For any large-scale computer vision application, one of the critical criteria to success is the quality and quantity of the training dataset required to train the relevant machine learning model.

Open-source datasets such as ImageNet are sufficient to train machine learning models for computer vision applications that do not require high accuracy or are not too complicated, But for more complex use cases, obtaining a large amount of high-quality training data can be quite challenging, such as autonomous driving, safety monitoring systems, medical image diagnosis and more. 

In this article, we take a look at how to quickly create (including collection, labelling, and quality inspection) high-quality training data sets for various computer vision scenarios.

Creating Suitable Training Datasets For Machine Learning Projects

Different types of machine learning modelling methods may use different types of training data. The main difference in data type is the degree to which it is marked. There are usually four different machine learning modelling methods in actual application scenarios:

• Supervised learning: refers to the model trained on the labelled data set.
• Semi-supervised learning: means that the model is trained on a small number of labelled data sets plus a large number of non-labelled data sets.
• Unsupervised learning: Use cluster analysis to group unlabeled data. Cluster analysis is not to respond to feedback, but to identify commonalities in the data, and react based on whether such commonalities exist in each new piece of data.
• Reinforcement learning: The model uses feedback from its behaviour and experience in an interactive environment to achieve the purpose of learning and improvement through repeated experiments.

At present, the most successful computer vision systems involve supervised learning methods, which use a large amount of high-quality annotation data for training, such as deep learning methods. The type of learning model you choose largely depends on the actual project needs and available resources, such as budget and staffing.

Although some existing open-source data sets (such as ImageNet, or Coco, etc.) can be used to train a good computer vision model. But more often, these open-source data sets cannot meet the needs of your specific computer vision application scenarios such as the sample space of the data distribution, or the fineness of the annotations, etc.

For computer vision applications to achieve satisfactory application results in actual application deployment, the key point is that the training datasets must conform to the data distribution in the actual application scenario, and be as unbiased as possible and without omissions, to avoid garbage in and garbage out.

You need to collect enough real image or video data from actual application scenarios for your computer vision application scenarios and perform high-quality, excellent annotations on these data that meet your specific application requirements. Depending on the complexity or security requirements of the solution, this may mean the need to collect and label millions of image data.

Suppose your computer vision application scenarios are widespread and do not require very customised fine annotations. In that case, you may purchase some ready-made commonly used annotation datasets for common scenarios from some data providers.

If these readily available data sets do not meet your specific application scenarios, most companies usually choose to cooperate with training data providers to collect and label the required training data sets. Such companies work out with you one-to-one guidance documents for data collection, labelling, quality inspection, and delivery according to your specific application scenario requirements, and distribute these tasks and guidance documents. This can help you develop a large number of high-quality training data sets that meet the needs of your specific application scenarios in a relatively short time.

A large and diverse training data set will enable your machine learning model to have better robustness and success rate in judging details and avoiding false positives. This is especially important for solutions such as autonomous driving training data, where it should be able to differentiate between a child playing on the street and a shopping bag fluttering in the wind.

How To Improve The Quality Of Training Data

Accurate image annotation is essential for a wide range of computer vision applications, including robotic vision, facial recognition, and other solutions that rely on machine learning to interpret pictures. It can be done by defining metadata in the form of identifiers, titles, or keywords to the pictures. In most cases, to correctly identify the subtle differences and ambiguities that may often appear in complex images (such as traffic camera reports and crowded city street photos), manual processing is essential.

There are image annotation tools in the market which use the power of artificial intelligence to significantly improve the efficiency of image annotation workers, which outlines the object. For example, if the labelling task is to mark all cars in a picture, the labelling tool will automatically form a 3D bounding box around the car. If the car shape is not entirely aligned, you only need to adjust it manually several points of the bounding box. This is much faster and more efficient than having to draw the 3D bounding box from scratch manually.

Improving the quality of the training data set also means that you must ensure that your training dataset covers all the real scenarios that you may encounter to make the computer vision adapt to the real environment. Some of the ways to enrich image data include rotating or cropping pictures and changing colours and exposure values. 

How To Avoid Label Deviation When Training Image Data

One challenge that may affect the accuracy of machine learning models for computer vision is the bias in the training data. Labelling bias is a common problem in supervised learning projects. This problem occurs when the dataset used during model training does not accurately reflect the context in which the model is to be operated. 

When collecting training dataset samples, it is essential not only to consider scenarios related to your specific project requirements but also the diversity of the real world. In other words, the distribution of training data must match the distribution of real data.

To ensure this, it is important to take into account the data distribution factors of the actual machine learning model deployment, such as seasonal and trend signals, and the geographic distribution of data sources in the training data. 

At any given time, tens of thousands of professional annotators around the world can work together, so that a vast amount of data can be collected, labelled, quality checked and delivered with high quality in a short time.

For many CV projects, companies also adopt efficient data collection, labelling, verification, and quality inspection methods and project management processes assisted by artificial intelligence and machine learning, thereby drastically improving the efficiency and quality of labellers.

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