How To Build an Image Classification Dataset?

Distinguishing between cats and dogs sounds pretty easy, doesn't it? However, this is not the case for machine learning algorithms and artificial intelligence.

In order to distinguish and classify between objects and visual data, algorithms need large amounts of data sets. You can think of these data sets as nutrient sources for algorithms. In this article, we will focus on how you should feed algorithms for "visual classification" and how you can build data sets for the algorithm.  

How To Approach an Image Classification Dataset

You have visual data that you need to classify, and this data is important for your business and its development. While there are algorithms that can classify this data, you may need personalized data sets for accurate classification. So, how do you build a data set for visual classification?

In order to answer the question in detail, let us start by explaining the initial concepts. The answer to the question "What is image classification?" is a good place to start. 

Image classification refers to the ability of computer vision-based artificial intelligence to categorize images in a contextualized way. The "context" here goes beyond the relationships between pixels.

Let's come to another concept and look at what datasets are. A dataset consists of many "data points." Each data in a dataset is a "data point," and when these data points are combined, datasets are formed. These datasets are used to learn algorithms.

How To Create Datasets

First of all, there is an important point to be careful about. The better the quality of your dataset, in fact, your individual data points, the better your algorithm works. Your decision-making processes will speed up and improve at the same rate.

When creating a dataset, you need to think about tags, and tags are what your algorithm will learn and will classify images accordingly. Remember, artificial intelligence learns with data, and you create that data.

We said you need to pay attention to the labels, and there are three distinct things you need to pay attention to in terms of how to choose labels and what to consider.

How detailed labels do you want?

For example, you have a stationery shop. You design an algorithm to classify the pens in this stationery shop. We know that we need to think of labels for the algorithm. But how detailed will you choose the labels? That is, will the pens in your inventory be sorted only by brand or by both brand and color? Or much more granularly by nib type? The first step is to identify these labels.

How many labels do you need?

For example, you have decided to classify your pens by brand only. If you want to classify all ten brands, you must prepare labels accordingly. On the other hand, the number of labels should always be greater than 1! Let's say you only want to categorize brand X; then you should always include a second label, "not X.

Which part of the images do you want the artificial intelligence to identify?

This is a very important point. Because you need to build your data set and choose data points accordingly. For example, when you want to classify your pens, do you want to use photos where you can see the whole body of the pens or just the tip, for example? This is a very important step of the data set.

When working on your tags, please remember: that artificial intelligence does not seem like we humans do. For artificial intelligence, making sense of images differs from capturing their meaning; it perceives images as numbers and predefined tags and classifies them accordingly. Therefore, preparing your tags in a way that artificial intelligence understands is vital for your algorithm to work properly.

Let's take a closer look at how artificial intelligence sees images. Knowing this will help you understand how and how much data you need to develop artificial intelligence algorithms. There are several problems you need to overcome to train artificial intelligence algorithms. Let's examine them first, then let's look at how much data you need to train your algorithm.

Semantic Gap

Let's take a simple example. It is quite easy for us to distinguish between an image of a bird and a butterfly. The creatures we see in the two images are different from each other. But this is not the case for artificial intelligence. Computers perceive both images as a group of numbers. These numbers are the values of the pixels that make up the digital image.

This difference between how humans see images and how artificial intelligence perceives images is called the "semantic gap."

Variation

Another problem you need to overcome to train your artificial intelligence visual classification algorithm is the problem of diversity. You may have many images of the objects you want to classify, taken from different angles and in different lighting. Therefore, for your algorithm to work well, all these angles need to be introduced to the algorithm. So the number of data you need increases.

Now let us come to the question that is related to these problems:

How much data do you need?

It will be clear from what we have said so far. The more data you have, the better your algorithm will work. Unfortunately, it is not possible to say that you need "only this much" or "minimum X pieces but maximum Y pieces" of visual data for any algorithm. Nevertheless, there are some points you should pay attention to in this regard.

• First of all, it is possible to say that you need at least 100 visual data for each category. The richer your data set, the better the artificial intelligence will learn, but it is still important to exceed the lower limit of 100.
  

• Secondly, if your categories are divided into subcategories, you must have added at least 100 data points for each subcategory.
  

Please note that the 100 data point limit is only a lower limit. It is certain that a fast and reliable algorithm will need much more data than that. Let's say it again: Artificial intelligence thrives on data.

Creating A Diverse Training Dataset

We will summarize all the diversity and rich data set points mentioned above under this heading. We mentioned that you need to create a very diverse dataset. We have also mentioned several times that artificial intelligence learns from its data. Let's explain what we mean by a rich dataset with an example.

Let's continue with our stationery example. For example, you want to classify only pens of brand X and pens of brand Y. It is quite straightforward. If you feed your algorithm with only certain photos of brand X pencils and brand Y pencils, your algorithm will be largely inaccurate.

To explain in a little more detail, if you use only blue-colored X brand pens and red-colored Y brand pens as visuals, your algorithm cannot make a healthy visual classification between brands and may even mislabel red-colored X brand pens.

For this reason, you should use visual data of many different models and colors of pens from brand X, and the same is true for brand Y. In this way, you create a diverse data set.

Beyond Label: Quality and Features of Images

You have set your labels correctly and in detail, and your data is quite diverse. Is that all there is to it? No, of course, you should also pay attention to the features and quality of the images you use. Here are the points you should pay attention to:

• Use images taken from various angles
• Pay attention to proximity and distance characteristics
• Make sure objects are clearly visible
• Pay attention to the light, use images taken in different lighting
• Use high-quality images
• Use only a specific size. For example, only collect 300x300 images.
  

Summary

If you want to create a dataset for visual classification, you need to pay attention to the variety, quality, and quantity of your data. 

Artificial intelligence algorithms learn with data, and they need the right data to work correctly.

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