1、主要参考

（1）大佬教程

基于PaddleDetection的PCB瑕疵检测_AI Studio的博客-CSDN博客

（2）blibli视频

253-14_PCB电路板缺陷检测_项目配置文件_dec_哔哩哔哩_bilibili

（3）coco数据集说明

『深度应用』目标检测coco数据集格式介绍 - 腾讯云开发者社区-腾讯云

2、模型的下载

（1）下载地址

北京大学智能机器人开放实验室

 印刷电路板（PCB）瑕疵数据集。它是一个公共合成PCB数据集，包含1386张图像，具有6种缺陷（漏孔、鼠咬、开路、短路、杂散、杂铜），用于图像检测、分类和配准任务

3、模型的特征查看

（1）查看各个缺陷的分布情况（类别数是否均匀，如果不均匀可以考虑调整loss的计算方法）

（2）查看锚框的长宽比，看看都是什么形状的

（3）看看锚框的大小（和原图大小的比值，看看是不是都是小物体）

（4）文件pcb_dataset_analysis.py如下

import json
from collections import defaultdict
import matplotlib.pyplot as plt
%matplotlib inline

with open("/home/aistudio/work/PCB_DATASET/Annotations/train.json") as f:
    data = json.load(f)

imgs = {}
for img in data['images']:
    imgs[img['id']] = {
        'h': img['height'],
        'w': img['width'],
        'area': img['height'] * img['width'],
    }

hw_ratios = []
area_ratios = []
label_count = defaultdict(int)
for anno in data['annotations']:
    hw_ratios.append(anno['bbox'][3]/anno['bbox'][2])
    area_ratios.append(anno['area']/imgs[anno['image_id']]['area'])
    label_count[anno['category_id']] += 1

#查看各个缺陷的分布情况（类别数是否均匀，如果不均匀可以考虑调整loss的计算方法）
print( label_count, len(data['annotations']) / len(data['images']) )

#查看锚框的长宽比，看看都是什么形状的
plt.hist(hw_ratios, bins=100, range=[0, 2])
plt.show()

#看看锚框的大小（和原图大小的比值，看看是不是都是小物体）
plt.hist(area_ratios, bins=100, range=[0, 0.005])
plt.show()

4、下载paddle的object项目

4.1 下载

(1)paddle官网

飞桨PaddlePaddle-源于产业实践的开源深度学习平台

（2）下载指定PaddleDetection

 (3)对应的github地址如下

https://github.com/PaddlePaddle/PaddleDetection

4.2安装相关环境

#（1）在对应目录下
conda activate chenpaddle_cp310

#（2）安装相关环境
pip install -r requirements.txt 

5、配置和训练

？？？

6、基于PP-YOLOE+的PCB缺陷检测

（1）以下引用参考来自paddle官网

基于PP-YOLOE+的PCB缺陷检测 - 飞桨AI Studio基于PP-YOLOE+实现的PCB缺陷检测方案全流程实战，覆盖训练、调优、部署等 - 飞桨AI Studiohttps://aistudio.baidu.com/aistudio/projectdetail/4670836

黑盒教程真有意思 ：（

（2）其中ppyoloe_plus_crn_m_80e_obj365_pretrained_pcb.yml内容如下

_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

（3）'./_base_/pcb_detection.yml',文件如下

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'

（4）'../../runtime.yml',文件内容如下

use_gpu: true
use_xpu: false
log_iter: 20
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.
  fuse_conv_bn: False

（5） '../_base_/optimizer_80e.yml',内容如下

epoch: 80

LearningRate:
  base_lr: 0.001
  schedulers:
    - !CosineDecay
      max_epochs: 96
    - !LinearWarmup
      start_factor: 0.
      epochs: 5

OptimizerBuilder:
  optimizer:
    momentum: 0.9
    type: Momentum
  regularizer:
    factor: 0.0005
    type: L2

(6)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

(7)ppyoloe_plus_crn.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