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JFT-300M

Introduced in Revisiting Unreasonable Effectiveness of Data in Deep Learning Era2017

About this Dataset

JFT-300M is an internal Google dataset used for training image classification models. 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.

Dataset Variants

JFT-300M

Papers1

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

It is found that the performance on vision tasks increases logarithmically based on volume of training data size, and it is shown that representation learning (or pre-training) still holds a lot of promise.

Dataset Loaders

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tensorflow/models

tfpytorch

Tasks

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Image Classification

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