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Bag of Tricks for Image Classification with Convolutional Neural Networks

2018-12-04CVPR 2019Code Available1· sign in to hype

Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, Mu Li

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Abstract

Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods. In the literature, however, most refinements are either briefly mentioned as implementation details or only visible in source code. In this paper, we will examine a collection of such refinements and empirically evaluate their impact on the final model accuracy through ablation study. We will show that, by combining these refinements together, we are able to improve various CNN models significantly. For example, we raise ResNet-50's top-1 validation accuracy from 75.3% to 79.29% on ImageNet. We will also demonstrate that improvement on image classification accuracy leads to better transfer learning performance in other application domains such as object detection and semantic segmentation.

Tasks

Domain GeneralizationGeneral Classificationimage-classificationImage ClassificationObject DetectionSemantic SegmentationTransfer Learning

Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
VizWiz-ClassificationResNet-26-DAccuracy - All Images39.7—Unverified

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