That error correction has errors. Their consensus claims a label "chain link fence" is wrong on an image that includes a chain link fence, albeit as a lesser el

That error correction has errors. Their consensus claims a label "chain link fence" is wrong on an image that includes a chain link fence, albeit as a lesser element of the images composition. https://labelerrors.com/static/imagenet/val/n03000134/ILSVRC2012_val_00048435.JPEG https://labelerrors.com/static/imagenet/val/n03000134/ILSVRC... It is also interesting that their model guessed "container ship" with the dominant element of the picture being a harbor crane. I think there might be a lot of images in the ImageNet data set that include both a crane and a ship as strong elements, but only have the ship label. ImageNet is a single label set, not a multi label set and that is a known problem. This paper talks more about it: https://github.com/naver-ai/relabel_imagenet https://github.com/naver-ai/relabel_imagenet With large data sets containing millions of images the only way forward seems to have AI keep refining the data, escalating things multiple automatons disagree on to be reviewed by more classifiers, including humans.