How to prepare images for a training dataset?

Several tips for image data preparation for building your own AI & Machine Learning models.
Michal Lukáč, Ximilar
Michal Lukac
15. June 2017

Best practices for the preparation of image training data

Ximilar (vize.ai) offers powerful and easy to use image recognition and classification service using deep neural networks. With Ximilar App, you can train your own custom model for image recognition, online and for free. Working with custom data comes with the responsibility of collecting the right dataset. Good dataset is crucial in achieving the highest possible accuracy. Let’s break down some rules for those who are building datasets.

So what are the steps when preparing the dataset?

1. Plan and simplify

In the beginning, we must think about how does the computer see the images. It is important to understand environment, type of camera or lighting conditions. Want to use the API in a mobile camera? Aim to collect images captured by mobile phone, so they match with future images. Analysing medical images? You can get images from the same point of view, and the neural network learns nuanced patterns. Do you want to analyse many features (eg. “contains glass” and “is image blurry”)? Setup more models for each of the feature. Don’t mix it up all in one. 😉

If you are not sure, ask the support. They can provide educated advice.

2. Collect images

For all the tasks, try to get the most variable and diverse training dataset. Here are some tips:

  • get images from different angles
  • change lightning conditions
  • take images with good quality and in focus
  • change object size and distance / zoom

This is especially true for cases, when you want to recognise real-world objects. They always vary a lot in their background, image quality, lighting etc. Take this in account and try to create as realistic dataset as possible. Realistic in the way of how you are going to use the model in future. Training with amazing images and deployment with lowres blurry images won’t deliver a good performance. Don’t worry if you can get enough images, our platform is able to augment images and generate more data during the training of the machine learning model.

Working with coloured object make sure your dataset consist of different colours.

Higher diversity of the dataset leads to higher accuracy.

With Ximilar (vize.ai) the training minimum is as little as 20 images, and you can still achieve great results. However, for more complex and nuance categories you should think about 50, 100 or even more images for training. You can test with 20 images to understand the accuracy and then add more.

Sometimes it might be tempting to use stock images or images from Bing or Google Search. These will work too, mostly for simple tasks. However, you might hinder the accuracy. There are also a lot of professional image gathering services that can help you.

3. Sort and upload data

You have your images ready, and it’s time to sort them. When you have only a few categories, you can upload all the images into the mixed zone and label them in our app. For big dataset, it is best to separate training images into different folders and upload them directly to each of the category in our app.

Make the dataset as clean as possible. Skip images that might confuse you. If you are not sure about the category of a particular image, do not use it.

Think about structure once again. Many times you have more tasks you want to achieve, but you put it all in one and create overlapping categories. For such cases, it is good to create more tasks, where each is trained for a feature you want to recognise.

You can upload images via our frontend with drag and drop feature or use our REST API. You are able to set part of your dataset only for testing, in this way you can get evaluation numbers (accuracy, precision, recall) that are independent on your training dataset.

More on processing & chaining multiple AI models in the blog post about Flows.

4. Train and test your model

Now comes the exciting part! Training your own neural network and seeing the results. When you send the task to training, we split your dataset into training and testing images. This way, we can evaluate the accuracy of your model.

If you’re happy with the accuracy, you’re just a few lines of code from implementation into your app.

If you want to achieve higher accuracy, you can add more data, fix mislabelled data and retrain the model again. Ximilar App cloud platform keeps last 5 trained models per task, so you will not lose your previously trained model.


Summary

To wrap up. You will achieve high accuracy by

  • having diverse dataset
  • cleaning and properly structuring the data
  • using real images, similar to ones you will then send to the API for classification or prediction

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Following these steps, you are all set to start training and recognising right away. You can hop right in to it here. If you have any questions, please contact us.

Michal Lukáč, Ximilar

Michal Lukáč ML Engineer & Co-founder

Michal is a co-founder of Ximilar and a machine learning expert focusing mainly on image recognition, visual search and computer vision. He is interested in science, loves reading books and choking people at Brazillian Jiu-Jitsu trainings.

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