State-of-the-art visual perception models for a wide range of tasks rely on
supervised pretraining. ImageNet classification is the de facto pretraining
task for these models. Yet, ImageNet is now nearly ten years old and is by
modern standards "small". Even so, relatively little is known about the
behavior of pretraining with datasets that are multiple orders of magnitude
larger. The reasons are obvious: such datasets are difficult to collect and
annotate. In this paper, we present a unique study of transfer learning with
large convolutional networks trained to predict hashtags on billions of social
media images. Our experiments demonstrate that training for large-scale hashtag
prediction leads to excellent results. We show improvements on several image
classification and object detection tasks, and report the highest ImageNet-1k
single-crop, top-1 accuracy to date: 85.4% (97.6% top-5). We also perform
extensive experiments that provide novel empirical data on the relationship
between large-scale pretraining and transfer learning performance.

本文探讨了利用大规模社交媒体图像预测 hashtag 的卷积神经网络进行的迁移学习的行为，并展示了相应的实验结果，证明进行大规模预训练能够显著提高图片分类和物体检测任务的表现。