Sep 20, 2022
How much does computer vision AI model training cost without using Synthetic Data?
Synthetic Data is an cost-effective alternative to real-world data that is used to train and improve AI models. Gartner predicts that Synthetic Data usage by 2030 will greatly surpass the use of real data. Synthetic data has many benefits, such as reducing bias in training data, improving the AI model`s accuracy, and reducing the time for data collection and labeling. This reduction of the time for data collection will also cause reducing the total cost of the project. In this blog post, we will go through an example to find out how much you can actually save up by using Synthetic Data to train your AI vision mode.
In order to train an AI model with data, you must first label the data. Data labeling refers to the process of augmenting existing raw data with information. Labels are also known as tags, which are used to give an identification to a piece of data that shows some information about that element.
In this post, we will focus on data labeling in computer vision AI model training. So, in this case, the data that is used for AI training are pictures and the process of labeling refers to adding the information on top of these pictures.
In order to label the pictures, we should draw an outline around the object in the picture. So that, the AI model will be able to distinguish the different parts of the picture. Depending on the AI model needs, we could either draw bounding boxes around the objects or for more advanced use cases we could divide the picture into different regions.
The following picture illustrates the two most common tasks in computer vision: Object detection and Segmentation.
The first step to train a computer vision model is to gather pictures for labeling. These pictures need to either be taken by a photographer or bought from stock photos. Stock photos are normally not a viable option, since during the training of a model we need pictures from a variety of angles and different lightning. Photographers need to have all the lighting and angles in mind while taking the photos and spend some time afterward to retouch and adjust them. The price per picture taken by the photographer most likely will not be under $0.40.
The second step is data labeling. Prices of labeling services for bounding box type labeling with a human labeler are usually $50 for 1000 pictures for projects up to 1 million pictures (price is based on offerings from several popular labeling services). Segmentation is a much more complicated task and therefore it is more expensive. Creating only one segment in the picture costs around $850.
For accurate computer vision projects, you will need a large number of labeled pictures. Imagine for your next project you will need 100.000 pictures. Based on previous estimations, pictures taken by a photographer will cost $40.000, labeling services for object detection will cost $5.000, and if you will need segmentation with only one segment will cost you $85.000. The total cost for the object detection project will result in $45.000, and for the segmentation, the project will be $125.000.
With the use of realistic 3D models, you can easily create synthetic data at a large scale for computer vision AI model training. Synthetic data will not only achieve great cost and time savings for your projects, but it will also reduce the risk of human error, allow you to fix the bias in your data, and much more.
Following video showcases how easy it is to generate synthetic data with our solution. Also, it showcases an example for which it is really hard to acquire huge amounts of pictures.