利用RCNN完成印刷电路板缺陷检测的系列算法。采用评价方法IoU=0.5，area=all的mAP作为评价指标，得分=mAP * 100，范围[0，100]。

首先印刷电路板（PCB）缺陷数据集和PaddleDetection代码解压到~/work/目录中：

# 解压数据集 !tar -xf /home/aistudio/data/data52914/PCB_DATASET.tar -C ~/work/ # 解压PaddleDetection源码 !unzip /home/aistudio/data/data103033/PaddleDetection-release-2.1.zip -d ~/work/ 

  /home/aistudio/work/PaddleDetection-release-2.1/static/docs/advanced_tutorials/slim/prune/SENSITIVE.md -> ../../../../slim/sensitive/README.md 

训练前需要安装PaddleDetection执行下列命令即可安装所需的依赖包：

%cd ~/work/PaddleDetection-release-2.1/ ! pip install -r requirements.txt ! pip install pycocotools 

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在调整配置之前，请首先对数据有一个大概的了解。由于是个小数据，这里只简单分析了几项跟配置息息相关的内容。

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

label_count, len(data['annotations']) / len(data['images'])

(defaultdict(int, {3: 399, 5: 416, 2: 435, 6: 447, 4: 412, 1: 418}),
 4.261382799325464)

本实验用到的数据配置文件在work/PaddleDetection-release-2.1/configs/datasets/coco_detection.yml中，具体修改后的内容如下：

metric: COCO
num_classes: 6 # 不包括背景

TrainDataset:
  !COCODataSet
    image_dir: images
    anno_path: Annotations/train.json
    dataset_dir: /home/aistudio/work/PCB_DATASET
    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']

EvalDataset:
  !COCODataSet
    image_dir: images
    anno_path: Annotations/val.json
    dataset_dir: /home/aistudio/work/PCB_DATASET

TestDataset:
  !ImageFolder
    anno_path: /home/aistudio/work/PCB_DATASET/Annotations/val.json

1. 预训练模型：
   建议直接到PaddleDection的MODEL_ZOO文档中找相关的模型参数。预训练能大大缩短收敛时间。本实验预训练模型参数为https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_cos_pretrained.pdparams/

2. FPN通道数：
   本实验FPN模块通道数默认是256通道，训练时间较长，为了压缩这个训练时间，我将通道数缩减为了64。具体修改为work/PaddleDetection-release-2.1/configs/cascade_rcnn/_base_/cascade_rcnn_r50_fpn.yml文件中FPN: out_channel: 64和RPNHead: rpn_target_assign: batch_size_per_im: 64。

3. 学习率和衰减：
   学习率按照默认的学习率除以8，即0.000125。衰减轮数milestones一般配置在max_iters的2/3和8/9处，尽量靠后。具体对应的配置文件为work/PaddleDetection-release-2.1/configs/cascade_rcnn/_base_/optimizer_1x.yml，修改LearningRate: base_lr: 0.00125。

4. 数据增强：
   这里使用的数据增强方式有RandomResize、RandomFlip、NormalizeImage。

# 训练脚本
!CUDA_VISIBLE_DEVICES=0 
!python3.7 -u tools/train.py -c configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.yml --eval 

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import MutableMapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Iterable, Mapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Sized
/home/aistudio/work/PaddleDetection-release-2.1/ppdet/modeling/ops.py:542: DeprecationWarning: invalid escape sequence \_
  """
/home/aistudio/work/PaddleDetection-release-2.1/ppdet/modeling/ops.py:1375: DeprecationWarning: invalid escape sequence \l
  """
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. 
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  if data.dtype == np.object:
loading annotations into memory...
Done (t=0.09s)
creating index...
index created!
W0913 19:13:46.472611   674 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0913 19:13:46.478024   674 device_context.cc:422] device: 0, cuDNN Version: 7.6.
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. 
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  if data.dtype == np.object:
[09/13 19:13:49] ppdet.utils.download INFO: Downloading ResNet50_cos_pretrained.pdparams from https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_cos_pretrained.pdparams
100%|██████████████████████████████████| 92063/92063 [00:01<00:00, 65819.16KB/s]
[09/13 19:13:51] ppdet.utils.checkpoint INFO: Finish loading model weights: /home/aistudio/.cache/paddle/weights/ResNet50_cos_pretrained.pdparams
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[09/13 19:13:51] ppdet.engine INFO: Epoch: [0] [  0/593] learning_rate: 0.000001 loss_rpn_cls: 0.697280 loss_rpn_reg: 0.031664 loss_bbox_cls_stage0: 0.735053 loss_bbox_reg_stage0: 0.000138 loss_bbox_cls_stage1: 0.648889 loss_bbox_reg_stage1: 0.000265 loss_bbox_cls_stage2: 0.642100 loss_bbox_reg_stage2: 0.000457 loss: 2.755846 eta: 0:18:58 batch_cost: 0.1600 data_cost: 0.0004 ips: 6.2487 images/s
[09/13 19:13:54] ppdet.engine INFO: Epoch: [0] [ 20/593] learning_rate: 0.000026 loss_rpn_cls: 0.696808 loss_rpn_reg: 0.056438 loss_bbox_cls_stage0: 0.665093 loss_bbox_reg_stage0: 0.000073 loss_bbox_cls_stage1: 0.638169 loss_bbox_reg_stage1: 0.000155 loss_bbox_cls_stage2: 0.628870 loss_bbox_reg_stage2: 0.000236 loss: 2.683045 eta: 0:16:32 batch_cost: 0.1388 data_cost: 0.0002 ips: 7.2052 images/s
[09/13 19:13:57] ppdet.engine INFO: Epoch: [0] [ 40/593] learning_rate: 0.000051 loss_rpn_cls: 0.696619 loss_rpn_reg: 0.047299 loss_bbox_cls_stage0: 0.557011 loss_bbox_reg_stage0: 0.000094 loss_bbox_cls_stage1: 0.515861 loss_bbox_reg_stage1: 0.000205 loss_bbox_cls_stage2: 0.518766 loss_bbox_reg_stage2: 0.000299 loss: 2.346453 eta: 0:15:57 batch_cost: 0.1305 data_cost: 0.0002 ips: 7.6640 images/s
[09/13 19:13:59] ppdet.engine INFO: Epoch: [0] [ 60/593] learning_rate: 0.000076 loss_rpn_cls: 0.697658 loss_rpn_reg: 0.042460 loss_bbox_cls_stage0: 0.037034 loss_bbox_reg_stage0: 0.000147 loss_bbox_cls_stage1: 0.030499 loss_bbox_reg_stage1: 0.000318 loss_bbox_cls_stage2: 0.030750 loss_bbox_reg_stage2: 0.000649 loss: 0.873187 eta: 0:15:40 batch_cost: 0.1293 data_cost: 0.0002 ips: 7.7339 images/s
[09/13 19:14:02] ppdet.engine INFO: Epoch: [0] [ 80/593] learning_rate: 0.000101 loss_rpn_cls: 0.683219 loss_rpn_reg: 0.050110 loss_bbox_cls_stage0: 0.019129 loss_bbox_reg_stage0: 0.000185 loss_bbox_cls_stage1: 0.019823 loss_bbox_reg_stage1: 0.000433 loss_bbox_cls_stage2: 0.019747 loss_bbox_reg_stage2: 0.000714 loss: 0.801777 eta: 0:15:38 batch_cost: 0.1335 data_cost: 0.0002 ips: 7.4930 images/s
[09/13 19:14:05] ppdet.engine INFO: Epoch: [0] [100/593] learning_rate: 0.000126 loss_rpn_cls: 0.641044 loss_rpn_reg: 0.057100 loss_bbox_cls_stage0: 0.050942 loss_bbox_reg_stage0: 0.000329 loss_bbox_cls_stage1: 0.047029 loss_bbox_reg_stage1: 0.000560 loss_bbox_cls_stage2: 0.046340 loss_bbox_reg_stage2: 0.000886 loss: 0.839477 eta: 0:15:36 batch_cost: 0.1337 data_cost: 0.0002 ips: 7.4782 images/s
[09/13 19:14:07] ppdet.engine INFO: Epoch: [0] [120/593] learning_rate: 0.000151 loss_rpn_cls: 0.502041 loss_rpn_reg: 0.051631 loss_bbox_cls_stage0: 0.058352 loss_bbox_reg_stage0: 0.000240 loss_bbox_cls_stage1: 0.057758 loss_bbox_reg_stage1: 0.000567 loss_bbox_cls_stage2: 0.057330 loss_bbox_reg_stage2: 0.000826 loss: 0.730823 eta: 0:15:38 batch_cost: 0.1376 data_cost: 0.0002 ips: 7.2692 images/s
[09/13 19:14:10] ppdet.engine INFO: Epoch: [0] [140/593] learning_rate: 0.000176 loss_rpn_cls: 0.258168 loss_rpn_reg: 0.050298 loss_bbox_cls_stage0: 0.062788 loss_bbox_reg_stage0: 0.000303 loss_bbox_cls_stage1: 0.065374 loss_bbox_reg_stage1: 0.000849 loss_bbox_cls_stage2: 0.060197 loss_bbox_reg_stage2: 0.001549 loss: 0.507770 eta: 0:15:31 batch_cost: 0.1304 data_cost: 0.0002 ips: 7.6689 images/s
[09/13 19:14:13] ppdet.engine INFO: Epoch: [0] [160/593] learning_rate: 0.000201 loss_rpn_cls: 0.193648 loss_rpn_reg: 0.052191 loss_bbox_cls_stage0: 0.040497 loss_bbox_reg_stage0: 0.000375 loss_bbox_cls_stage1: 0.044253 loss_bbox_reg_stage1: 0.001090 loss_bbox_cls_stage2: 0.040505 loss_bbox_reg_stage2: 0.001863 loss: 0.381232 eta: 0:15:29 batch_cost: 0.1342 data_cost: 0.0002 ips: 7.4498 images/s
[09/13 19:14:16] ppdet.engine INFO: Epoch: [0] [180/593] learning_rate: 0.000226 loss_rpn_cls: 0.174198 loss_rpn_reg: 0.044281 loss_bbox_cls_stage0: 0.026620 loss_bbox_reg_stage0: 0.000269 loss_bbox_cls_stage1: 0.029924 loss_bbox_reg_stage1: 0.000748 loss_bbox_cls_stage2: 0.027804 loss_bbox_reg_stage2: 0.001435 loss: 0.314368 eta: 0:15:28 batch_cost: 0.1355 data_cost: 0.0002 ips: 7.3812 images/s
[09/13 19:14:18] ppdet.engine INFO: Epoch: [0] [200/593] learning_rate: 0.000251 loss_rpn_cls: 0.160981 loss_rpn_reg: 0.046810 loss_bbox_cls_stage0: 0.028095 loss_bbox_reg_stage0: 0.000315 loss_bbox_cls_stage1: 0.026326 loss_bbox_reg_stage1: 0.000604 loss_bbox_cls_stage2: 0.026103 loss_bbox_reg_stage2: 0.001056 loss: 0.314169 eta: 0:15:25 batch_cost: 0.1339 data_cost: 0.0002 ips: 7.4677 images/s
[09/13 19:14:21] ppdet.engine INFO: Epoch: [0] [220/593] learning_rate: 0.000276 loss_rpn_cls: 0.162685 loss_rpn_reg: 0.045351 loss_bbox_cls_stage0: 0.023775 loss_bbox_reg_stage0: 0.000313 loss_bbox_cls_stage1: 0.023748 loss_bbox_reg_stage1: 0.000600 loss_bbox_cls_stage2: 0.023331 loss_bbox_reg_stage2: 0.001040 loss: 0.283127 eta: 0:15:22 batch_cost: 0.1326 data_cost: 0.0002 ips: 7.5394 images/s
[09/13 19:14:24] ppdet.engine INFO: Epoch: [0] [240/593] learning_rate: 0.000301 loss_rpn_cls: 0.166816 loss_rpn_reg: 0.047582 loss_bbox_cls_stage0: 0.027426 loss_bbox_reg_stage0: 0.003095 loss_bbox_cls_stage1: 0.025591 loss_bbox_reg_stage1: 0.000841 loss_bbox_cls_stage2: 0.024981 loss_bbox_reg_stage2: 0.001400 loss: 0.302733 eta: 0:15:21 batch_cost: 0.1371 data_cost: 0.0002 ips: 7.2933 images/s
[09/13 19:14:26] ppdet.engine INFO: Epoch: [0] [260/593] learning_rate: 0.000326 loss_rpn_cls: 0.147262 loss_rpn_reg: 0.044232 loss_bbox_cls_stage0: 0.025150 loss_bbox_reg_stage0: 0.001952 loss_bbox_cls_stage1: 0.023441 loss_bbox_reg_stage1: 0.000622 loss_bbox_cls_stage2: 0.023094 loss_bbox_reg_stage2: 0.001334 loss: 0.289993 eta: 0:15:18 batch_cost: 0.1331 data_cost: 0.0002 ips: 7.5108 images/s
[09/13 19:14:29] ppdet.engine INFO: Epoch: [0] [280/593] learning_rate: 0.000351 loss_rpn_cls: 0.166053 loss_rpn_reg: 0.055038 loss_bbox_cls_stage0: 0.029527 loss_bbox_reg_stage0: 0.004480 loss_bbox_cls_stage1: 0.026308 loss_bbox_reg_stage1: 0.001137 loss_bbox_cls_stage2: 0.024665 loss_bbox_reg_stage2: 0.001997 loss: 0.308855 eta: 0:15:14 batch_cost: 0.1311 data_cost: 0.0002 ips: 7.6258 images/s
[09/13 19:14:32] ppdet.engine INFO: Epoch: [0] [300/593] learning_rate: 0.000376 loss_rpn_cls: 0.151672 loss_rpn_reg: 0.049369 loss_bbox_cls_stage0: 0.024999 loss_bbox_reg_stage0: 0.000352 loss_bbox_cls_stage1: 0.024625 loss_bbox_reg_stage1: 0.001019 loss_bbox_cls_stage2: 0.023440 loss_bbox_reg_stage2: 0.001985 loss: 0.304269 eta: 0:15:10 batch_cost: 0.1319 data_cost: 0.0002 ips: 7.5840 images/s
^C
Traceback (most recent call last):
  File "tools/train.py", line 139, in 
    main()
  File "tools/train.py", line 135, in main
    run(FLAGS, cfg)
  File "tools/train.py", line 110, in run
    trainer.train(FLAGS.eval)
  File "/home/aistudio/work/PaddleDetection-release-2.1/ppdet/engine/trainer.py", line 306, in train
    outputs = model(data)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 902, in __call__
    outputs = self.forward(*inputs, **kwargs)
  File "/home/aistudio/work/PaddleDetection-release-2.1/ppdet/modeling/architectures/meta_arch.py", line 26, in forward
    out = self.get_loss()
  File "/home/aistudio/work/PaddleDetection-release-2.1/ppdet/modeling/architectures/cascade_rcnn.py", line 125, in get_loss
    rpn_loss, bbox_loss, mask_loss = self._forward()
  File "/home/aistudio/work/PaddleDetection-release-2.1/ppdet/modeling/architectures/cascade_rcnn.py", line 87, in _forward
    body_feats = self.backbone(self.inputs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 902, in __call__
    outputs = self.forward(*inputs, **kwargs)
  File "/home/aistudio/work/PaddleDetection-release-2.1/ppdet/modeling/backbones/resnet.py", line 577, in forward
    x = stage(x)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 902, in __call__
    outputs = self.forward(*inputs, **kwargs)
  File "/home/aistudio/work/PaddleDetection-release-2.1/ppdet/modeling/backbones/resnet.py", line 427, in forward
    block_out = block(block_out)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 902, in __call__
    outputs = self.forward(*inputs, **kwargs)
  File "/home/aistudio/work/PaddleDetection-release-2.1/ppdet/modeling/backbones/resnet.py", line 365, in forward
    out = self.branch2b(out)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 902, in __call__
    outputs = self.forward(*inputs, **kwargs)
  File "/home/aistudio/work/PaddleDetection-release-2.1/ppdet/modeling/backbones/resnet.py", line 133, in forward
    out = self.norm(out)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 902, in __call__
    outputs = self.forward(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/nn.py", line 1342, in forward
    mean_out, variance_out, *attrs)
KeyboardInterrupt

如果在训练中加了--eval参数，在模型训练完就可得到mAP指标，如果要对模型单独计算mAP，可以运行下列命令。

本实验提供了一个训练好的baseline模型，修改/home/aistudio/work/PaddleDetection-release-2.1/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.yml中weights即可对baseline模型进行评估：

weights: /home/aistudio/work/PaddleDetection-release-2.1/models/best_model

!cd /home/aistudio/work/PaddleDetection-release-2.1
!python -u /home/aistudio/work/PaddleDetection-release-2.1/tools/eval.py -c configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.yml
            

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import MutableMapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Iterable, Mapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Sized
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. 
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  if data.dtype == np.object:
W0913 19:17:47.394450  1020 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0913 19:17:47.400025  1020 device_context.cc:422] device: 0, cuDNN Version: 7.6.
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. 
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  if data.dtype == np.object:
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[09/13 19:17:52] ppdet.utils.checkpoint INFO: Finish loading model weights: /home/aistudio/work/PaddleDetection-release-2.1/models/best_model.pdparams
[09/13 19:17:52] ppdet.engine INFO: Eval iter: 0
[09/13 19:18:00] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json.
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[09/13 19:18:00] ppdet.metrics.coco_utils INFO: Start evaluate...
Loading and preparing results...
DONE (t=0.01s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.41s).
Accumulating evaluation results...
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/pycocotools/cocoeval.py:378: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/pycocotools/cocoeval.py:379: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)
DONE (t=0.08s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.351
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.864
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.157
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.207
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.363
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.191
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.107
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.438
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.445
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.425
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.451
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.321
[09/13 19:18:00] ppdet.engine INFO: Total sample number: 100, averge FPS: 12.847098054265162

模型推理，用训练出来的模型在一个PCB图像上进行测试。测试结果保存在work/PaddleDetection-release-2.1/output/04_missing_hole_10.jpg

! python -u tools/infer.py -c configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.yml \
                --infer_img=../PCB_DATASET/images/04_missing_hole_10.jpg \
                -o weights=/home/aistudio/work/PaddleDetection-release-2.1/models/best_model

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import MutableMapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Iterable, Mapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Sized
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. 
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  if data.dtype == np.object:
W0913 19:21:15.096993  1332 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0913 19:21:15.102478  1332 device_context.cc:422] device: 0, cuDNN Version: 7.6.
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. 
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  if data.dtype == np.object:
[09/13 19:21:19] ppdet.utils.checkpoint INFO: Finish loading model weights: /home/aistudio/work/PaddleDetection-release-2.1/models/best_model.pdparams
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[09/13 19:21:20] ppdet.engine INFO: Detection bbox results save in output/04_missing_hole_10.jpg

推理结果可视化：

%matplotlib inline
import matplotlib.pyplot as plt 
import cv2

infer_img = cv2.imread("output/04_missing_hole_10.jpg")
plt.figure(figsize=(15, 10))
plt.imshow(cv2.cvtColor(infer_img, cv2.COLOR_BGR2RGB))
plt.show()

数据增广（Mixup、AutoAugment、GridMask等）、测试时增强。

预训练模型、backbone,fpn，head,后处理、正则化、损失函数（如Balanced L1 Loss）。

学习率，优化器、参数更新策略。