Augmentor is an image augmentation library in Python for machine learning. It aims to make image augmentation platform and framework independent, more convenient, less error prone, and reproducible. It employs a stochastic approach using building blocks that allow for operations to be pieced together ...
Python 3D Image Augmentation for 3D Image Segmentation This is a lightweight libaray/framework containing a collection of mothods for 3D image data augmentation. Its intended usage is pair-wise (simultanous) augmentation of medical image data and their corresponding manual segmentation masks. It also...
Augmentations As usual, we are going to write our augmentation functions in python. We’ll just be using simple functions from numpy and scipy. Translation In our functions, image is a 2 or 3D array - if it’s a 3D array, we need to be careful about specifying our translation directions...
/usr/bin/python3 # HiLens Framework 0.2.2 python demo import cv2 import os import hilens...
Data Augmentation is one way to battle this shortage of data, by artificially augmenting our dataset. In fact, the technique has proven to be so successful that it's become a staple of deep learning systems.
the transformed images are generated in Python code on the CPU while the GPU is training on the previous batch of images. So these data augmentation schemes are, in effect, computationally free. The first form of data augmentation consists of generating image translations and horizontal reflections...
little computation, so the transformed images do not need to be stored on disk. In our implementation, the transformed images are generated inPythoncode on the CPU while the GPU is training on the previous batch of images. So these data augmentation schemes are, in effect, computationally free...
data augmentation. These methods effectively enriched the diversity of the training data and provided a solid foundation for training the detection model. Chen et al.39proposed a framework combining YOLOv4 and CycleGAN to improve the quality of diamond pineapple surface-defect detection. The data ...
little computation, so the transformed images do not need to be stored on disk. In our implementation, the transformed images are generated in Python code on the CPU while the GPU is training on the previous batch of images. So these data augmentation schemes are, in effect, computationally ...
We implemented and trained the network using Python 3 and the deep learning library Tensorflow (version 1.12)75 on one NVIDIA GTX 1080Ti GPU (12GB GPU memory). The code can be found on GitHub (https://github.com/EchanHe/DL_seg_avian_plumage)76. To balance the memory usage of the GPU...