Image augmentation library in Python-Augmentor使用心得 Augmentor是个增强图像训练数据的库,减少了使用图像库自己编写代码的繁杂工序,能够批量完成图像的旋转,放大,缩小,添加噪音以扩充数据量。接下来结合官方文档介绍下这个库和使用心得。 首先github:https:///mdbloice/Augmentor DOCs:https://...
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。
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...
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...
[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。
In contrast, image augmentation, using additional channels and subsetting models did not improve the performance of the deep learning model. The best performance overall was achieved with DeepLabv3+ and an input resolution of 618 × 410 pixels. This resolution was the maximum we could achieve...
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,...
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...
The data augmentation that they used involved cropping two parts of the CT lung images, one part undergone random cropping followed by random flipping, the other part undergone random cropping followed by colour distortion. Then, representation learning was trained to improve on the similarity score ...
On the contrary, TLBOCNN can solve the Fashion dataset with competitive performance without requiring any data augmentation techniques nor complicated network architectures. Unlike most handcrafted deep learning networks that might feature feedback or parallel connections, the optimal network architectures ...