Automated Cleanup of the ImageNet Dataset by Model Consensus, Explainability and Confident Learning
arXiv:2103.16324
Abstract
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.
References in corpus (5)
- Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
- No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World
- Data augmentation instead of explicit regularization
- From ImageNet to Image Classification: Contextualizing Progress on Benchmarks
- Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets