X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2FREADME.md;fp=example-apps%2FPDD%2Fpcb-defect-detection%2FREADME.md;h=0000000000000000000000000000000000000000;hb=3ed2c61d9d7e7916481650c41bfe5604f7db22e9;hp=a29e1eff6e78ea4a9d86e6c28a2b9f01048ddd6a;hpb=e6d40ddb2640f434a9d7d7ed99566e5e8fa60cc1;p=ealt-edge.git diff --git a/example-apps/PDD/pcb-defect-detection/README.md b/example-apps/PDD/pcb-defect-detection/README.md deleted file mode 100755 index a29e1ef..0000000 --- a/example-apps/PDD/pcb-defect-detection/README.md +++ /dev/null @@ -1,117 +0,0 @@ -# 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: - -![1](a(missinghole).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) -![1](01_missing_hole_01.jpg) -![2](04_mouse_bite_10.jpg) - -## The Precision and Recall curve (PR) -![3](TDD_results.jpg)