  The success of deep learning in vision can be attributed to: (a) models with
high capacity; (b) increased computational power; and (c) availability of
large-scale labeled data. Since 2012, there have been significant advances in
representation capabilities of the models and computational capabilities of
GPUs. But the size of the biggest dataset has surprisingly remained constant.
What will happen if we increase the dataset size by 10x or 100x? This paper
takes a step towards clearing the clouds of mystery surrounding the
relationship between `enormous data' and visual deep learning. By exploiting
the JFT-300M dataset which has more than 375M noisy labels for 300M images, we
investigate how the performance of current vision tasks would change if this
data was used for representation learning. Our paper delivers some surprising
(and some expected) findings. First, we find that the performance on vision
tasks increases logarithmically based on volume of training data size. Second,
we show that representation learning (or pre-training) still holds a lot of
promise. One can improve performance on many vision tasks by just training a
better base model. Finally, as expected, we present new state-of-the-art
results for different vision tasks including image classification, object
detection, semantic segmentation and human pose estimation. Our sincere hope is
that this inspires vision community to not undervalue the data and develop
collective efforts in building larger datasets.
