[cherry-pick] ppsmrt (#6010)

* add ppsmrt, test=document_fix * fix picodet cfg, test=document_fix * add rcnn & picodet, test=document_fix * rename battery dataset * add faster rcnn * update README, test=document_fix * add data analysis, test=document_fix * add faster r101, test=document_fix

[cherry-pick] ppsmrt (#6010)
* add ppsmrt, test=document_fix * fix picodet cfg, test=document_fix * add rcnn & picodet, test=document_fix * rename battery dataset * add faster rcnn * update README, test=document_fix * add data analysis, test=document_fix * add faster r101, test=document_fix
40503f14 · wangguanzhong · GitHub · c0ede7b1 · 40503f14 · 40503f14
68 changed file
--- a/ppsmrt/DataAnalysis.md
+++ b/ppsmrt/DataAnalysis.md
+# 数据分析功能说明
+
+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。
+
+当前支持格式有：
+* LabelMe标注数据格式
+* 精灵标注数据格式
+* LabelImg标注数据格式
+* VOC数据格式
+* COCO数据格式
+* Seg数据格式
+
+## LabelMe标注数据格式
+
+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。
+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。
+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。
+<div align="center">
+  <img src="https://user-images.githubusercontent.com/48433081/169194724-c3fff1db-78b0-4013-925b-b99e5f51e5f2.png"  width = "600" />  
+</div>
+
+## 精灵标注数据格式
+
+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。
+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。
+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。
+<div align="center">
+  <img src="https://user-images.githubusercontent.com/48433081/169194724-c3fff1db-78b0-4013-925b-b99e5f51e5f2.png"  width = "600" />  
+</div>
+
+## LabelImg标注数据格式
+
+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。
+2. 仅支持检测任务。
+3. 标注文件中必须提供bndbox字段信息；segmentation字段是可选的。
+
+<div align="center">
+  <img src="https://user-images.githubusercontent.com/48433081/169195232-2ccd4c07-8203-44a5-9911-97c092a228d8.png"  width = "600" />  
+</div>
+
+## VOC数据格式
+
+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。
+2. 仅支持检测任务。
+3. 标注文件中必须提供bndbox字段信息；segmentation字段是可选的。
+<div align="center">
+  <img src="https://user-images.githubusercontent.com/48433081/169195232-2ccd4c07-8203-44a5-9911-97c092a228d8.png"  width = "600" />  
+</div>
+
+## COCO数据格式
+
+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。
+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。
+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。
+<div align="center">
+  <img src="https://user-images.githubusercontent.com/48433081/169195416-eb12f1bb-6d18-4354-bad5-c18961aa049d.png"  width = "600" />  
+</div>
+
+
+## Seg数据格式
+
+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。
+2. 仅支持分割任务。
+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。
+
+<div align="center">
+  <img src="https://user-images.githubusercontent.com/48433081/169195389-85a9bda2-282b-452f-a809-d0100291f86f.png"  width = "600" />  
+</div>
--- a/ppsmrt/README.md
+++ b/ppsmrt/README.md
+# 飞桨产业模型选型工具PP-SMRT
+
+## 一、项目介绍
+
+PP-SMRT (PaddlePaddle Sense Model Recommend Tool) 是飞桨结合产业落地经验推出的产业模型选型工具，在项目落地过程中，用户根据自身的实际情况，输入自己的需求，即可以得到对应在算法模型、部署硬件以及教程文档的信息。同时为了更加精准的推荐，增加了数据分析功能，用户上传自己的标注文件，系统可以自动分析数据特点，例如数据分布不均衡、小目标、密集型等，从而提供更加精准的模型以及优化策略，更好的符合场景的需求。
+
+PP-SMRT用户图
+
+本文档主要介绍PP-SMRT在检测方向上是如何进行模型选型推荐，以及推荐模型的使用方法。分割方向模型介绍请参考[文档](https://github.com/PaddlePaddle/PaddleSeg)
+
+## 二、数据介绍
+
+PP-SMRT结合产业真实场景，通过比较检测算法效果，向用户推荐最适合的模型。目前PP-SMRT覆盖工业质检、城市安防两大场景，下面介绍PP-SMRT进行算法对比所使用的数据集
+
+### 1. 新能源电池质检数据集
+
+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。
+
+新能源电池数据展示图:
+
+<div align="center">
+  <img src="images/Board_diaojiao_1591.png"  width = "200" />  
+  <img src="images/UpCoa_liewen_163.png"  width = "200" />  
+</div>
+
+数据集特点为：
+
+1. 类别分布均衡
+2. 属于小目标数据
+3. 非密集型数据
+
+### 2. 铝件质检数据集
+
+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。
+
+铝件质检数据展示图:
+
+<div align="center">
+  <img src="images/lvjian1_0.jpg"  width = "200" />  
+  <img src="images/lvjian1_10.jpg"  width = "200" />  
+</div>
+
+
+数据集特点为：
+
+1. 类别分布不均衡
+2. 属于小目标数据
+3. 非密集型数据
+
+
+### 3. 人车数据集
+
+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：
+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。
+
+人车数据展示图:
+
+<div align="center">
+  <img src="images/renche_00002.jpg"  width = "200" />  
+  <img src="images/renche_00204.jpg"  width = "200" />  
+</div>
+
+
+数据集特点为：
+
+1. 类别分布不均衡
+2. 属于小目标数据
+3. 非密集型数据
+
+**说明：**
+
+数据集特点判断依据如下：
+
+- 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
+- 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%
+- 密集型数据集：
+
+```
+    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；
+
+    密集图片定义：密集目标个数占图片目标总数50%以上；
+
+    密集数据集定义：密集图片个数占总个数30%以上
+
+```
+
+以上数据集特点通过数据分析工具实现，当前支持多种检测、分割数据标注格式，详细文档说明请参考[链接](DataAnalysis.md)
+
+## 三、推荐模型使用全流程
+
+通过模型选型工具会得到对应场景和数据特点的检测模型配置，例如[PP-YOLOE](./configs/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml)
+
+该配置文件的使用方法如下
+
+### 1. 环境配置
+
+首先需要安装PaddlePaddle
+
+```bash
+# CUDA10.2
+pip install paddlepaddle-gpu==2.2.2 -i https://mirror.baidu.com/pypi/simple
+
+# CPU
+pip install paddlepaddle==2.2.2 -i https://mirror.baidu.com/pypi/simple
+```
+
+然后安装PaddleDetection和相关依赖
+
+```bash
+# 克隆PaddleDetection仓库
+cd <path/to/clone/PaddleDetection>
+git clone https://github.com/PaddlePaddle/PaddleDetection.git
+
+# 安装其他依赖
+cd PaddleDetection
+pip install -r requirements.txt
+```
+
+详细安装文档请参考[文档](../docs/tutorials/INSTALL_cn.md)
+
+### 2. 数据准备
+
+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/PrepareDataSet.md)
+
+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)
+
+数据储存格式如下：
+
+```
+battery_mini
+├── annotations
+│   ├── test.json
+│   └── train.json
+└── images
+    ├── Board_daowen_101.png
+    ├── Board_daowen_109.png
+    ├── Board_daowen_117.png
+    ...
+```
+
+
+
+### 3. 模型训练/评估/预测
+
+使用经过模型选型工具推荐的模型进行训练，目前所推荐的模型均使用**单卡训练**，可以在训练的过程中进行评估，模型默认保存在`./output`下
+
+```bash
+python tools/train.py -c configs/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml  --eval
+```
+
+如果训练过程出现中断，可以使用-r命令恢复训练
+
+```bash
+python tools/train.py -c configs/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml  --eval -r output/ppyoloe_crn_m_300e_battery_1024/9.pdparams
+```
+
+如果期望单独评估模型训练精度，可以使用`tools/eval.py`
+
+```bash
+python tools/eval.py -c configs/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml -o weights=output/ppyoloe_crn_m_300e_battery_1024/model_final.pdparams
+```
+
+完成训练后，可以使用`tools/infer.py`可视化训练效果
+
+```bash
+python tools/infer.py -c configs/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml -o weights=output/ppyoloe_crn_m_300e_battery_1024/model_final.pdparams --infer_img=images/Board_diaojiao_1591.png
+```
+
+更多模型训练参数请参考[文档](../docs/tutorials/GETTING_STARTED_cn.md)
+
+### 4. 模型导出部署
+
+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
+
+首先需要将模型导出为部署时使用的模型和配置文件
+
+```bash
+python tools/export_model.py -c configs/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml -o weights=output/ppyoloe_crn_m_300e_battery_1024/model_final.pdparams
+```
+
+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)
+
+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。
+
+## 四、部署demo
+
+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
+
+* [Windows Demo下载地址](https://github.com/PaddlePaddle/PaddleX/tree/develop/deploy/cpp/docs/csharp_deploy)
+
+<div align="center">
+  <img src="https://user-images.githubusercontent.com/48433081/169064583-c931f4c0-dfd6-4bfa-85f1-be68eb351e4a.png"  width = "800" />  
+</div>
+
+* [Linux Demo下载地址](https://github.com/cjh3020889729/The-PaddleX-QT-Visualize-GUI)
+
+<div align="center">
+  <img src="https://user-images.githubusercontent.com/48433081/169065951-147f8d51-bf3e-4a28-9197-d717968de73f.png"  width = "800" />  
+</div>
+
+## 五、场景范例
+
+为了更方便大家更好的进行产业落地，PP-SMRT也提供了详细的应用范例，欢迎大家使用。
+
+* 工业视觉
+  * [工业缺陷检测](https://aistudio.baidu.com/aistudio/projectdetail/2598319)
+  * [表计读数](https://aistudio.baidu.com/aistudio/projectdetail/2598327)
+  * [钢筋计数](https://aistudio.baidu.com/aistudio/projectdetail/2404188)
+* 城市  
+  * [行人计数](https://aistudio.baidu.com/aistudio/projectdetail/2421822)
+  * [车辆计数](https://aistudio.baidu.com/aistudio/projectdetail/3391734?contributionType=1)
+  * [安全帽检测](https://aistudio.baidu.com/aistudio/projectdetail/3944737?contributionType=1)
--- a/ppsmrt/configs/picodet/picodet_l_1024_coco_lcnet_battery.yml
+++ b/ppsmrt/configs/picodet/picodet_l_1024_coco_lcnet_battery.yml
+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
--- a/ppsmrt/configs/picodet/picodet_l_1024_coco_lcnet_lvjian1.yml
+++ b/ppsmrt/configs/picodet/picodet_l_1024_coco_lcnet_lvjian1.yml
+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
--- a/ppsmrt/configs/picodet/picodet_l_1024_coco_lcnet_renche.yml
+++ b/ppsmrt/configs/picodet/picodet_l_1024_coco_lcnet_renche.yml
+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
--- a/ppsmrt/configs/picodet/picodet_l_640_coco_lcnet_battery.yml
+++ b/ppsmrt/configs/picodet/picodet_l_640_coco_lcnet_battery.yml
+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
--- a/ppsmrt/configs/picodet/picodet_l_640_coco_lcnet_lvjian1.yml
+++ b/ppsmrt/configs/picodet/picodet_l_640_coco_lcnet_lvjian1.yml
+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
--- a/ppsmrt/configs/picodet/picodet_l_640_coco_lcnet_renche.yml
+++ b/ppsmrt/configs/picodet/picodet_l_640_coco_lcnet_renche.yml
+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
--- a/ppsmrt/configs/ppyolo/ppyolov2_r101vd_dcn_365e_battery.yml
+++ b/ppsmrt/configs/ppyolo/ppyolov2_r101vd_dcn_365e_battery.yml
+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
--- a/ppsmrt/configs/ppyolo/ppyolov2_r101vd_dcn_365e_battery_1024.yml
+++ b/ppsmrt/configs/ppyolo/ppyolov2_r101vd_dcn_365e_battery_1024.yml
+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
--- a/ppsmrt/configs/ppyolo/ppyolov2_r101vd_dcn_365e_lvjian1_1024.yml
+++ b/ppsmrt/configs/ppyolo/ppyolov2_r101vd_dcn_365e_lvjian1_1024.yml
+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
--- a/ppsmrt/configs/ppyolo/ppyolov2_r101vd_dcn_365e_lvjian1_640.yml
+++ b/ppsmrt/configs/ppyolo/ppyolov2_r101vd_dcn_365e_lvjian1_640.yml
+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
--- a/ppsmrt/configs/ppyolo/ppyolov2_r101vd_dcn_365e_renche_1024.yml
+++ b/ppsmrt/configs/ppyolo/ppyolov2_r101vd_dcn_365e_renche_1024.yml
+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
--- a/ppsmrt/configs/ppyolo/ppyolov2_r101vd_dcn_365e_renche_640.yml
+++ b/ppsmrt/configs/ppyolo/ppyolov2_r101vd_dcn_365e_renche_640.yml
+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
--- a/ppsmrt/configs/ppyolo/ppyolov2_r50vd_dcn_365e_battery.yml
+++ b/ppsmrt/configs/ppyolo/ppyolov2_r50vd_dcn_365e_battery.yml
+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
--- a/ppsmrt/configs/ppyolo/ppyolov2_r50vd_dcn_365e_battery_1024.yml
+++ b/ppsmrt/configs/ppyolo/ppyolov2_r50vd_dcn_365e_battery_1024.yml
+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
--- a/ppsmrt/configs/ppyolo/ppyolov2_r50vd_dcn_365e_lvjian1_1024.yml
+++ b/ppsmrt/configs/ppyolo/ppyolov2_r50vd_dcn_365e_lvjian1_1024.yml
+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
--- a/ppsmrt/configs/ppyolo/ppyolov2_r50vd_dcn_365e_lvjian1_640.yml
+++ b/ppsmrt/configs/ppyolo/ppyolov2_r50vd_dcn_365e_lvjian1_640.yml
+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
--- a/ppsmrt/configs/ppyolo/ppyolov2_r50vd_dcn_365e_renche_1024.yml
+++ b/ppsmrt/configs/ppyolo/ppyolov2_r50vd_dcn_365e_renche_1024.yml
+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
--- a/ppsmrt/configs/ppyolo/ppyolov2_r50vd_dcn_365e_renche_640.yml
+++ b/ppsmrt/configs/ppyolo/ppyolov2_r50vd_dcn_365e_renche_640.yml
+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
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_l_300e_battery.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_l_300e_battery.yml
+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
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_l_300e_battery_1024.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_l_300e_battery_1024.yml
+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
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_l_300e_lvjian1.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_l_300e_lvjian1.yml
+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
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_l_300e_lvjian1_1024.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_l_300e_lvjian1_1024.yml
+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
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_l_300e_renche.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_l_300e_renche.yml
+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
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_l_300e_renche_1024.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_l_300e_renche_1024.yml
+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
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_m_300e_battery.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_m_300e_battery.yml
+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
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_m_300e_battery_1024.yml
+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
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_m_300e_lvjian1.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_m_300e_lvjian1.yml
+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
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_m_300e_lvjian1_1024.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_m_300e_lvjian1_1024.yml
+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
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_m_300e_renche.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_m_300e_renche.yml
+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
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_m_300e_renche_1024.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_m_300e_renche_1024.yml
+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
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_s_300e_battery.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_s_300e_battery.yml
+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
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_s_300e_battery_1024.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_s_300e_battery_1024.yml
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_s_300e_lvjian1.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_s_300e_lvjian1.yml
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_s_300e_lvjian_1024.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_s_300e_lvjian_1024.yml
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_s_300e_renche.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_s_300e_renche.yml
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_s_300e_renche_1024.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_s_300e_renche_1024.yml
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_x_300e_battery.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_x_300e_battery.yml
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_x_300e_battery_1024.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_x_300e_battery_1024.yml
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_x_300e_lvjian1.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_x_300e_lvjian1.yml
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_x_300e_lvjian1_1024.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_x_300e_lvjian1_1024.yml
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_x_300e_renche.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_x_300e_renche.yml
--- a/ppsmrt/configs/ppyoloe/ppyoloe_crn_x_300e_renche_1024.yml
+++ b/ppsmrt/configs/ppyoloe/ppyoloe_crn_x_300e_renche_1024.yml
--- a/ppsmrt/configs/rcnn/cascade_rcnn_r50_vd_fpn_ssld_2x_1500_battery.yml
+++ b/ppsmrt/configs/rcnn/cascade_rcnn_r50_vd_fpn_ssld_2x_1500_battery.yml
--- a/ppsmrt/configs/rcnn/cascade_rcnn_r50_vd_fpn_ssld_2x_1500_lvjian1.yml
+++ b/ppsmrt/configs/rcnn/cascade_rcnn_r50_vd_fpn_ssld_2x_1500_lvjian1.yml
--- a/ppsmrt/configs/rcnn/cascade_rcnn_r50_vd_fpn_ssld_2x_1500_renche.yml
+++ b/ppsmrt/configs/rcnn/cascade_rcnn_r50_vd_fpn_ssld_2x_1500_renche.yml
--- a/ppsmrt/configs/rcnn/cascade_rcnn_r50_vd_fpn_ssld_2x_800_battery.yml
+++ b/ppsmrt/configs/rcnn/cascade_rcnn_r50_vd_fpn_ssld_2x_800_battery.yml
--- a/ppsmrt/configs/rcnn/cascade_rcnn_r50_vd_fpn_ssld_2x_800_lvjian1.yml
+++ b/ppsmrt/configs/rcnn/cascade_rcnn_r50_vd_fpn_ssld_2x_800_lvjian1.yml
--- a/ppsmrt/configs/rcnn/cascade_rcnn_r50_vd_fpn_ssld_2x_800_renche.yml
+++ b/ppsmrt/configs/rcnn/cascade_rcnn_r50_vd_fpn_ssld_2x_800_renche.yml
--- a/ppsmrt/configs/rcnn/faster_rcnn_r101_vd_fpn_ssld_2x_1500_battery.yml
+++ b/ppsmrt/configs/rcnn/faster_rcnn_r101_vd_fpn_ssld_2x_1500_battery.yml
--- a/ppsmrt/configs/rcnn/faster_rcnn_r101_vd_fpn_ssld_2x_1500_lvjian1.yml
+++ b/ppsmrt/configs/rcnn/faster_rcnn_r101_vd_fpn_ssld_2x_1500_lvjian1.yml
--- a/ppsmrt/configs/rcnn/faster_rcnn_r101_vd_fpn_ssld_2x_1500_renche.yml
+++ b/ppsmrt/configs/rcnn/faster_rcnn_r101_vd_fpn_ssld_2x_1500_renche.yml
--- a/ppsmrt/configs/rcnn/faster_rcnn_r101_vd_fpn_ssld_2x_800_battery.yml
+++ b/ppsmrt/configs/rcnn/faster_rcnn_r101_vd_fpn_ssld_2x_800_battery.yml
--- a/ppsmrt/configs/rcnn/faster_rcnn_r101_vd_fpn_ssld_2x_800_lvjian1.yml
+++ b/ppsmrt/configs/rcnn/faster_rcnn_r101_vd_fpn_ssld_2x_800_lvjian1.yml
--- a/ppsmrt/configs/rcnn/faster_rcnn_r101_vd_fpn_ssld_2x_800_renche.yml
+++ b/ppsmrt/configs/rcnn/faster_rcnn_r101_vd_fpn_ssld_2x_800_renche.yml
--- a/ppsmrt/configs/rcnn/faster_rcnn_r50_vd_fpn_ssld_2x_1500_battery.yml
+++ b/ppsmrt/configs/rcnn/faster_rcnn_r50_vd_fpn_ssld_2x_1500_battery.yml
--- a/ppsmrt/configs/rcnn/faster_rcnn_r50_vd_fpn_ssld_2x_1500_lvjian1.yml
+++ b/ppsmrt/configs/rcnn/faster_rcnn_r50_vd_fpn_ssld_2x_1500_lvjian1.yml
--- a/ppsmrt/configs/rcnn/faster_rcnn_r50_vd_fpn_ssld_2x_1500_renche.yml
+++ b/ppsmrt/configs/rcnn/faster_rcnn_r50_vd_fpn_ssld_2x_1500_renche.yml
--- a/ppsmrt/configs/rcnn/faster_rcnn_r50_vd_fpn_ssld_2x_800_battery.yml
+++ b/ppsmrt/configs/rcnn/faster_rcnn_r50_vd_fpn_ssld_2x_800_battery.yml
--- a/ppsmrt/configs/rcnn/faster_rcnn_r50_vd_fpn_ssld_2x_800_lvjian1.yml
+++ b/ppsmrt/configs/rcnn/faster_rcnn_r50_vd_fpn_ssld_2x_800_lvjian1.yml
--- a/ppsmrt/configs/rcnn/faster_rcnn_r50_vd_fpn_ssld_2x_800_renche.yml
+++ b/ppsmrt/configs/rcnn/faster_rcnn_r50_vd_fpn_ssld_2x_800_renche.yml
--- a/ppsmrt/images/Board_diaojiao_1591.png
+++ b/ppsmrt/images/Board_diaojiao_1591.png
--- a/ppsmrt/images/UpCoa_liewen_163.png
+++ b/ppsmrt/images/UpCoa_liewen_163.png
--- a/ppsmrt/images/lvjian1_0.jpg
+++ b/ppsmrt/images/lvjian1_0.jpg
--- a/ppsmrt/images/lvjian1_10.jpg
+++ b/ppsmrt/images/lvjian1_10.jpg
--- a/ppsmrt/images/renche_00002.jpg
+++ b/ppsmrt/images/renche_00002.jpg
--- a/ppsmrt/images/renche_00204.jpg
+++ b/ppsmrt/images/renche_00204.jpg