This repository contains visualizations of the results of the classification of ImageNet validation set (50,000 images, 1,000 classes) by 28 pre-trained CNNs before and after compression by Discrete Atomic Compression (DAC) and a Progressive DCT-based Coder (PDCTC). References Discrete Atomic Compression: Makarichev, V.; Vasilyeva, I.; Lukin, V.; Vozel, B.; Shelestov, A.; Kussul, N. Discrete Atomic Transform-Based Lossy Compression of Three-Channel Remote Sensing Images with Quality Control. Remote Sens. 2022, 14, 125. DOI: https://doi.org/10.3390/rs14010125. Progressive DCT-based Coder: Makarichev, V.; Lukin, V.; Brysina, I. Progressive DCT-Based Coder and Its Comparison to Atomic Function Based Image Lossy Compression. In Proceedings of the 2022 IEEE 16th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), Lviv-Slavske, Ukraine, 22-26 February 2022; pp. 1-6. DOI: https://doi.org/10.1109/TCSET55632.2022.9766871. ImageNet: Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; Berg, A.C.; Fei-Fei, L. ImageNet Large Scale Visual Recognition Challenge. Int. J. Comput. Vis. 2015, 115, pp. 211--252. DOI: https://doi.org/10.1007/s11263-015-0816-y.