icpr-track2-lightweight's Introduction
测试结果文件下载地址: 百度网盘: 链接:https://pan.baidu.com/s/1RxdttZnZBhuPqQOCqUbbBw?pwd=hge6 提取码:hge6 OneDrive: https://1drv.ms/u/c/90bf30fdc8dd9ee7/EVxOhbh2Z5hOmhuejKJRM80BJ4HYRSDUSCh0YQkwWt-R-w?e=FXL6ro 以下依次介绍Baseline模型训练、测试、评分的流程: 一、训练 1.准备数据。将你的数据集放在dataset文件夹下。images包含所有的训练集图像,mask包含所有的标签。由随机采样的方式划分训练集和测试集。 Baseline的划分为训练集7000张,测试集2000张,将训练集和测试集的TXT放在70_20文件夹下。请仿照ICPR_Track2文件的放置来规划你的数据集。 2.路径更新。下载测试结果文件,解压到result_WS路径下。 model/parse_args_train.py 中dataset更新为你的数据集名称,root更新指向dataset文件夹的路径, split_method更新为存储你的划分TXT的文件夹名称,base_size、crop_size将图片尺寸调整为512*512。 3.运行train_test_evaluation.py 开启训练,训练结果和权重文件保存在result_WS/ICPR_Track2中。训练完成后,会打印模型在测试集上取得的指标数值。 二、测试 1.更新路径。model/parse_args_test.py 中st_model指向result_WS中的文件夹,model_dir指向训练得到的权重.pth.tar的路径,root指向你的数据集路径。 2.测试模型精度。运行test_and_visulization.py 即可获得测试指标,Pd、Fa、mIoU. 3.测试模型复杂度。model/complexity.py 中net更新为你的模型,更新好图片的尺寸,如Baseline的图片尺寸为(3,512,512)。 运行complexity.py 打印的Total params即为模型的参数量,Total FLops即为模型的运算量。 三、评分 1.使用你的评价指标更新Eval.py中的参数。以下参数需要更新为你的指标数值,Fa虚警率,Pd检测率,IoU平均交并比,Params参数量,Flops运算量。 注意格式、单位与Baseline保持一致,如参数量单位为M,运算量单位为GFLOPs等。 2.运行Eval.py 得到评分Score。
icpr-track2-lightweight's People
icpr-track2-lightweight's Issues
官网上的基线模型应该是采用DNANet而非UNet吧?
如题,能否顺便更新一下比赛交流群的二维码?谢谢您。
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