Steel is one of the most important building materials of modern times. Steel buildings are resistant to natural and man-made wear which has made the material ubiquitous around the world. To help make production of steel more efficient, this competition will help identify defects.
Severstal is leading the charge in efficient steel mining and production. They believe the future of metallurgy requires development across the economic, ecological, and social aspects of the industry—and they take corporate responsibility seriously. The company recently created the country’s largest industrial data lake, with petabytes of data that were previously discarded. Severstal is now looking to machine learning to improve automation, increase efficiency, and maintain high quality in their production.
The production process of flat sheet steel is especially delicate. From heating and rolling, to drying and cutting, several machines touch flat steel by the time it’s ready to ship. Today, Severstal uses images from high frequency cameras to power a defect detection algorithm.
In this competition, you’ll help engineers improve the algorithm by localizing and classifying surface defects on a steel sheet.
If successful, you’ll help keep manufacturing standards for steel high and enable Severstal to continue their innovation, leading to a stronger, more efficient world all around us.
In this competition you will be predicting the location and type of defects found in steel manufacturing. Images are named with a uniqueImageId. You must segment and classify the defects in the test set.
Each image may have no defects, a defect of a single class, or defects of multiple classes. For each image you must segment defects of each class (ClassId = [1, 2, 3, 4]).
The segment for each defect class will be encoded into a single row, even if there are several non-contiguous defect locations on an image. You can read more about the encoding standard on the evaluation page.
Submissions to this competition must be made through Kernels. After your submission against the Public test set, your kernel will re-run automatically against the entire Public and Private (unseen) test set. Refer to the Kernels Requirement Page for more information.
1、train_images/ - folder of training images
2、test_images/ - folder of test images (you are segmenting and classifying these images)
3、train.csv - training annotations which provide segments for defects (ClassId = [1, 2, 3, 4])
4、sample_submission.csv - a sample submission file in the correct format; note, eachImageId4 rows, one for each of the 4 defect classes