Data Augumentation

Any training - be it classification or object detection - requires an appropriate dataset. The accuracy of the model depends on it. Data augmentation is a process aimed at increasing the amount of data and thus the quality of the dataset.

This is usually done by adding slightly modified copies of already existing data, or by creating synthetic data based on existing data.

OSAI Dataset Manipulator

Many frameworks provide data augmentation functions, but it is a blind process, meaning that the user is unable to verify it.

The OSAI Dataset manipulator gives more control over this process. Thanks to cloud-based operations performed by our servers with powerful GPUs, you can increase the value of the dataset in training in a controlled way by manually setting the data augmentation parameters.

To access the Dataset Manipulator, press the Dataset manipulator button in the detailed Datasets view.

For a visual guide, check out the tutorial on our Youtube channel:

Types of data augmentation

The dataset manipulator creates a new, larger dataset by adding slightly modified copies of already existing images. Copies are created by applying a selection of geometric operations to the source image.

By default, the operations are performed in the order specified below. You can change the order by using the arrows next to each operation.

There are currently 3 operations supported in the manipulator:

• Rotation
• Brightness and Contrast
• Resize

Each of the operations will be described in a separate article.