  Nowadays deep learning-based methods have achieved a remarkable progress at
the image classification task among a wide range of commonly used datasets
(ImageNet, CIFAR, SVHN, Caltech 101, SUN397, etc.). SOTA performance on each of
the mentioned datasets is obtained by careful tuning of the model architecture
and training tricks according to the properties of the target data. Although
this approach allows setting academic records, it is unrealistic that an
average data scientist would have enough resources to build a sophisticated
training pipeline for every image classification task he meets in practice.
This work is focusing on reviewing the latest augmentation and regularization
methods for the image classification and exploring ways to automatically choose
some of the most important hyperparameters: total number of epochs, initial
learning rate value and it's schedule. Having a training procedure equipped
with a lightweight modern CNN architecture (like bileNetV3 or EfficientNet),
sufficient level of regularization and adaptive to data learning rate schedule,
we can achieve a reasonable performance on a variety of downstream image
classification tasks without manual tuning of parameters to each particular
task. Resulting models are computationally efficient and can be deployed to CPU
using the OpenVINO toolkit. Source code is available as a part of the OpenVINO
Training Extensions (https://github.com/openvinotoolkit/training_extensions).
