+++ /dev/null
-# PCB-defect-detection
-## Note
-This project code is forked from https://github.com/DetectionTeamUCAS/FPN_Tensorflow. I have only made minor changes on this wonderful and clear project. Thanks for their perfect code. I can learn and apply it to a new problem. The Chinese blog is: https://blog.csdn.net/dlh_sycamore/article/details/87886198
-## PCB defect dataset
-The Open Lab on Human Robot Interaction of Peking University has released the PCB defect dataset. If you need to do experiments on this dataset, please contact the dataset author.
-
-You can download at http://robotics.pkusz.edu.cn/resources/dataset/. or https://www.dropbox.com/s/32kolsaa45z2mpj/PCB_DATASET.zip?dl=0
-
-
-6 types of defects are made by photoshop, a graphics editor published by Adobe Systems. The defects defined in the dataset are: missing hole, mouse bite, open circuit, short, spur, and spurious copper.
-For example:
-
-.png)
-### Dataset Update
-The paper of this project will be update.
-However, the defect images of raw dataset are high-resolution.
-With the respect of such small dataset, data augmentation techniques are adopted before data training. The images are then cropped
-into 600 × 600 sub-images, forming our training set and testing set with 9920 and 2508 images, respectively.
-
-### Some instructions:
-
-1、This augmented dataset is privately owned, if you want to use it in your paper, please contact me.
-
-2、If you have questions, please open an issue, I will reply as soon as possible. If you don't have some difficult problem about this project, maybe you **don't need** to send me an email and add wechat, just open an issue.
-
-3、Of course, if you have other research questions like dataset and defect detection, welcome to send me an email.
-
-4、If you use our code, please access this paper and cite:
-Ding, Runwei, et al. "TDD-Net: A Tiny Defect Detection Network for Printed Circuit Boards." CAAI Transactions on Intelligence Technology (https://ieeexplore.ieee.org/document/8757220)(2019).
-
-5、Chinese Blog: https://blog.csdn.net/dlh_sycamore/article/details/87886198
-
-## Download Model
-Please download [resnet50_v1](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz)、[resnet101_v1](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz) pre-trained models on Imagenet, put it to $PATH_ROOT/data/pretrained_weights.
-## My Development Environment
-1、python2.7 (anaconda recommend)
-2、CUDA Version 8.0.44 , CUDNN=5.1.10
-3、[opencv(cv2)](https://pypi.org/project/opencv-python/)
-4、[tfplot](https://github.com/wookayin/tensorflow-plot)
-5、tensorflow == 1.121
-
-## Compile
-```
-cd $PATH_ROOT/libs/box_utils/cython_utils
-python setup.py build_ext --inplace
-```
-
-## Demo(available)
-
-Then download the corresponding weights:
-
-For Baiduyun: [weights](https://pan.baidu.com/s/1rvHjihG1fL499SqU28Nang). code：shac
-
-For Google driver: [weights](https://drive.google.com/drive/folders/1oEHrEslnM5DBKB-610j-gO-x9-e90VwR?usp=sharing)
-
-
-And then put the trained weights into
-```
-TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights')
-```
-It depends on your project path. Please read '$PATH_ROOT/libs/configs/cfgs.py'.
-
-```
-cd $PATH_ROOT/tools
-python inference.py --data_dir='/PATH/TO/THE/TO/BE/DETECTED/IMAGES/'
- --save_dir='/PATH/TO/SAVE/RESULTS/'
- --GPU='0'
-```
-After running this code, you will get the detected image in your 'save_dir' path.
-
-Why didn't you get the correct output image when run the inference.py?
-
-**Note!!! If you didn't load the correct checkpoint but the pretrain model, you need to change the checkpoint file (FPN_Res101_0117_OHEM/checkpoint), because this file contains the wrong path, and you need to change it with your file path!!!**
-
-## Train
-If you want to train your own data, please follow this project: https://github.com/DetectionTeamUCAS/FPN_Tensorflow
-
-1、Select a configuration file in the folder ($PATH_ROOT/libs/configs/) and copy its contents into cfgs.py. Modify parameters (such as CLASS_NUM, DATASET_NAME, VERSION, etc.) in $PATH_ROOT/libs/configs/cfgs.py
-
-2、Generate train and test floder
-
-```
-cd $PATH_ROOT/data/io/
-python divide_data.py
-```
-You should check the image_path and xml_path in the 'divide_data.py'
-
-2、Make tfrecord
-
-(1)Modify parameters (such as VOC_dir, xml_dir, image_dir, dataset, etc.) in $PATH_ROOT/data/io/convert_data_to_tfrecord.py
-```
-cd $PATH_ROOT/data/io/
-python convert_data_to_tfrecord.py
-```
-
-3、Train
-```
-cd $PATH_ROOT/tools
-python train.py
-```
-
-## Eval
-```
-cd $PATH_ROOT/tools
-python eval.py --eval_imgs='/PATH/TO/THE/TO/BE/EVALED/IMAGES/'
- --annotation_dir='/PATH/TO/TEST/ANNOTATION/'
- --GPU='0'
-```
-After running this code, you will get the precision, recall and AP of per defect type.
-
-## Some results
-[the more results](https://github.com/Ixiaohuihuihui/PCB-defect-detection/tree/master/tools/inference_results)
-
-
-
-## The Precision and Recall curve (PR)
-
Code Review / ealt-edge.git / blobdiff