If you are interested in photography or image processing, you might have heard of a technique called Random Gaussian Blur. This technique can be used to enhance images or create new data for machine learning applications. In this article, we will explore what Gaussian Blur is, how Random Gaussian Blur works, and where it can be applied.
What is Gaussian Blur?
Gaussian Blur is a type of image filter that is used to reduce the noise or detail in an image. It works by averaging the pixel values
Random grayscale is a technique used in image processing and machine learning that can help improve the accuracy and diversity of image datasets. It involves converting a color image into grayscale with a certain probability, which can help prevent overfitting and make the data more robust.
What is Random Grayscale?
Random grayscale is a type of image data augmentation that can help improve the accuracy of machine learning models that are trained on image data. Image data augmentation is a te
Random Horizontal Flip: A Guide to Image Data Augmentation
In the world of machine learning and computer vision, image data augmentation is an important technique used to improve the performance of image-based algorithms. Random Horizontal Flip is one such data augmentation technique that flips images horizontally with a certain probability. In this article, we'll delve deeper into what Random Horizontal Flip is, how it works, and its applications.
What is Random Horizontal Flip?
Random Hori
Overview of R-Mix
R-Mix is a data augmentation technique used in machine learning that combines two different types of Mix-up methods. Mix-up methods aim to improve the accuracy and reliability of neural networks by generating more data for the model to learn from. The two methods that are combined in R-Mix are random Mix-up and Saliency-guided Mix-up. By blending these two techniques, R-Mix produces a procedure that is both fast and effective.
What is Mix-up?
Before diving into the details
When it comes to training machine learning models to recognize images, having a diverse set of training data can be crucial for good performance. However, collecting a large and diverse dataset can be difficult and time-consuming. This is where data augmentation comes in, which is a technique used to artificially increase the size and diversity of a dataset. One popular type of data augmentation is Random Resized Crop.
What is Random Resized Crop?
Random Resized Crop is a type of image data a
Random Scaling is a technique used to modify images by changing their size in a random manner. This image data augmentation technique is used in machine learning and deep learning applications to improve the performance of image recognition algorithms. In this article, we will explore what random scaling is, how it works, and its benefits.
What is Random Scaling?
Random Scaling is a type of image data augmentation that involves changing the scale of an image randomly. This means that the size
Image data augmentation is the process of artificially increasing the size of our dataset by applying various transformations to the images. These transformations include rotation, flipping, zooming, and many more. One of these transformations called "RandomRotate" randomly rotates an image by a degree.
What is RandomRotate?
RandomRotate is a type of image data augmentation that randomly rotates an image by a degree. It is a common technique used in machine learning and computer vision for im
What is Sample Redistribution?
Sample Redistribution is a technique used in face detection to create more training samples based on the statistics of benchmark datasets. This is done by enlarging the size of square patches cropped from original images during training data augmentation.
How Does Sample Redistribution Work?
During training data augmentation, square patches are cropped from original images using a random size from the set of [0.3,1.0] of the short edge of the original images. T
What are SuperpixelGridMasks?
SuperpixelGridMasks is a term used to describe a type of data augmentation method used in computer vision. Essentially, it involves dividing an image into smaller, square-shaped segments called "superpixels". These superpixels are then labeled based on their color or texture, and can be used to create a more detailed and accurate representation of the original image.
How do SuperpixelGridMasks work?
The process of creating SuperpixelGridMasks begins by segmentin