Image Augmentation is a technique used in computer visio.
CDAN: Convolutional Dense Attention-guided Network for Low-
Relight images to enhance their lighting and appearance
Enhance low light photos using AI
AI filter for your portraits
Enhance low light photos to make them clearer
Edit images by replacing segments and grading colors
Swap faces between images and adjust colors
ehnace your own photos based on user inputs
Enhance low-light images
Transform images based on textual instructions
Edit image brightness and apply various filters
Generate images with specific lighting effects
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.
• 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.
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.