This project include the code and results for 'Cross-Scale Edge Purification Network for Salient Object Detection of Steel Defect Images', Measurement, accepted, 2022. paper links:

Ubuntu 16.04 matplotlib 3.2.2 numpy 1.19.2 pip 21.2.2 timm 0.5.4 torch 1.1.0 torchstat 0.0.6 torchvision 0.3.0

We provide saliency maps of our CSEPNet (VGG_backbone (code: 8gde )on SD-saliency-900 dataset.

You can also download dataset of our model (code: hlpi)) and Pretrained Parameters (code: gxc4 ).

$ tree -L 3
.
├── backbone
│   ├── __init__.py
│   ├── origin
│   │   ├── from_origin.py
│   │   ├── __init__.py
│   │   ├── resnet.py
│   │   └── vgg.py
│   └── wsgn
│       ├── customized_func.py
│       ├── from_wsgn.py
│       ├── __init__.py
│       ├── resnet.py
│       └── resnext.py
├── config.py
├── LICENSE
├── loss
│   ├── CEL.py,pytorch_ssim
│   └── __init__.py
├── main.py
├── module
│   ├── BaseBlocks.py
│   ├── __init__.py
│   ├── MyModule.py

├── network
│   ├── __init__.py
│   ├── CSEPNet.py
├── output (These are the files generated when I ran the code.)
│   ├── result
│   │   ├── cfg_2020-07-23.txt
│   │   ├── pre
│   │   ├── pth(generate in training process)
│   └── result.xlsx   
│         
├── readme.md
└── utils
    ├── py_sod_metrics(code to calculate metrics)
    ├── dataloader.py
    ├── __init__.py
    ├── joint_transforms.py
    ├── metric.py
    ├── misc.py
    ├── pipeline_ops.py
    ├── recorder.py
    ├── solver.py
    └── tensor_ops.py

• backbone: Store some code for backbone networks.
• loss: The code of the loss function.
• module: The code of important modules.
• network: The code of the network.
• output: It saves all results.
• utils: Some instrumental code.
  • dataloader.py: About creating the dataloader.
  • ...
• main.py: I think you can understand.
• config.py: Configuration file for model training and testing.

# config.py
arg_config = {
    "model": "CSEPNet_VGG16",  # 实际使用的模型，需要在`network/__init__.py`中导入   CSEPNET_Res50
    "info": "",  # 关于本次实验的额外信息说明，这个会附加到本次试验的exp_name的结尾，如果为空，则不会附加内容。
    "use_amp": False,  # 是否使用amp加速训练
    "resume_mode": "",# the mode for resume parameters: ['train', 'test', '']
    "use_aux_loss": False,  # 是否使用辅助损失
    "save_pre": True,  # 是否保留最终的预测结果
    "epoch_num": 100,  # 训练周期, 0: directly test model
    "lr": 0.001,  # 微调时缩小100倍
    "xlsx_name": "result.xlsx",  # the name of the record file

    # 数据集设置
    "rgb_data": {
        "tr_data_path": SDI_tr_path,  ##(训练集path)
        "te_data_list": OrderedDict(
            {

                "TrainsetEDRNet": SDI_te_path, ##(测试集path)
            },
        ),
    },
    # 训练过程中的监控信息
    "tb_update": 0,  # >0 则使用tensorboard
    "print_freq": 50,  # >0, 保存迭代过程中的信息
    # img_prefix, gt_prefix，用在使用索引文件的时候的对应的扩展名
    "prefix": (".jpg", ".png"),
    # if you dont use the multi-scale training, you can set 'size_list': None
    # "size_list": [224, 256, 288, 320, 352],
    "size_list": None,  # 不使用多尺度训练
    "reduction": "mean",  # 损失处理的方式，可选“mean”和“sum”
    # 优化器与学习率衰减
    "optim": "sgd_trick",  # 自定义部分的学习率
    "weight_decay": 5e-4,  # 微调时设置为0.0001
    "momentum": 0.9,
    "nesterov": False,
    "sche_usebatch": False,
    "lr_type": "poly",
    "warmup_epoch": 1,  # depond on the special lr_type, only lr_type has 'warmup', when set it to 1, it means no warmup.
    "lr_decay": 0.9,  # poly
    "use_bigt": True,  # 训练时是否对真值二值化（阈值为0.5）
    "batch_size": 4,  # 要是继续训练, 最好使用相同的batchsize
    "num_workers": 4,  # 不要太大, 不然运行多个程序同时训练的时候, 会造成数据读入速度受影响
    "input_size": 256,
}

1. You can customize the value of the arg_config dictionary in the configuration file.
   • The first time you use it, you need to adjust the path of every dataset.
   • Set model to the model name that has been imported in network/__init__.py.
   • Modify info according to your needs, which will be appended to the final exp_name. (The function construct_exp_name in utils/misc.py will generate the final exp_name.)
   • set the "resume_mode" in 【arg_config】 of "config.py" as " "( if set it as "train", the model will continue to train from the saved pth)
   • And other setting in config.py, like epoch_num, lr and so on...
2. In the folder code, run the command python main.py.
3. Everything is OK. Just wait for the results.
4. The test will be performed automatically when the training is completed.
5. All results will be saved into the folder output, including predictions in folder pre (if you set save_pre to True), .pth files in folder pth and other log files.

Our pre-training parameters can also be used in this way.

1. In the output folder, please ensure that there is a folder corresponding to the model (See Note), which contains the pth folder, and the .pth file of the model is located here and its name is state_final.pth.
2. Set the value of model of arg_config to the model you want to test.
3. Set the value of te_data_list to your dataset path.
4. Set the value of resume_mode to test.
5. In the folder code, run python main.py.
6. You can find predictions from the model in the folder pre of the output.

• You can use the evaluation tool (MATLAB version) to evaluate the above saliency maps.
• Thanks for the code (python version) provided by Lartpang , we use the code to evaluate the metrics for testing
• We also give thanks to Fan for the matlab code (https://github.com/DengPingFan/CODToolbox)

Our code is based on MINet: https://github.com/lartpang/MINet, thanks for the authors!!

@inproceedings{fan2017structure, title={{Structure-measure: A New Way to Evaluate Foreground Maps}}, author={Fan, Deng-Ping and Cheng, Ming-Ming and Liu, Yun and Li, Tao and Borji, Ali}, booktitle={IEEE International Conference on Computer Vision (ICCV)}, pages = {4548-4557}, year={2017}, note={\url{http://dpfan.net/smeasure/}}, organization={IEEE} }

@inproceedings{Fan2018Enhanced, author={Fan, Deng-Ping and Gong, Cheng and Cao, Yang and Ren, Bo and Cheng, Ming-Ming and Borji, Ali}, title={{Enhanced-alignment Measure for Binary Foreground Map Evaluation}}, booktitle={International Joint Conference on Artificial Intelligence (IJCAI)}, pages={698--704}, note={\url{http://dpfan.net/e-measure/}}, year={2018} }

@inproceedings{MINet-CVPR2020, author = {Pang, Youwei and Zhao, Xiaoqi and Zhang, Lihe and Lu, Huchuan}, title = {Multi-Scale Interactive Network for Salient Object Detection}, booktitle = CVPR, month = {June}, year = {2020} }

For any questions, please contact me at tding97@163.com.