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The 6 steps to create your own image recognition system

Artificial intelligence, especially image recognition, will soon have a prominent place in our daily lives. The possibilities offered by this technology are considerable, as it will improve existing services and create new ones, stimulate new economic opportunities and thus redefine the standards of our industries.

Image recognition already has many uses in everyday life, and in a wide variety of domains. For example, it can make your digital camera smarter or improve the quality control of consumer products on production lines. The possibilities induced by this technology are immense, that’s why we have built for you a 6-step guide in order to create your image recognition project and allow you to redefine the standards of your activity.

1- Design your project

Before thinking about the results, it is necessary to establish the clear perimeter of your project. This will reduce the risk of errors, save your resources and move forward more efficiently. To do this, you need to ask yourself specific questions such as: 

  • What kind of images do you want your artificial intelligence to process?
  • What task would you like your AI to perform?
  • How many concepts do you want to identify?
  • Do you need to locate objects?
  • Do you need to follow them?
  • What kind of results do you want to achieve?

Our guide is here to help you answer these questions.

2- Collect your data

In order to train your image recognition system and make it as efficient as possible, you need to build a data set. The data set, composed of photos and videos, will allow your system to learn how to identify the concepts necessary for your project, and thus gain in efficiency. The type of data you need to collect depends entirely on the nature of your project. Nevertheless, keep in mind the four main factors that should guide you when selecting your data: quantity, accuracy, diversity and quality.

3- Build a good dataset

Collecting data alone is not enough, the important thing is to annotate it in order to teach your artificial intelligence to recognize your concepts. To do this, it must be told what is and what is not present in a picture. This is called “labeling” images. There are different types of labels with various degrees of accuracy: 

  • Tags – a general annotation of what is present on the image.
  • Bounding boxes – rectangular frames around the identified concepts in order to locate them.
  • Lines and polygons precisely matching the shapes of the concepts in the image.

Labeling methods vary depending on the task you chose in the first step. The more precise the labels, the longer the annotation of the images.  Find out about the different ways to label your dataset in our guide to help you to create your image recognition system. 

4- Train you model

To ensure that your model learns how to perform the task you need it to do, you must provide it with a labeled input data set that will serve as an example. 

For example, say you want to build a Computer Vision system that can distinguish cars from trains. A data set of images of cars and trains, annotated with their characteristics, must be submitted to it. By being exposed to this information, the algorithm will learn to recognize these characteristics. Once sufficiently trained, he will be able to distinguish between cars and trains by producing a prediction. 

5- Measure your performance

Once your model has been trained and specialized in the task at hand, you need to know its efficiency and improve its performance. Various indicators are presented in more detail in our guide. These tools allow you to know the rate of images well classified by your artificial intelligence, the degree of difference between its analysis and the handwritten annotations but also the labeling errors it makes. All these indicators allow you to understand the performance of artificial intelligence and focus on certain points of failure.

6- Deploy your model

That’s it, your model is trained and you can now put it into production. The choice of hardware on which your image recognition system will run depends on the tasks you want it to perform. If the performance and speed of your model are a priority, during real-time tasks for example, then it is advisable to invest in a GPU (Graphics Processing Unit). More efficient, they are however more expensive to buy and more energy-consuming. If, on the contrary, speed is not crucial, investing in a CPU (Central Processing Unit) is sufficient. The final step in the construction of your image recognition system is its deployment, a final phase detailed in our guide

Conclusion

So now you know the 6 steps to follow to create your image recognition system. Keep in mind that this is a simplified version of the process. If you want to know the details, tips, best practices and issues to look out for when developing a project, feel free to download our White Paper dedicated to this topic.

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Data Drift or the Nightmare of Artificial Intelligence in Production (Part 1)
Lean AI – The secret to developing Computer Vision on an industrial scale
La data drift or how to regain control of your AIs in production (Part 2)

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