Synthetic Data

How Synthetic Data Helps Boost Computer Vision Performance?

This article talks about how synthetic data helps boost computer vision performance and the benefits of using synthetic data.

You need a lot of data to train a computer vision model to effectively interpret its surroundings, which can strain resources. Synthetic data along with powerful tools and packages enable the developers to generate diverse datasets with randomized assets- perfect for computer vision model training.

Need for Synthetic Data for computer vision tasks

Computer vision tasks read images and videos to comprehend what they contain. The interpretation of images could be a broad analysis of the complete frame, or a detailed understanding of components such as poses. Computer vision model requires a significant amount of data to complete these tasks. Gathering massive data for this purpose could be time and cost consuming.

The data collection from real world using photography and videography has various downsides:

1.     Photography

Capturing the right shots can be quite difficult, or time consuming. For example, images for different meteorological conditions can be challenging to capture due to tough weather conditions.

2.     Videography

A real footage cannot be captured and used because of privacy issues or resource consumption. For example, shooting with actors and hardware in certain locations demand logistic planning which hikes the overall cost of a project.

After the collection of data, each frame requires manual labeling for efficient identification and accessibility. This activity is time consuming, putting a strain on valuable resources internally, or if outsourced it would add up to the cost.

With the help of synthetic data large quantities of images can be generated pretty quickly. You can modify the datasets as per your requirements i-e manipulating their lighting, objects, materials, and environments for a better and improved dataset. Synthetic data allows generation of new data easily, quickly and without any dependency on the external factors.

Challenges of a homemade solution

You need synthetic data because training a computer vision model to identify and track objects requires a large amount of data. You can have more liberty with fresh data, but it comes with its own challenges. Also, the need for synthetic data arises from improving existing computer vision performance because it requires significant amount of data for more complex scenarios.

With the help of pixel-perfect bounding boxes, you can generate balanced datasets of synthetic data, with objects in myriad positions because it only considers the visible parts of objects or a defined pattern for object-placement.

Efficient algorithms of sophisticated tools allow object detection AI training more efficiently and effectively. Being domain dependent, computer vision models are highly sensitive to background variations such as lighting and other environmental factors. With the help of technique called domain randomization, a robust synthetic dataset having high degrees of randomization can be generated. The smart tools or packages allow control of customizable components in synthetic data; like tags, scenarios, labels, randomizers, and smart cameras. This helps improve computer vision performance. It allows for an organized flow and structure, and can be used again for another perception project.

However, designing custom randomizations is not necessary most of the times because perception package contains the basic tools needed to get started.

The business impact of Synthetic Data

Numerous iterations, around 30 cycles, are required for training a model on real-world data. Each of these iterations requires data collection, annotation, training and evaluation, which take up around one week per cycle on average. Depending on the nature of project, the cost for one iteration falls from $2,000 to $5,000. This means for a 30 cycles training set, the costs can hike to $60,000–$150,000 and can take 4–6 months for a workable model.

On the other hand, using synthetic data in combination with the Perception Package, you can save time and money up to 95%.

Conclusion

With the help of efficient tools and synthetic data, you can achieve variations in data, which reduces the need for real-world data. Synthetic data enhances the performance of the computer vision models and in combination with efficient packages; they give higher-quality dataset results. Because of its benefits, synthetic data is a practical approach for business organizations and their partners. GenRocket helps you in generating robust synthetic data with variations.