Severstal: Steel Defect Detection比赛的discussion调研

特征匹配
https://zhuanlan.zhihu.com/p/52140541
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/108078#latest-621878

ensemble技巧
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/107716#latest-624046
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/111457#latest-642578

这个链接提到训练时长的问题,或许需要保存中间结果
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/108554#latest-626181

提到了Dice-Score
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/101465#latest-586178

一篇检测锈斑的论文
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/101471#latest-625980
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/109297#latest-631198
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/108821#latest-629610
https://software.intel.com/en-us/articles/use-machine-learning-to-detect-defects-on-the-steel-surface

引导性链接
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/101969#latest-641353
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/103296#latest-640460

关注图像角落里的第一个像素的坐标到底是(1,1)还是(0,1)
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/102146#latest-589715

提到了一篇论文讨论了语义分割里面的不同类型的loss
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/102386#latest-625072
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/110536#latest-639400
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/108206#latest-635042

提供了一些网络
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/105296#latest-606287

下面这几个没有完全看懂
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/103861#latest-600125
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/103367#latest-639821
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/106477#latest-642453
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/109423#latest-630712
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/108270#latest-629664
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/107889#latest-631449

半监督
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/110426#latest-641084

提到了数据增强
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/104850#latest-606137
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/109227#latest-640539

貌似是使用了条件随机场
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/106086#latest-613534

蛙哥说先判断一个像素是不是锈斑,然后判断是第几类
然后提到不要使用所有数据,那样反而会让得分低下
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/106099#latest-629814

照片一致,但是标签不一致
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/107053#latest-621775

pool大小的调整建议
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/106952#latest-620343

新手包
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/106462#latest-641632

说法是34层的resnet最好
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/108949#latest-636914

以前的语义分割冠军方案
https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation/discussion/108308#latest-625068

椒盐噪声和对抗验证
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/111119#latest-640192
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/106834#latest-633503
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/108790#latest-627471

找到很多子类
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/110363#latest-638823

提出一个问题:
使用预训练的网络,但是预训练的图片和当前的图片不一样的时候如何处理?(帖子内容我没看,其实就是修改最后一层)
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/107246#latest-618321

kaggle在语义分割中的得分机制dice-score
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/110188#latest-642222

貌似需要扔掉一些图片
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/109673#latest-637866

一大堆神经网络的论文
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/109370#latest-631305

提到了IOU
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/109847#latest-632505

语义分割网络回顾
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/109318#latest-629292

下面这个似乎非常重要，据说只要移除False Positive，就可以获得0.9117
https://www.kaggle.com/evgenyshtepin/severstal-mlcomp-catalyst-infer-0-90726
https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/106462#latest-634450

这个EDA做的很漂亮
https://www.kaggle.com/avirald/clear-mask-visualization-and-simple-eda

这个链接提到IoU是一种 loss
https://www.kaggle.com/rishabhiitbhu/unet-starter-kernel-pytorch-lb-0-88

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