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 understand about UFOs.

How does synthetic data help improve object detection?

Synthetic data enhances performance by providing additional diverse training samples, which helps overcome the limitations of real datasets, thereby improving the model’s generalization and reducing overfitting. It can augment the training set, leading to better object detection accuracy, especially in scenarios where real annotated data is scarce or limited. Here are some advantages of using synthetic data in object detection:

• Augmenting the training dataset: We can increase the variety of samples by adding synthetic data to the training dataset and improving model generalization.

• Addressing data imbalance: Synthetic data can be generated for underrepresented classes to balance the dataset and reduce bias in the model's predictions.

• Domain randomization: Synthetic data helps in domain randomization to add the variations present in real-world data. This means that the parameters, such as lighting, pose, object textures, etc., are randomized in non-realistic ways to force the model to learn the essential features of the object.

• Scalable: Synthetic data is scalable and cheap to generate because we do not need to manually annotate our dataset.

Training strategies with synthetic data

• Training on just synthetic data

• Training on real and synthetic data

• Train on synthetic data and fine-tuning the model on real data