How Gene­rati­ve AI Trans­forms Com­pu­ter Visi­on Tra­ining

Computer Vision (CV) Solutions are growing at a rapid pace and are used across industries to monitor for critical events, respond to them instantly, and send updates to operators. It is important to remember that Computer Vision models are only as good as the data that is used to train them. While real-world data is great for training CV models for specific purposes, sometimes this real-world data does not exist or is hard to come by. This article explores why this gap in data exists and how Generative AI is being used to build out datasets for more effective model training.

Often, there are specific situations that make data collection difficult. Rare, risky, or unpredictable events, such as natural disasters or workplace emergencies, do not provide us the opportunity to collect enough data to adequately simulate the scenarios. Other collection issues can stem from the location of the collection. For example, remote locations can make data collection difficult, especially when coupled with harsh conditions. 

Generative AI offers a practical solution. By creating synthetic data that accurately reflects real-world conditions, it can fill dataset gaps, simulate rare scenarios, and accelerate model training, all without the constraints of physical data collection.

Additional examples where data may be challenging to collect

1. Monitoring distant oil rigs
2. Performing pipeline inspections at extreme sea-depths
3. Inspecting equipment in uninhabitable workplaces
4. Forest Fire or Flooding Event that occur at unpredictable intervals
5. Workplace emergencies such as factory fires, spills, or explosions

 

In these cases synthetic datais a practical alternative. Instead of spending substantial resources trying to gather and label large volumes of data, we can generate them with AI models.

What is synthetic data?

In the field of computer vision, synthetic data includes images and videos that simulate real-world scenarios. Modern State of the Art (SOTA) models such as diffusion models, transformers and other generative algorithms can produce realistic images or videos at scale. These methods offer control over various parameters such as the number of objects, lighting or weather conditions, and camera angle. As a result, we can generate large quantities of diverse, realistic data.

In addition to replacing real data, synthetic data is also valuable for expanding limited datasets, ensuring all classes are better represented, and introducing rare or difficult scenarios that are hard to capture in real life.

Currently four main approaches are commonly used to generate synthetic data:

1. Prompt-based generation using powerful pre-trained models.
2. Inpainting and editing of existing images.
3. Fine-tuning pre-trained models on a specific domain.
4. Training custom generative networks from scratch.

1. Prompt-based generation with pre-trained models

Modern text-to-image models like Stable Diffusion and Midjourney, generate detailed images from text prompts. To build a synthetic dataset, describe the desired image (or video) specifying elements such as the number of objects, lighting, weather, camera type or aspect ratio, and generate images with thousands of possible variations.

This prompt-based approach is relatively easy to set up, fast, and scalable, requiring no model training and offering control over content diversity. 

However, it’s not always as simple as providing a general prompt. The results depend heavily on how the prompts are phrased, and creating precise descriptions that consistently produce the required scenes is challenging. The synthetic images may also lack realism, leaving a “domain gap” between synthetic and real images. Also, generating many images can be computationally and financially costly. Finally, some publicly available solutions are under relatively restrictive licensing that might not allow using the generated data for training other AI. 

2. Inpainting and editing

Editing involves using an AI model to modify specific parts of a real image. It’s a powerful way to insert objects that are expected in a particular location but may be missing from the original dataset.  Combining this with inpainting techniques allows edits to be made within specific regions of the image. This not only maintains control over the scene but also provides corresponding labels, since the edited areas are known, making the method even more valuable and useful.

This method offers high controllability, with the ability to specify exactly where and what to edit. It results in labelled images (since the changed region is known) and is excellent for data augmentation. However, the synthetic additions may appear unrealistic. Inpainting is therefore best suited for expanding existing datasets rather than creating completely new ones from scratch.

3. Fine-tuning pre-trained generative models

When working in a specialized domain (for example, fisheye cameras, unusual locations or rare object classes), general pre-trained models may not produce realistic enough images. A solution is to fine-tune an existing SOTA model with a small dataset representing desired domain.

Fine-tuning improves quality in specific domains and still allows scalable data generation. Yet it requires an initial dataset and training time, which increases costs. It may not generalize beyond the trained domain, but for a specific use case with lack of data it may be a powerful way to produce high-quality synthetic data.

4. Training a custom generative model

The most advanced approach to synthetic data generation is to build and train a custom generative model that will work on a specific task. This method provides full control over the output and can even outperform fine-tuned pre-trained models, especially when the target domain is highly specialized or underrepresented in public data.

However, developing such a model comes with significant challenges - it requires large, high-quality training datasets, computational resources and long development cycles.

Summary of pros and cons of the 4 approaches

Synthetic data is no longer just an experimental concept. It is becoming a practical way to solve some of the hardest problems in AI training. Instead of spending months chasing rare examples or risking work in dangerous environments, teams can now create what they need on demand.

Whether you start with simple prompt-based tools or build a model from the ground up, the result is the same: more data, more variety, and faster progress. For anyone working at the edge of what’s possible with AI, generative methods are no longer a nice-to-have, but a way to move ahead while others are still waiting for the right data to appear.