What is Image Classification? Data Augmentation? Transfer Learning?

What is Image Classification? Data Augmentation? Transfer Learning?

In this article, we will explore the image classification problem. The first part will present training a model from scratch, the second will present training with data augmentation, and the last transfer learning with pre-trained models.

This article is the first part of three articles about computer vision. Part 2 will explain Object Recognition. Part 3 will be about Image Segmentation.

With this article is provided a notebook: [here _](https://github.com/Christophe-pere/Image_classification)_on GitHub

Introduction

What is more exciting than seeing the world? To be able to see the best around us? The beauty of a sunset, the memorable waterfalls, or the seas of ice? Nothing would be possible if evolution hadn’t endowed us with eyes.

We recognize things because we have learned the shape of objects, we have learned to estimate that different shape from those we have encountered can be associated with the same object. We have learned by experience and because we were given the names of said objects. Like a supervised algorithm that needs a label to associate the shape, details, colors with a category. A dog and a wolf are very similar just across the pixels. Computer vision methods have enabled machines to be able to decipher these shapes and “learn” to classify them.

Now, algorithms, just like our eyes can identify in pictures or films, objects, or shapes. The methods are constantly evolving and perfecting to the point of reaching the so-called human level. But, there are several methods, image classification, object detection or recognition, and image segmentation. In this article, we will explore the image classification problem. The first part will present training a model from scratch, the second will present training with data augmentation, and the last transfer learning with pre-trained models.

Methods

Image Classification from scratch

Image classification can, when the volume of data you have is large enough, be done “_from scratch_”. The idea is to create a model and train it from scratch.

Like any classification problem, the data must be annotated. How to proceed when it comes to images? It’s quite simple in fact, the data of the same class must be stored in the same folder. It is necessary to take a folder per class or category considered. Like that:

> train/
      ... forest/
            ... img_1.jpeg
            ... img_2.jpeg
            ...
      ... moutain/
            ... img_1.jpeg
            ... img_2.jpeg
            ...
      ... sea/
            ... img_1.jpeg
            ... img_2.jpeg
      ...
  validation/
      ... forest/
            ... img_1.jpeg
            ... img_2.jpeg
            ...
      ... moutain/
            ... img_1.jpeg
            ... img_2.jpeg
            ...
      ... sea/
            ... img_1.jpeg
            ... img_2.jpeg
  test/
      ... forest/
            ... img_1.jpeg
            ... img_2.jpeg
            ...
      ... moutain/
            ... img_1.jpeg
            ... img_2.jpeg
            ...
      ... sea/
            ... img_1.jpeg
            ... img_2.jpeg

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