Why You Should Utilize Hybrid Data
A hybrid synthetic data approach is best implemented when some real data exists. This is when part of the dataset is generated from supposed distributions and other parts from actual data. This best of both worlds approach leads to unmatched, rapid datasets for your business.
Using synthetic data with a small amount of real data can be 10x faster than collecting and annotating thousands of real images. See model improvements in weeks not months
95-99% Rapid Training Data
Datasets that are 95-99% synth with a small fraction of real data can outperform training on real data alone
Started With Just 100 Images
Starting with just 100 real images is often enough to reach strong performance when combined with 10Ks of synthetic training image data.
Remove Data as a Blocker & Get Back to Building
What is Hybrid Data?
Hybrid data is data that’s created based on mathematical algorithms (synthetic data) and also real-world events (manual data). This makes it an efficient and reliable alternative to standard data.
Benefits of Hybrid Data
- Producing hybrid-synthetic data takes less time and is highly cost-effective
- Reduces the constraints on obtaining difficult-to-retrieve or tightly regulated data
- Has the ability to be shared and used across industries or with colleagues faster
- Use it to train & pre-train machine learning methods with large data repositories
Each SBX dataset is the product of iterative testing and optimization to achieve the best performance on real-world data. This way, you can skip costly hardware setup, time-consuming data collection, data annotation, and data cleaning.