{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Transfer Learning on CIFAR-10 Dataset\n", "\n", "\n", "## Introduction\n", "\n", "In this tutorial, you learn how to train an image classification model using [Transfer Learning](https://en.wikipedia.org/wiki/Transfer_learning). Transfer learning is a popular machine learning technique that uses a model trained on one problem and applies it to a second related problem. Compared to training from scratch or designing a model for your specific problem, transfer learning can leverage the features already learned on a similar problem and produce a more robust model in a much shorter time.\n", "\n", "Train your model with the [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) dataset which consists of 60,000 32x32 color images in 10 classes. As for the pre-trained model, use the ResNet50v1[1] model. It's a 50 layer deep model already trained on [ImageNet](http://www.image-net.org/), a much larger dataset consisting of over 1.2 million images in 1000 classes. Modify it to classify 10 classes from the CIFAR-10 dataset.\n", "\n", "![The CIFAR-10 Dataset](https://djl-ai.s3.amazonaws.com/resources/images/cifar-10.png)\n", "