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Change the lighting in a photo
Image Augmentation

Image Augmentation

Image Augmentation is a technique used in computer visio.

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What is Image Augmentation ?

Image Augmentation is a technique used in computer vision to artificially increase the size of a training dataset by applying transformations to existing images. It helps improve model performance by exposing the model to a wider variety of scenarios, reducing overfitting, and enhancing generalization. With features like flip, rotate, brightness adjustment, and crop, this technique is essential for training robust machine learning models.

Features

• Change Lighting: Adjust brightness, contrast, and saturation to simulate different lighting conditions. • Geometric Transformations: Rotate, flip, and crop images to create diverse perspectives. • Noise Addition: Add random noise to images for robustness against real-world variations. • Color Jittering: Modify color balance to mimic real-life camera inconsistencies. • Scaling: Resize images to test model performance at different resolutions.

How to use Image Augmentation ?

  1. Import the Image: Load the image or video you want to augment.
  2. Apply Transformations: Use tools to flip, rotate, adjust brightness, or crop the image.
  3. Preview Changes: Review the augmented image to ensure desired results.
  4. Save Output: Export the transformed image in your preferred format (JPEG, PNG, etc.).

Frequently Asked Questions

What is Image Augmentation used for?
Image Augmentation is used to enhance dataset diversity, improving model generalization and reducing overfitting in machine learning tasks.

Can Image Augmentation be applied to videos?
Yes, Image Augmentation techniques can be applied to video frames, enabling similar benefits for video-based machine learning models.

What file formats does Image Augmentation support?
Image Augmentation typically supports common formats like JPEG, PNG, and BMP, depending on the specific implementation.

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