Adding Synthetic Data to Our Dataset

Synthetic data for model training

While real data is invaluable for training and testing machine learning models, there are several reasons why synthetic data is necessary.

Limited availability of labeled data

In an ideal scenario, it’ll be optimal to have our model trained on only real data. However, we require a lot of training data to build a good object detection model. Depending on the use case, we may not have enough data for specific scenarios or rare objects, for example, detecting a fire. Moreover, collecting and labeling real-world data can be time-consuming and expensive.

Imbalanced data distribution

Real-world data is often biased or imbalanced, leading to poor model performance on under-represented classes. For example, let’s consider we want to create an object detection model to detect two classes: airplanes and UFOs (unidentified flying objects). We may have data on worldwide data on airplanes, but that won’t be the case for UFOs. Assuming that claims made by people of spotting a UFO are true, and the pictures shared by them are authentic, how many in total pictures will we be able to collect? A 100, maybe? With such little, we won’t be able to make our model ...