Recently Yann LeCun complemented the authors of 'A ConvNet for the 2020s' https://mobile.twitter.com/ylecun/status/1481194969830498308?s=20 https://github.com/facebookresearch/ConvNeXt These statements imply that continued improvements in the metrics of success are indicators that learning is improving. Furthermore says LeCun, common sense reinforces this idea that 'helpful tricks' are successful in increasing the learning that occurs in these models. But are these models learning? Are they learning better? Or perhaps they have succeeded at overfitting and scoring better but have not learnt anything new. We took a look at the what the model learned, not just how it scored on its own metric. To this end we created a graph with links between each image and its top 5 classifications, the weights of the links are in proportion to the score of the class. Here are the data files: https://github.com/DaliaSmirnov/imagenet_research/blob/main/prediction_df_resnet50.p...