There has been remarkable success in the field of computer vision over the past decade, much of which can be directly attributed to the application of deep learning models to this machine perception task. Furthermore, since 2012 there have been significant advances in representation capabilities of these systems due to (a) deeper models with high complexity, (b) increased computational power and (c) availability of large-scale labeled data. And while every year we get further increases in computational power and the model complexity (from 7-layer AlexNet to 101-layer ResNet), available datasets have not scaled accordingly. A 101-layer ResNet with significantly more capacity than AlexNet is still trained with the same 1M images from ImageNet circa 2011. As researchers, we have always wondered: what will happen if we scale up the amount of training data 10x, will the accuracy double? How about 100x or maybe even 300x? Will the accuracy plateau or will we continue to see increasing gains with more and more data?
|While GPU computation power and model sizes have continued to increase over the last five years, size of the largest training dataset has surprisingly remained constant.|
In our paper, “Revisiting Unreasonable Effectiveness of Data in Deep Learning Era”, we take the first steps towards clearing the clouds of mystery surrounding the relationship between `enormous data’ and deep learning. Our goal was to explore: (a) if visual representations can be still improved by feeding more and more images with noisy labels to currently existing algorithms; (b) the nature of the relationship between data and performance on standard vision tasks such as classification, object detection and image segmentation; (c) state-of-the-art models for all the tasks in computer vision using large-scale learning.
Of course, the elephant in the room is where can we obtain a dataset that is 300x larger than ImageNet? At Google, we have been continuously working on building such datasets automatically to improve computer vision algorithms. Specifically, we have built an internal dataset of 300M images that are labeled with 18291 categories, which we call JFT-300M. The images are labeled using an algorithm that uses complex mixture of raw web signals, connections between web-pages and user feedback. This results in over one billion labels for the 300M images (a single image can have multiple labels). Of the billion image labels, approximately 375M are selected via an algorithm that aims to maximize label precision of selected images. However, there is still considerable noise in the labels: approximately 20% of the labels for selected images are noisy. Since there is no exhaustive annotation, we have no way to estimate the recall of the labels.
Our experimental results validate some of the hypotheses but also generates some unexpected surprises:
|Object detection performance when pre-trained on different subsets of JFT-300M from scratch. x-axis is the dataset size in log-scale, y-axis is the detection performance in mAP@[.5,.95] on COCO-minival subset.|
It is important to highlight that the training regime, learning schedules and parameters we used are based on our understanding of training ConvNets with 1M images from ImageNet. Since we do not search for the optimal set of hyper-parameters in this work (which would have required considerable computational effort), it is highly likely that these results are not the best ones you can obtain when using this scale of data. Therefore, we consider the quantitative performance reported to be an underestimate of the actual impact of data.
This work does not focus on task-specific data, such as exploring if more bounding boxes affects model performance. We believe that, although challenging, obtaining large scale task-specific data should be the focus of future study. Furthermore, building a dataset of 300M images should not be a final goal – as a community, we should explore if models continue to improve in the regime of even larger (1 billion+ image) datasets.
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta
This work would not have been possible without the significant efforts of the Image Understanding and Expander teams at Google who built the massive JFT dataset. We would specifically like to thank Tom Duerig, Neil Alldrin, Howard Zhou, Lu Chen, David Cai, Gal Chechik, Zheyun Feng, Xiangxin Zhu and Rahul Sukthankar for their help. Also big thanks to the VALE team for APIs and specifically, Jonathan Huang, George Papandreou, Liang-Chieh Chen and Kevin Murphy for helpful discussions.