Augmentation

• on-the-fly argumentation is a dynamic approach where our model constructs arguments in real-time based on the specific context of a problem or conversation. This relies on dynamic reasoning, allowing the model to adapt to new inputs instantly rather than depending on a predefined set of augmented data. And our platform offers the flexibility to incorporate custom argumentation, enabling users to control the argumentation process and enhance the model’s accuracy.

• How Argumentation Techniques Improve Model Performance.

  • The three argumentation techniques—Random Flip, Brightness Delta, and Hue Delta—enhance model performance by increasing data diversity, reducing overfitting, and improving generalization. Here’s how each contributes to better results:

• Random Flip:- Random flip is a technique in which images are flipped horizontally or vertically to increase dataset diversity, You can set the probability between 0.1 and 1.0, determining how often images will be flipped during training.

• Brightness Delta:- This technique randomly adjusts image brightness within your specified range. It learns to recognize objects regardless of how light or dark an image appears. The adjustment range spans from 1 to 99.

• Hue Delta:- Hue delta introduces random color shifts to training images.