Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network

Recent studies in image classification have demonstrated a variety of techniques for improving the performance of Convolutional Neural Networks (CNNs). However, attempts to combine existing techniques to create a practical model are still uncommon. In this study, we carry out extensive experiments to validate that carefully assembling these techniques and applying them to a basic CNN model in combination can improve the accuracy and robustness of the model while minimizing the loss of throughput. For example, our proposed ResNet-50 shows an improvement in top-1 accuracy from 76.3% to 82.78%, and an mCE improvement from 76.0% to 48.9%, on the ImageNet ILSVRC2012 validation set. With these improvements, inference throughput only decreases from 536 to 312. The resulting model significantly outperforms state-of-the-art models with similar accuracy in terms of mCE and inference throughput. To verify the performance improvement in transfer learning, fine grained classification and image retrieval tasks were tested on several open datasets and showed that the improvement to backbone network performance boosted transfer learning performance significantly. Our approach achieved 1st place in the iFood Competition Fine-Grained Visual Recognition at CVPR 2019, and the source code and trained models are available at https://github.com/clovaai/assembled-cnn