CoCalc -- Now that we've gotten our hands dirty with classification, let's take on onelast task: training a classification model from scratch!A standard benchmark for image classification is the ImageNet dataset, howeverdue to licensing constraints we will use the CalTech 101 image classificationdataset in this tutorial.While we use the simpler CalTech 101 dataset in this guide, the same trainingtemplate may be used on ImageNet to achieve near state-of-the-art scores.Let's start out by tackling data loading:```pythonBATCH_SIZE = 32NUM_CLASSES = 101IMAGE_SIZE = (224, 224

GitHub Repository: keras-team/keras-io
Path: blob/master/guides/md/keras_hub/Now that we've gotten our hands dirty with classification, let's take on onelast task: training a classification model from scratch!A standard benchmark for image classification is the ImageNet dataset, howeverdue to licensing constraints we will use the CalTech 101 image classificationdataset in this tutorial.While we use the simpler CalTech 101 dataset in this guide, the same trainingtemplate may be used on ImageNet to achieve near state-of-the-art scores.Let's start out by tackling data loading:```pythonBATCH_SIZE = 32NUM_CLASSES = 101IMAGE_SIZE = (224, 224
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