Supports augmentation on multiple CPU cores. Documentation http://imgaug.readthedocs.io/en/latest/source/examples_basics.html - Quick example code to use the library. http://imgaug.readthedocs.io/en/latest/source/augmenters.html - Example code for each augmentation technique. http://imgaug.readthed...
Augmentor is an image augmentation library in Python for machine learning. It aims to be a standalone library that is platform and framework independent, which is more convenient, allows for finer grained control over augmentation, and implements the most real-world relevant augmentation techniques. ...
具体可以参考augmentation.py，另外还有通过GAN生成数据，我们使用opencv进行模拟。
[CV] Augmentation using image blending Image Blending Ref:https://www.pythonheidong.com/blog/article/286626/982d0bea20fb9cc62e57/【另外一个例子】 Ref:https://www.jianshu.com/p/49adfbe4b804 normal clone: 不保留dstination 图像的texture细节。目标区域的梯度只由源图像决定。 mixed clone: 保留d...
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...
4.1 Data Augmentation 这里对原来的样本做些简单的变换得到新的样本。这里我们用了两种方法。This scheme reduces the top-1 error rate by over 1%. 4.2 Dropout 三个臭皮匠赛个诸葛亮,显然如果我们训练多个不同模型,将这些模型结合起来可以降低 test errors。但是主要问题是训练时间太长。这里我们采用了 dropout,...
The model retraining is conducted using the small-scale subset of the SIDD training collection, employing data augmentation techniques such as horizontal and vertical flipping to enhance the robustness of the findings. Given that the SIDD test suite does not include corresponding clean images, it is...
We adopted similar online data augmentation as used with 3D RCAN11, which is a stochastic block selection process. For every training iteration, the batch size is set to four. The parameters of RLN and the size of selected blocks are summarized in Supplementary Table 3. For the comparison of...
Specifies whether to crop the images to achieve data augmentation. Type: BOOL true eval_each_category No Specifies whether to separately evaluate the model for each class. Type: BOOL false optimizer No The type of the optimizer. Valid values: ...
of data augmentation, both of which allow transformed images to be produced from the original images with very 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...