
||||
weights: output/faster_rcnn_r101_vd_fpn_ssld_2x_1500_renche/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/faster_rcnn_r101_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.001
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [16, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [[800, 800], [900, 900], [1000, 1000], [1200, 1200], [1400, 1400], [1500, 1500]], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 2
|
||||
print_flops: false
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
|
||||
architecture: FasterRCNN
|
||||
|
||||
FasterRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: BBoxHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
|
||||
ResNet:
|
||||
# index 0 stands for res2
|
||||
depth: 101
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 1000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
BBoxHead:
|
||||
head: TwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
use_random: True
|
||||
|
||||
TwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode: RCNNBox
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
@@ -0,0 +1,167 @@
|
||||
weights: output/faster_rcnn_r101_vd_fpn_ssld_2x_800_lvjian1/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/faster_rcnn_r101_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.001
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [16, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 2
|
||||
print_flops: false
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
|
||||
architecture: FasterRCNN
|
||||
|
||||
FasterRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: BBoxHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
|
||||
ResNet:
|
||||
# index 0 stands for res2
|
||||
depth: 101
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 1000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
BBoxHead:
|
||||
head: TwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
use_random: True
|
||||
|
||||
TwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode: RCNNBox
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
@@ -0,0 +1,167 @@
|
||||
weights: output/faster_rcnn_r101_vd_fpn_ssld_2x_800_lvjian1/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/faster_rcnn_r101_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train/
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.001
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [16, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 2
|
||||
print_flops: false
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
|
||||
architecture: FasterRCNN
|
||||
|
||||
FasterRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: BBoxHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
|
||||
ResNet:
|
||||
# index 0 stands for res2
|
||||
depth: 101
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 1000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
BBoxHead:
|
||||
head: TwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
use_random: True
|
||||
|
||||
TwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode: RCNNBox
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
@@ -0,0 +1,167 @@
|
||||
weights: output/faster_rcnn_r101_vd_fpn_ssld_2x_800_renche/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/faster_rcnn_r101_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.001
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [16, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 2
|
||||
print_flops: false
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
|
||||
architecture: FasterRCNN
|
||||
|
||||
FasterRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: BBoxHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
|
||||
ResNet:
|
||||
# index 0 stands for res2
|
||||
depth: 101
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 1000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
BBoxHead:
|
||||
head: TwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
use_random: True
|
||||
|
||||
TwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode: RCNNBox
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
@@ -0,0 +1,166 @@
|
||||
weights: output/faster_rcnn_r50_vd_fpn_ssld_2x_1500_battery/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.001
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [16, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [[800, 800], [900, 900], [1000, 1000], [1200, 1200], [1400, 1400], [1500, 1500]], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 2
|
||||
print_flops: false
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
|
||||
architecture: FasterRCNN
|
||||
|
||||
FasterRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: BBoxHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
|
||||
ResNet:
|
||||
# index 0 stands for res2
|
||||
depth: 50
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 1000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
BBoxHead:
|
||||
head: TwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
use_random: True
|
||||
|
||||
TwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode: RCNNBox
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
@@ -0,0 +1,166 @@
|
||||
weights: output/faster_rcnn_r50_vd_fpn_ssld_2x_1500_lvjian1/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train/
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.001
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [16, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [[800, 800], [900, 900], [1000, 1000], [1200, 1200], [1400, 1400], [1500, 1500]], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 2
|
||||
print_flops: false
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
|
||||
architecture: FasterRCNN
|
||||
|
||||
FasterRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: BBoxHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
|
||||
ResNet:
|
||||
# index 0 stands for res2
|
||||
depth: 50
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 1000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
BBoxHead:
|
||||
head: TwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
use_random: True
|
||||
|
||||
TwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode: RCNNBox
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
@@ -0,0 +1,166 @@
|
||||
weights: output/faster_rcnn_r50_vd_fpn_ssld_2x_1500_renche/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.001
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [16, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [[800, 800], [900, 900], [1000, 1000], [1200, 1200], [1400, 1400], [1500, 1500]], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [1500, 1500], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 2
|
||||
print_flops: false
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
|
||||
architecture: FasterRCNN
|
||||
|
||||
FasterRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: BBoxHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
|
||||
ResNet:
|
||||
# index 0 stands for res2
|
||||
depth: 50
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 1000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
BBoxHead:
|
||||
head: TwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
use_random: True
|
||||
|
||||
TwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode: RCNNBox
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
@@ -0,0 +1,167 @@
|
||||
weights: output/faster_rcnn_r50_vd_fpn_ssld_2x_800_lvjian1/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 45
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/train.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/test.json
|
||||
dataset_dir: dataset/battery_mini
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.001
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [16, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 2
|
||||
print_flops: false
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
|
||||
architecture: FasterRCNN
|
||||
|
||||
FasterRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: BBoxHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
|
||||
ResNet:
|
||||
# index 0 stands for res2
|
||||
depth: 50
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 1000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
BBoxHead:
|
||||
head: TwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
use_random: True
|
||||
|
||||
TwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode: RCNNBox
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
@@ -0,0 +1,167 @@
|
||||
weights: output/faster_rcnn_r50_vd_fpn_ssld_2x_800_lvjian1/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 5
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/train/
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: images
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: val.json
|
||||
dataset_dir: dataset/slice_lvjian1_data/eval
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.001
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [16, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 2
|
||||
print_flops: false
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
|
||||
architecture: FasterRCNN
|
||||
|
||||
FasterRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: BBoxHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
|
||||
ResNet:
|
||||
# index 0 stands for res2
|
||||
depth: 50
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 1000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
BBoxHead:
|
||||
head: TwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
use_random: True
|
||||
|
||||
TwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode: RCNNBox
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
@@ -0,0 +1,167 @@
|
||||
weights: output/faster_rcnn_r50_vd_fpn_ssld_2x_800_renche/model_final
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams
|
||||
|
||||
metric: COCO
|
||||
num_classes: 22
|
||||
|
||||
TrainDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: train.json
|
||||
dataset_dir: dataset/renche
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: train_images
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: test.json
|
||||
dataset_dir: dataset/renche
|
||||
|
||||
epoch: 24
|
||||
LearningRate:
|
||||
base_lr: 0.001
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: [16, 22]
|
||||
- !LinearWarmup
|
||||
start_factor: 0.1
|
||||
steps: 1000
|
||||
|
||||
|
||||
worker_num: 2
|
||||
TrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], interp: 2, keep_ratio: True}
|
||||
- RandomFlip: {prob: 0.5}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 4
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
|
||||
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
|
||||
- Permute: {}
|
||||
batch_transforms:
|
||||
- PadBatch: {pad_to_stride: -1}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
use_gpu: true
|
||||
use_xpu: false
|
||||
log_iter: 100
|
||||
save_dir: output
|
||||
snapshot_epoch: 2
|
||||
print_flops: false
|
||||
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
|
||||
|
||||
architecture: FasterRCNN
|
||||
|
||||
FasterRCNN:
|
||||
backbone: ResNet
|
||||
neck: FPN
|
||||
rpn_head: RPNHead
|
||||
bbox_head: BBoxHead
|
||||
# post process
|
||||
bbox_post_process: BBoxPostProcess
|
||||
|
||||
|
||||
ResNet:
|
||||
# index 0 stands for res2
|
||||
depth: 50
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [0,1,2,3]
|
||||
num_stages: 4
|
||||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||||
|
||||
FPN:
|
||||
out_channel: 256
|
||||
|
||||
RPNHead:
|
||||
anchor_generator:
|
||||
aspect_ratios: [0.5, 1.0, 2.0]
|
||||
anchor_sizes: [[32], [64], [128], [256], [512]]
|
||||
strides: [4, 8, 16, 32, 64]
|
||||
rpn_target_assign:
|
||||
batch_size_per_im: 256
|
||||
fg_fraction: 0.5
|
||||
negative_overlap: 0.3
|
||||
positive_overlap: 0.7
|
||||
use_random: True
|
||||
train_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 2000
|
||||
post_nms_top_n: 1000
|
||||
topk_after_collect: True
|
||||
test_proposal:
|
||||
min_size: 0.0
|
||||
nms_thresh: 0.7
|
||||
pre_nms_top_n: 1000
|
||||
post_nms_top_n: 1000
|
||||
|
||||
|
||||
BBoxHead:
|
||||
head: TwoFCHead
|
||||
roi_extractor:
|
||||
resolution: 7
|
||||
sampling_ratio: 0
|
||||
aligned: True
|
||||
bbox_assigner: BBoxAssigner
|
||||
|
||||
BBoxAssigner:
|
||||
batch_size_per_im: 512
|
||||
bg_thresh: 0.5
|
||||
fg_thresh: 0.5
|
||||
fg_fraction: 0.25
|
||||
use_random: True
|
||||
|
||||
TwoFCHead:
|
||||
out_channel: 1024
|
||||
|
||||
BBoxPostProcess:
|
||||
decode: RCNNBox
|
||||
nms:
|
||||
name: MultiClassNMS
|
||||
keep_top_k: 100
|
||||
score_threshold: 0.05
|
||||
nms_threshold: 0.5
|
||||
Reference in New Issue
Block a user