Vision-Transformer, ViT, model validation dataset

The U.S. cotton industry is highly concerned with removing plastic contamination from cotton lint. A major source of this contamination is the plastic used to wrap cotton modules produced by John Deere round module harvesters. A machine-vision detection and removal system has been developed to address this problem, using low-cost color cameras to detect plastic in the cotton stream and remove it. However, the system requires a lot of calibration and is difficult for cotton gin workers to operate due to its reliance on custom machine-vision classifier running on low-cost ARM computers running Linux. This research aims to make the system more user-friendly by adding an auto-calibration feature that can track cotton colors and avoid plastic images, reducing the need for skilled personnel to operate the system and making it easier for the cotton ginning industry to adopt. This image dataset was created to validate several Vision-Transformer, ViT, AI models that in combination provides the key enabling technology for the auto-calibration code.Methods:Each image was hand-classed into one of four classes {cotton, empty-tray, plastic, hand-intrusion}. In the original dataset there were over 6000 hand-classed images. A few were removed as it was unclear from visual inspection as to which class the image should belong to and was classed as non-determinant, "ND". The ND images were omitted from this dataset. So this data provides a high quality training or validation image dataset. Images were collected at three commercial cotton gins in the 2023 cotton ginning season with one gin in mid-west, one in south and one in W. Texas; all in the U.S.A.