  We propose Dataset Reinforcement, a strategy to improve a dataset once such
that the accuracy of any model architecture trained on the reinforced dataset
is improved at no additional training cost for users. We propose a Dataset
Reinforcement strategy based on data augmentation and knowledge distillation.
Our generic strategy is designed based on extensive analysis across CNN- and
transformer-based models and performing large-scale study of distillation with
state-of-the-art models with various data augmentations. We create a reinforced
version of the ImageNet training dataset, called ImageNet+, as well as
reinforced datasets CIFAR-100+, Flowers-102+, and Food-101+. Models trained
with ImageNet+ are more accurate, robust, and calibrated, and transfer well to
downstream tasks (e.g., segmentation and detection). As an example, the
accuracy of ResNet-50 improves by 1.7% on the ImageNet validation set, 3.5% on
ImageNetV2, and 10.0% on ImageNet-R. Expected Calibration Error (ECE) on the
ImageNet validation set is also reduced by 9.9%. Using this backbone with
Mask-RCNN for object detection on MS-COCO, the mean average precision improves
by 0.8%. We reach similar gains for MobileNets, ViTs, and Swin-Transformers.
For MobileNetV3 and Swin-Tiny, we observe significant improvements on
ImageNet-R/A/C of up to 20% improved robustness. Models pretrained on ImageNet+
and fine-tuned on CIFAR-100+, Flowers-102+, and Food-101+, reach up to 3.4%
improved accuracy. The code, datasets, and pretrained models are available at
https://github.com/apple/ml-dr.
