siton bug

configs/ppyoloe/_base_/ppyoloe_plus_crn.yml

+architecture: YOLOv3
+norm_type: sync_bn
+use_ema: true
+ema_decay: 0.9998
+ema_black_list: ['proj_conv.weight']
+custom_black_list: ['reduce_mean']
+
+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
+  use_alpha: 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: 30
+  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: 300
+    score_threshold: 0.01
+    nms_threshold: 0.7

configs/ppyoloe/_base_/ppyoloe_plus_crn_tiny_auxhead.yml

+architecture: PPYOLOEWithAuxHead
+norm_type: sync_bn
+use_ema: true
+ema_decay: 0.9998
+ema_black_list: ['proj_conv.weight']
+custom_black_list: ['reduce_mean']
+
+PPYOLOEWithAuxHead:
+  backbone: CSPResNet
+  neck: CustomCSPPAN
+  yolo_head: PPYOLOEHead
+  aux_head: SimpleConvHead
+  post_process: ~
+
+CSPResNet:
+  layers: [3, 6, 6, 3]
+  channels: [64, 128, 256, 512, 1024]
+  return_idx: [1, 2, 3]
+  use_large_stem: True
+  use_alpha: True
+
+CustomCSPPAN:
+  out_channels: [384, 384, 384]
+  stage_num: 1
+  block_num: 3
+  act: 'swish'
+  spp: true
+
+SimpleConvHead:
+  feat_in: 288
+  feat_out: 288
+  num_convs: 1
+  fpn_strides: [32, 16, 8]
+  norm_type: 'gn'
+  act: 'LeakyReLU'
+  reg_max: 16
+
+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}
+  attn_conv: 'repvgg' #
+  static_assigner:
+    name: ATSSAssigner
+    topk: 9
+  assigner:
+    name: TaskAlignedAssigner
+    topk: 13
+    alpha: 1.0
+    beta: 6.0
+    is_close_gt: True #
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 300
+    score_threshold: 0.01
+    nms_threshold: 0.7

configs/ppyoloe/_base_/ppyoloe_plus_reader.yml

+worker_num: 4
+eval_height: &eval_height 640
+eval_width: &eval_width 640
+eval_size: &eval_size [*eval_height, *eval_width]
+
+TrainReader:
+  sample_transforms:
+    - Decode: {}
+    - RandomDistort: {}
+    - RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
+    - RandomCrop: {}
+    - RandomFlip: {}
+  batch_transforms:
+    - BatchRandomResize: {target_size: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608, 640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - 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., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - 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., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 1

configs/ppyoloe/_base_/ppyoloe_plus_reader_320.yml

+worker_num: 4
+eval_height: &eval_height 320
+eval_width: &eval_width 320
+eval_size: &eval_size [*eval_height, *eval_width]
+
+TrainReader:
+  sample_transforms:
+    - Decode: {}
+    - RandomDistort: {}
+    - RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
+    - RandomCrop: {}
+    - RandomFlip: {}
+  batch_transforms:
+    - BatchRandomResize: {target_size: [224, 256, 288, 320, 352, 384, 416, 448, 480, 512, 544], random_size: True, random_interp: True, keep_ratio: False}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - 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., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - 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., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 1

configs/ppyoloe/_base_/ppyoloe_reader.yml

+worker_num: 4
+eval_height: &eval_height 640
+eval_width: &eval_width 640
+eval_size: &eval_size [*eval_height, *eval_width]
+
+TrainReader:
+  sample_transforms:
+    - Decode: {}
+    - RandomDistort: {}
+    - RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
+    - RandomCrop: {}
+    - RandomFlip: {}
+  batch_transforms:
+    - BatchRandomResize: {target_size: [320, 352, 384, 416, 448, 480, 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: 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

configs/ppyoloe/application/README.md

+# PP-YOLOE+ 下游任务
+
+我们验证了PP-YOLOE+模型强大的泛化能力，在农业、低光、工业等不同场景下游任务检测效果稳定提升!
+
+农业数据集采用[Embrapa WGISD](https://github.com/thsant/wgisd)，该数据集用于葡萄栽培中基于图像的监测和现场机器人技术，提供了来自5种不同葡萄品种的实地实例，
+处理后的COCO格式，包含图片训练集242张，测试集58张，5个类别，[Embrapa WGISD COCO格式下载](https://bj.bcebos.com/v1/paddledet/data/wgisd.zip)；
+
+低光数据集使用[ExDark](https://github.com/cs-chan/Exclusively-Dark-Image-Dataset/tree/master/Dataset)，该数据集是一个专门在低光照环境下拍摄出针对低光目标检测的数据集，包括从极低光环境到暮光环境等10种不同光照条件下的图片，
+处理后的COCO格式，包含图片训练集5891张，测试集1472张，12个类别，[ExDark COCO格式下载](https://bj.bcebos.com/v1/paddledet/data/Exdark.zip)；
+
+工业数据集使用[PKU-Market-PCB](https://robotics.pkusz.edu.cn/resources/dataset/)，该数据集用于印刷电路板（PCB）的瑕疵检测，提供了6种常见的PCB缺陷，
+处理后的COCO格式，包含图片训练集555张，测试集138张，6个类别，[PKU-Market-PCB COCO格式下载](https://bj.bcebos.com/v1/paddledet/data/PCB_coco.zip)。
+
+商超数据集[SKU110k](https://github.com/eg4000/SKU110K_CVPR19)是商品超市场景下的密集目标检测数据集，包含11,762张图片和超过170个实例。其中包括8,233张用于训练的图像、588张用于验证的图像和2,941张用于测试的图像。
+
+
+## 实验结果：
+
+|    模型  |       数据集     | mAP<sup>val<br>0.5:0.95 |  下载链接  | 配置文件 |
+|:---------|:---------------:|:-----------------------:|:---------:| :-----: |
+|PP-YOLOE_m|   Embrapa WGISD  |  52.7 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_80e_wgisd.pdparams) | [配置文件](./ppyoloe_crn_m_80e_wgisd.yml) |
+|PP-YOLOE+_m<br>(obj365_pretrained)|   Embrapa WGISD  |  60.8(+8.1) | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_m_80e_obj365_pretrained_wgisd.pdparams) | [配置文件](./ppyoloe_plus_crn_m_80e_obj365_pretrained_wgisd.yml) |
+|PP-YOLOE+_m<br>(coco_pretrained)|   Embrapa WGISD  |  59.7(+7.0) | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_m_80e_coco_pretrained_wgisd.pdparams) | [配置文件](./ppyoloe_plus_crn_m_80e_coco_pretrained_wgisd.yml) |
+|PP-YOLOE_m|      ExDark      |  56.4 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_80e_exdark.pdparams) | [配置文件](./ppyoloe_crn_m_80e_exdark.yml) |
+|PP-YOLOE+_m<br>(obj365_pretrained)|   ExDark  |  57.7(+1.3) | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_m_80e_obj365_pretrained_exdark.pdparams) | [配置文件](./ppyoloe_plus_crn_m_80e_obj365_pretrained_exdark.yml) |
+|PP-YOLOE+_m<br>(coco_pretrained)|   ExDark  |  58.1(+1.7) | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_m_80e_coco_pretrained_exdark.pdparams) | [配置文件](./ppyoloe_plus_crn_m_80e_coco_pretrained_exdark.yml) |
+|PP-YOLOE_m|      PKU-Market-PCB      |  50.8 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_80e_pcb.pdparams) | [配置文件](./ppyoloe_crn_m_80e_pcb.yml) |
+|PP-YOLOE+_m<br>(obj365_pretrained)|   PKU-Market-PCB  |  52.7(+1.9) | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_m_80e_obj365_pretrained_pcb.pdparams) | [配置文件](./ppyoloe_plus_crn_m_80e_obj365_pretrained_pcb.yml) |
+|PP-YOLOE+_m<br>(coco_pretrained)|   PKU-Market-PCB  |  52.4(+1.6) | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_m_80e_coco_pretrained_pcb.pdparams) | [配置文件](./ppyoloe_plus_crn_m_80e_coco_pretrained_pcb.yml) |
+
+**注意:**
+- PP-YOLOE模型训练过程中使用8 GPUs进行训练，如果**GPU卡数**或者**batch size**发生了改变，你需要按照公式 **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)** 调整学习率。
+- 具体使用教程请参考[ppyoloe](../ppyoloe#getting-start)。  
+
+
+## SKU110k Model ZOO
+|     Model      | Epoch | GPU number | images/GPU |  backbone  | input shape | Box AP<sup>val<br>0.5:0.95 (maxDets=300) | Box AP<sup>test<br>0.5:0.95 (maxDets=300) | download | config |
+|:--------------:|:-----:|:-------:|:----------:|:----------:| :-------:|:-------------------------:|:---------------------------:|:---------:|:------:|
+| PP-YOLOE+_s | 80 | 8 | 8 | cspresnet-s | 960 | 57.4 | 58.8 | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_sku110k.pdparams) | [config](./ppyoloe_plus_crn_s_80e_sku110k.yml) |
+| PP-YOLOE+_m | 80 | 8 | 8 | cspresnet-m | 960 | 58.2 | 59.7 | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_m_80e_sku110k.pdparams) | [config](./ppyoloe_plus_crn_m_80e_sku110k.yml) |
+| PP-YOLOE+_l | 80 | 8 | 4 | cspresnet-l | 960 | 58.8 | 60.2 | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_sku110k.pdparams) | [config](./ppyoloe_plus_crn_l_80e_sku110k.yml) |
+| PP-YOLOE+_x | 80 | 8 | 4 | cspresnet-x | 960 | 59.0 | 60.3 | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_x_80e_sku110k.pdparams) | [config](./ppyoloe_plus_crn_x_80e_sku110k.yml) |
+
+
+**注意:**
+- SKU110k系列模型训练过程中使用8 GPUs进行训练，如果**GPU卡数**或者**batch size**发生了改变，你需要按照公式 **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)** 调整学习率。
+- SKU110k数据集使用**maxDets=300**的mAP值作为评估指标。
+- 具体使用教程请参考[ppyoloe](../ppyoloe#getting-start)。
+
+
+## 引用
+```
+@inproceedings{goldman2019dense,
+ author    = {Eran Goldman and Roei Herzig and Aviv Eisenschtat and Jacob Goldberger and Tal Hassner},
+ title     = {Precise Detection in Densely Packed Scenes},
+ booktitle = {Proc. Conf. Comput. Vision Pattern Recognition (CVPR)},
+ year      = {2019}
+}
+
+@article{Exdark,
+title={Getting to Know Low-light Images with The Exclusively Dark Dataset},
+author={Loh, Yuen Peng and Chan, Chee Seng},
+journal={Computer Vision and Image Understanding},
+volume={178},
+pages={30-42},
+year={2019},
+doi={https://doi.org/10.1016/j.cviu.2018.10.010}
+}
+```

configs/ppyoloe/application/_base_/exdark_detection.yml

+metric: COCO
+num_classes: 12
+
+TrainDataset:
+  !COCODataSet
+    image_dir: images
+    anno_path: coco_annotations/train.json
+    dataset_dir: dataset/Exdark/
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: images
+    anno_path: coco_annotations/val.json
+    dataset_dir: dataset/Exdark/
+
+TestDataset:
+  !ImageFolder
+    anno_path: coco_annotations/val.json # also support txt (like VOC's label_list.txt)
+    dataset_dir: dataset/Exdark/ # if set, anno_path will be 'dataset_dir/anno_path'

configs/ppyoloe/application/_base_/pcb_detection.yml

+metric: COCO
+num_classes: 6
+
+TrainDataset:
+  !COCODataSet
+    image_dir: images
+    anno_path: pcb_cocoanno/train.json
+    dataset_dir: dataset/PCB_coco/
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: images
+    anno_path: pcb_cocoanno/val.json
+    dataset_dir: dataset/PCB_coco/
+
+TestDataset:
+  !ImageFolder
+    anno_path: pcb_cocoanno/val.json # also support txt (like VOC's label_list.txt)
+    dataset_dir: dataset/PCB_coco/ # if set, anno_path will be 'dataset_dir/anno_path'

configs/ppyoloe/application/_base_/sku110k.yml

+metric: COCO
+num_classes: 1
+
+TrainDataset:
+  !COCODataSet
+    image_dir: images
+    anno_path: annotations/annotations_train.json
+    dataset_dir: dataset/SKU110K_fixed
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: images
+    anno_path: annotations/annotations_val.json
+    dataset_dir: dataset/SKU110K_fixed
+    allow_empty: true
+
+TestDataset:
+  !ImageFolder
+    anno_path: annotations/annotations_test.json
+    dataset_dir: dataset/SKU110K_fixed

configs/ppyoloe/application/_base_/wgisd_detection.yml

+metric: COCO
+num_classes: 5
+
+TrainDataset:
+  !COCODataSet
+    image_dir: data
+    anno_path: coco_annotations/new_train_bbox_instances.json
+    dataset_dir: dataset/wgisd/
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: data
+    anno_path: coco_annotations/new_test_bbox_instances.json
+    dataset_dir: dataset/wgisd/
+
+TestDataset:
+  !ImageFolder
+    anno_path: coco_annotations/new_test_bbox_instances.json # also support txt (like VOC's label_list.txt)
+    dataset_dir: dataset/wgisd/ # if set, anno_path will be 'dataset_dir/anno_path'

configs/ppyoloe/application/ppyoloe_crn_m_80e_exdark.yml

+_BASE_: [
+  './_base_/exdark_detection.yml',
+  '../../runtime.yml',
+  '../_base_/optimizer_80e.yml',
+  '../_base_/ppyoloe_crn.yml',
+  '../_base_/ppyoloe_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_crn_m_80e_exdark/model_final
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams
+depth_mult: 0.67
+width_mult: 0.75

configs/ppyoloe/application/ppyoloe_crn_m_80e_pcb.yml

+_BASE_: [
+  './_base_/pcb_detection.yml',
+  '../../runtime.yml',
+  '../_base_/optimizer_80e.yml',
+  '../_base_/ppyoloe_crn.yml',
+  '../_base_/ppyoloe_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_crn_m_80e_pcb/model_final
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams
+depth_mult: 0.67
+width_mult: 0.75

configs/ppyoloe/application/ppyoloe_crn_m_80e_wgisd.yml

+_BASE_: [
+  './_base_/wgisd_detection.yml',
+  '../../runtime.yml',
+  '../_base_/optimizer_80e.yml',
+  '../_base_/ppyoloe_crn.yml',
+  '../_base_/ppyoloe_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_crn_m_80e_wgisd/model_final
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams
+depth_mult: 0.67
+width_mult: 0.75

configs/ppyoloe/application/ppyoloe_plus_crn_l_80e_sku110k.yml

+_BASE_: [
+  './_base_/sku110k.yml',
+  '../../runtime.yml'
+]
+
+log_iter: 10
+snapshot_epoch: 20
+weights: output/ppyoloe_plus_crn_s_80e_coco/model_final
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_l_obj365_pretrained.pdparams
+depth_mult: 1.0
+width_mult: 1.0
+
+
+# arch
+architecture: YOLOv3
+norm_type: sync_bn
+use_ema: true
+ema_decay: 0.9998
+custom_black_list: ['reduce_mean']
+
+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
+  use_alpha: True
+
+CustomCSPPAN:
+  out_channels: [768, 384, 192]
+  stage_num: 1
+  block_num: 3
+  act: 'swish'
+  spp: true
+  use_alpha: True
+
+PPYOLOEHead:
+  fpn_strides: [32, 16, 8]
+  grid_cell_scale: 5.0
+  grid_cell_offset: 0.5
+  static_assigner_epoch: -1
+  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: 3000
+    keep_top_k: 1000
+    score_threshold: 0.01
+    nms_threshold: 0.7
+
+
+# reader
+worker_num: 8
+eval_height: &eval_height 960
+eval_width: &eval_width 960
+eval_size: &eval_size [*eval_height, *eval_width]
+
+TrainReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [3000, 1800], keep_ratio: True, interp: 2}
+    - RandomDistort: {}
+    - RandomCrop: {}
+    - RandomFlip: {}
+  batch_transforms:
+    - BatchRandomResize: {target_size: [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800, 832, 864, 896, 928, 960, 992, 1024, 1056, 1088, 1120, 1152], random_size: True, random_interp: True, keep_ratio: False}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - 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., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - 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., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 1
+
+
+# optimizer
+epoch: 80
+
+LearningRate:
+  base_lr: 0.002
+  schedulers:
+    - !CosineDecay
+      max_epochs: 96
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 5
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005
+    type: L2

configs/ppyoloe/application/ppyoloe_plus_crn_m_80e_coco_pretrained_exdark.yml

+_BASE_: [
+  './_base_/exdark_detection.yml',
+  '../../runtime.yml',
+  '../_base_/optimizer_80e.yml',
+  '../_base_/ppyoloe_plus_crn.yml',
+  '../_base_/ppyoloe_plus_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_m_80e_coco_pretrained_exdark/model_final
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/ppyoloe_plus_crn_m_80e_coco.pdparams
+depth_mult: 0.67
+width_mult: 0.75

configs/ppyoloe/application/ppyoloe_plus_crn_m_80e_coco_pretrained_pcb.yml

+_BASE_: [
+  './_base_/pcb_detection.yml',
+  '../../runtime.yml',
+  '../_base_/optimizer_80e.yml',
+  '../_base_/ppyoloe_plus_crn.yml',
+  '../_base_/ppyoloe_plus_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_m_80e_coco_pretrained_pcb/model_final
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/ppyoloe_plus_crn_m_80e_coco.pdparams
+depth_mult: 0.67
+width_mult: 0.75

configs/ppyoloe/application/ppyoloe_plus_crn_m_80e_coco_pretrained_wgisd.yml

+_BASE_: [
+  './_base_/wgisd_detection.yml',
+  '../../runtime.yml',
+  '../_base_/optimizer_80e.yml',
+  '../_base_/ppyoloe_plus_crn.yml',
+  '../_base_/ppyoloe_plus_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_m_80e_coco_pretrained_wgisd/model_final
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/ppyoloe_plus_crn_m_80e_coco.pdparams
+depth_mult: 0.67
+width_mult: 0.75

configs/ppyoloe/application/ppyoloe_plus_crn_m_80e_obj365_pretrained_exdark.yml

+_BASE_: [
+  './_base_/exdark_detection.yml',
+  '../../runtime.yml',
+  '../_base_/optimizer_80e.yml',
+  '../_base_/ppyoloe_plus_crn.yml',
+  '../_base_/ppyoloe_plus_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_m_80e_obj365_pretrained_exdark/model_final
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_m_obj365_pretrained.pdparams
+depth_mult: 0.67
+width_mult: 0.75

configs/ppyoloe/application/ppyoloe_plus_crn_m_80e_obj365_pretrained_pcb.yml

+_BASE_: [
+  './_base_/pcb_detection.yml',
+  '../../runtime.yml',
+  '../_base_/optimizer_80e.yml',
+  '../_base_/ppyoloe_plus_crn.yml',
+  '../_base_/ppyoloe_plus_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_m_80e_obj365_pretrained_pcb/model_final
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_m_obj365_pretrained.pdparams
+depth_mult: 0.67
+width_mult: 0.75

configs/ppyoloe/application/ppyoloe_plus_crn_m_80e_obj365_pretrained_wgisd.yml

+_BASE_: [
+  './_base_/wgisd_detection.yml',
+  '../../runtime.yml',
+  '../_base_/optimizer_80e.yml',
+  '../_base_/ppyoloe_plus_crn.yml',
+  '../_base_/ppyoloe_plus_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_m_80e_obj365_pretrained_wgisd/model_final
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_m_obj365_pretrained.pdparams
+depth_mult: 0.67
+width_mult: 0.75