  The convolutional neural networks (CNNs) trained on ILSVRC12 ImageNet were
the backbone of various applications as a generic classifier, a feature
extractor or a base model for transfer learning. This paper describes automated
heuristics based on model consensus, explainability and confident learning to
correct labeling mistakes and remove ambiguous images from this dataset. After
making these changes on the training and validation sets, the ImageNet-Clean
improves the model performance by 2-2.4 % for SqueezeNet and EfficientNet-B0
models. The results support the importance of larger image corpora and
semi-supervised learning, but the original datasets must be fixed to avoid
transmitting their mistakes and biases to the student learner. Further
contributions describe the training impacts of widescreen input resolutions in
portrait and landscape orientations. The trained models and scripts are
published on Github (https://github.com/kecsap/imagenet-clean) to clean up
ImageNet and ImageNetV2 datasets for reproducible research.
