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. Name template Description train-IG-I-1.5k Instagram training set of I images and ∼1.5k hashtags from ImageNet-1k. train-IG-I-8.5k Instagram training set of I images and ∼8.5k hashtags from WordNet. train-IG-I-17k Instagram training set of I images and ∼17k hashtags from WordNet. train-IN-1M-1k The standard ImageNet-1k ILSVRC training set with 1.28M images. val-IN-50k-1k The standard ImageNet-1k ILSVRC validation set with 50k images. train-IN-I-L Extended ImageNet training set of I images and L ∈ {5k, 9k} labels. val-IN-I-L Extended ImageNet validation set of I images and L ∈ {5k, 9k} labels. train-CUB-6k-200 The Caltech-UCSD Birds-200-2011 training set. val-CUB-6k-200 The Caltech-UCSD Birds-200-2011 validation set. train-Places-1.8M-365 The Places365-Standard training set (high-resolution version). val-Places-37k-365 The Places365-Standard validation set (high-resolution version). train-COCO-135k-80 The standard COCO detection training set (2017 version). val-COCO-5k-80 The standard COCO detection validation set (2017 version). test-COCO-20k-80 The standard COCO detection test-dev set (2017 version).Table 1: Summary of image classification datasets. Each dataset is named with a template, role-source-I-L, that indicates its role (training, validation, testing), source, number of images I, and number of labels L.

In this paper we discuss several forms of spatiotemporal convolutions for video analysis and study their effects on action recognition. Our motivation stems from the observation that 2D CNNs applied to individual frames of the video have remained solid performers in action recognition. In this work we empirically demonstrate the accuracy advantages of 3D CNNs over 2D CNNs within the framework of residual learning. Furthermore, we show that factorizing the 3D convolutional filters into separate spatial and temporal components yields significantly gains in accuracy. Our empirical study leads to the design of a new spatiotemporal convolutional block "R(2+1)D" which produces CNNs that achieve results comparable or superior to the state-of-theart on Sports-1M, Kinetics, UCF101, and HMDB51.

We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset. Our findings are three-fold: 1) 3D ConvNets are more suitable for spatiotemporal feature learning compared to 2D ConvNets; 2) A homogeneous architecture with small 3 × 3 × 3 convolution kernels in all layers is among the best performing architectures for 3D ConvNets; and 3) Our learned features, namely C3D (Convolutional 3D), with a simple linear classifier outperform state-of-the-art methods on 4 different benchmarks and are comparable with current best methods on the other 2 benchmarks. In addition, the features are compact: achieving 52.8% accuracy on UCF101 dataset with only 10 dimensions and also very efficient to compute due to the fast inference of ConvNets. Finally, they are conceptually very simple and easy to train and use.