The Ultimate Guide to Synthetic Data

Synthetic Data for AI & Computer Vision

What is Synthetic Data?

Synthetic data is computer-generated data which imitates real-world data. In the context of Computer Vision, synthetic data commonly takes the form of images and video. This data is annotated, which means that objects of interest in each image or video frame are identified by a bounding box.

Generally, synthetic data has the following characteristics:

  • Automatically labeled for AI learning.
  • Generated in large quantities.
  • Realistic i.e., mostly indistinguishable from reality.

For these reasons, it is often advantageous to use synthetic data instead of real data for AI & Computer Vision endeavors.

Why use Synthetic Data instead of Real Data?

Data scientists will often turn to real data as their first dataset-of-choice. This is logical since real-world training data is generally representative of real-world production data. However, data scientists who solely use real-world data will quickly run into problems.

To start, most real-world datasets are not application-specific. For example, many AI/ML researchers use ImageNet, a vast collection of labeled images from the internet. However, since ImageNet is from the internet, it's full of exactly what you would expect: pictures dogs, cats, and other household objects. That's fine for broad scientific research, but it is not ideal for industrial uses of AI.

Application-specific datasets are much more challenging to acquire. First, data scientists must collect real-world data from a sensor, usually a camera. Then, AI/ML practitioners must annotate the data. This process involves going through each image and manually drawing bounding boxes around objects of interest. Data labeling is a time-consuming process, and is certain to take up either significant time or money.

Synthetic data offers an alternative – a cost-effective dataset that appears realistic, and is automatically labeled for AI learning. To a convolutional neural network or other AI model, a synthetic image looks the same as a real-world image. After all, it's just pixels.

Why is Synthetic Data more
cost-effective?

Think about it – synthetic data is computer-generated. That means synthetic data is created at the speed of electrons. Humans are not built for mind-numbingly tedious tasks. Fortunately, computers are great at repetitive tasks.

By doing things at the speed of computers, we remove a lot of cost from the equation. The money normally spent on data labeling can now be spent elsewhere. While synthetic data is not free, the cost is typically much less than outsourced data labeling.

Where synthetic data really shines is when your AI/ML requires a large training set. For example, many training sets require over 1M+ labeled images, especially when applying Computer Vision to video. Manually labeling 1M images is often infeasible or otherwise cost-prohibitive. However, synthetic data is easy to generate in large quantities.

Hoops Synthetic Data

How do you make Synthetic Data realistic?

Realistic data generation is a golden-goose of synthetic datasets. To produce realistic data, we use a proprietary blend of technologies, both off-the-shelf and internally developed. The result is photorealistic datasets for AI training.

Making realistic data is an art form. At Simerse, we pride ourselves on being able to generate diverse datasets to accelerate AI projects. The end-goal of any synthetic data project is to create data that successfully trains an AI/ML model.

To do this, we follow a careful process. First, we learn about the specific AI/ML application. Then, we optimize and fine-tune our technology to that problem. The ultimate result is data that is highly beneficial for AI/ML training.

Is Synthetic Data perfectly annotated?

Yes! Synthetic data is perfectly annotated for AI learning. Simerse ensures that all objects in a dataset are labeled with 100% accuracy.

When it comes to manual data labeling, human error is a non-trivial factor. Data scientists should inspect real-world datasets to ensure that annotations are both consistently and accurately applied to the dataset. If data scientists choose to outsource their data labeling, it is recommended to run regular quality checks to ensure proper annotation.

Synthetic data, however, comes with a quality guarantee. Given that applying labels to synthetic data is a matter of computer programming rather than CAPTCHA-like annotation, synthetic data users can rest assured that their data is perfectly labeled.

Is Synthetic Data faster than Real Data?

In our internal research, we found that synthetic datasets can be created 3x faster than real-world datasets, on average.

A common pitfall of AI development is relying too heavily on real training datasets, and not leveraging synthetic data. Real-world data is laborious to collect, and even slower to manually process.

When considering where to source your training data, remember to consider the total project timeline. Capturing large volumes of real-world data is not a trivial task, and mishaps with either data gathering or data labeling can dramatically extend your project's timeline.

In comparison, a large synthetic dataset can be generated in a matter of hours. Synthetic data generation enables your team to move quicker and bring an AI solution to market faster.

How does Synthetic Data help with edge cases?

Edge cases can be a gnarly problem in AI development. Typically, edge cases are rare in the real world, which means that by their very nature they are underrepresented in real datasets.

Successfully handling edge cases is one of the most important benchmarks for an AI model. The conundrum is clear: how do you train an AI model to handle a situation that rarely occurs in the real world?

Synthetic data shines when it comes to edge cases. Since we control the synthetic data generation, we can actually intentionally overrepresent edge cases in an AI dataset.

With synthetic training data, your AI model will be prepared to handle rarely-occurring yet critical edge cases when deployed in the real world.

Is Synthetic Data multispectral?

Yes! A major advantage of synthetic data is that datasets are not limited to visible light. A synthetic dataset can consist of RGB, LiDAR, Infrared, Radar, or Depth perception images.

It is well known that humans can only see visible light. That means that human data labeling is limited. However, sensors are becoming more advanced, and companies are beginning to apply AI to sensor data gathered from across the electromagnetic spectrum.

Once again, synthetic data comes to the rescue. Generated datasets can be used to train multispectral AI models for a variety of sensors.

Can you combine Synthetic Data and Real Data?

Of course! If you've already collected a real-world dataset, synthetic data can be a great complement to fill in the gaps. We recommend a technique called transfer learning, where you first train your AI model on synthetic data, and then use real-world data to fine-tune your model.

By combining synthetic data and real data, you can significantly reduce the real-world data needs of your AI project. We estimate that up to 95% of your AI training can be done on synthetic data, with real-world data constituting the remaining percentage. Ultimately, this translates to a 95% cost savings on your data labeling expenditure.

Synthetic data also works great independently of real data, and can be used to benchmark real-world datasets. Simerse excels at creating both pure-synthetic and mixed-synthetic datasets.

How much Synthetic Data can you create?

Simerse can generate an unlimited amount of data for your AI initiative. One of the most appealing aspects of synthetic data is its volubility.

Unlike real data, which is limited by the speed of human labeling, synthetic data is computer-generated, and therefore has no significant limits to scale. This is a major advantage for AI projects.

The quantity of synthetic data will depend directly on how much data you need for your AI model. For example, we can easily generate one million or more images, along with thousands of hours of labeled-video. Ultimately, synthetic data can meet the needs of your project.

Simerse also uses a combination of industry-standard and proprietary techniques to minimize dataset size while maintaining extremely high levels of quality.

Can Synthetic Data augment my existing datasets?

Data augmentation in AI & machine learning is a trending topic right now. The concept is fairly simple: data augmentation means using techniques to modify a dataset to create more data from an initial batch. However, there are challenges to data augmentation.

1) Data augmentation alone doesn't solve the data diversity problem. Machine Learning works best when data is highly diverse; simply rearranging a dataset does not always lead to a well-varied dataset.

2) Data that is augmented is still derived from real world data. Therefore, edge cases are unlikely to be overrepresented, which means that an AI model may not be sufficiently prepared to handle rare events.

3) Tabular data is different than visual data. For understanding tabular data, we recommend reading this article from MIT’s Laboratory for Information and Decision Systems.

Fortunately, synthetic data is highly compatible with data augmentation. Synthetic data can produce a high number of edge cases, while data augmentation techniques can replicate commonly-observed real world examples. When combined, synthetic data and traditional data augmentation can lead to a robust AI training dataset.

Can Synthetic Data simulate future events?

Yes! Synthetic data can be used to prepare an AI model for future events. Creating future events through simulation is one of the best ways to future-proof an AI or Machine Learning model.

Think about it: if an event hasn't happened yet, there is no real world dataset that can be used to train an AI model.

Synthetic data is highly versatile in this regard, and can be used as a hedge against future events. We highly recommend exploring synthetic data if your AI model is being deployed in critical scenarios.

Is it possible to update Synthetic Data?

Absolutely! Synthetic datasets can be re-generated on demand. If a particularly sensor resolution or modality changes, the synthetic data generation can be tailored and updated as necessary.

In contrast, real world datasets are static: the resolution of a digital image or video is generally fixed and cannot be changed. This is one of the deficits of relying on conventional data for AI learning.

Given the continuous improvements in sensor hardware, we recommend synthetic data to ensure that your dataset and AI technology is compatible in the future.

How is Synthetic Data for data privacy?

Synthetic data is excellent for data privacy! In fact, synthetic data can be generated with absolutely zero personally identifiable information (PII). By its very nature, synthetic data is not derived from real data. As a result, synthetic data is largely free from data privacy concerns.

As governments increasingly regulate data privacy and personal information, synthetic data will stay ahead of the curve. Today, regulations such as the European Union's GDPR place restrictions on the collection and storage of personal information. This poses a legal limitation to real world data collection, but not synthetic data.

When it comes to collecting training data, it is a best practice to ensure that your data is future-proof. Investing solely in real data for AI training may pose legal risks should privacy regulations continue to become more strict worldwide.

We recommend using synthetic data to stay compliant with privacy regulations, as well as to garner greater trust and transparency with stakeholders when it comes to your AI endeavor.

What industries use Synthetic Data?

Synthetic data can be applied to nearly any computer vision problem. To date, synthetic data has been used by the following industries:

  • Aerospace
  • Agriculture
  • Automotive
  • Infrastructure
  • Retail

Industry leaders are increasingly adopting synthetic data to accelerate their AI initiatives. As sensors continue to proliferate, we expect the demand for synthetic data and computer vision models to increase.

What is the future of Synthetic Data?

The future of synthetic data as a means to train AI & Computer Vision models is extremely bright. We expect that more companies will adopt synthetic data in the future, given its significant advantages over conventional datasets.

The capabilities of synthetic data generation will grow alongside improvements in AI and Machine Learning. Eventually, we expect synthetic data to completely replace real data for AI training purposes.

In our opinion, it is a prudent decision to invest in emerging synthetic data technology rather than real world data collection. Synthetic data offers a myriad of benefits that cannot be matched by conventional data alone.

At Simerse, we can help you get ahead of the curve.

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Synthetic data accelerates AI training.