Awesome
Caffe-Data-Augmentation
The original repository for Caffe, developed by the Berkeley Vision and Learning Center (BVLC) and community contributors, is at (BVLC\caffe)
This project adds a data augmentation feature to caffe, augmenting the data in 9 several ways.
The ways in which the data is augmentated is explained here:
<ol> <li>Image Translation - a random shift in a x and y axis pixels of the entire image. The shift has uniform probability between -7 and 7.</li> <li>Image Rescailing - shrinking or enalrgin the image (before cropping) by a random unifrom factor between 0.8 and 1.2.</li> <li>Horizontal Flipping - flipping the image in the horizontal axis.</li> <li>Vertical Flipping - flipping the image in the vertical axis.</li> <li>Elastic Deformation with Random Interpolation - dislocate pixels and use OpenCV interpolations method randomly.</li> <li>Color Noising - adding a small independent noise to each color channel of the image.</li> <li>Brightness Noising - adding a small noise to the brightness of each pixel.</li> <li>Small Blurring - convolving the image with small random-sized blurring kernel.</li> <li>Single Random Transformation - choosing a transformation at random.</li> <li>Multiple Random Transformations - chooses each transofrmation with probability 1/7, such that the mean is one transformation for every image.</li> </ol> The desired transformation(s) is chosen by parameter transform_type within the prototxt file for the data layer. The value of the parameter corresponds to the transform schemes described above. For example, transform_type=4 uses vertical flipping as it's transformation.This project was developed by Shani Rehana, Baruch Epstein and Shahar Katz.
The latest version for this project is rc2.