about the input shape about crnn-with-stn HOT 11 OPEN

@sbillburg 哈哈哈，谢谢你了。我觉得你加了stn效果并不比没加stn效果好的原因是stn加在了后面，如果字符行本身旋转角度不大，那么其实形变比较小，后面的特征图，特别是经过了maxpooling的特征图，的特征都是经过了提炼的，你再去stn仿射变换可能效果不如直接在输入的时候做stn效果来的妥当。

from crnn-with-stn.

The input size is set by you before starting the training, and it's fixed. Once you train a model in one input shape, than rest inputs should be in the same size, including training dataset and test dataset.

My method is, set a aspect ratio like width:height = 5:1, and only a few inputs are bigger than this ratio, I resize them to 5:1. The neural network will learn features from these resized images, and if a image is so long, it will contains some features that is unique and good for recognize.
For those images which are smaller than this ratio, I add vain block(a pure black RGB(0, 0, 0) image) on both side of the image. Or say, generate a pure black image in 5:1 aspect ratio, then put the input image whose aspect ratio is smaller than 5:1 into the center of the black image.
You can find my method in the CRNN-with-STN/Batch_Generator.py, line38~line44.

My statement maybe nor clear, if you still get any question, please tell me. My English is not very good, but I'd love to help you.

from crnn-with-stn.

@sbillburg thanks a lot! I have got your point.

from crnn-with-stn.

看了一下才发现您是国人，那我就直接再用中文给你说一遍了。
输入长宽比不一样，在resize以后确实会影响识别结果。

所以对我来说，我的思路就是尽量少的去resize。比如我设定一个宽高比5:1， 然后在数据集里生成训练batch的时候，把所有宽高比高于5:1的图片（说明图片很宽，横向很长）直接压缩为5:1，虽然会有图像上的损失或者说失真，但是如果宽高比很高，就说明单词很长，特征很明显，对于网络来说也不难识别了。

对于长宽比小于5;1的图片，说明其宽度较窄，我会在其两遍加上纯黑色的色块，生成一个5:1的图像，原始的图像长宽比并没有改变，而是靠额外的拼接使得图像达到了需要的比例。纯黑色的色块对于网络来说也会学习为‘什么都不输出’，所以不必担心识别错误的问题。

相关的实现方法在CRNN-with-STN/Batch_Generator.py, line38~line44 可以看到，如果您还有不明白的地方可以直接问我或发邮件。

from crnn-with-stn.

CRNN-with-STN/Batch_Generator.py里面的38行
if (img_size[1]/img_size[0]*1.0) < 6.4:
要加个括号
if (img_size[1]/(img_size[0]*1.0)) < 6.4:
76行类似。

from crnn-with-stn.

CRNN-with-STN/Batch_Generator.py里面的38行
if (img_size[1]/img_size[0]*1.0) < 6.4:
要加个括号
if (img_size[1]/(img_size[0]*1.0)) < 6.4:
76行类似。

Can you tell me the difference? It seems the same in Python3 with or without the parentheses

from crnn-with-stn.

Python3没有问题，Python2的时候会有区别，习惯上加个括号比较好

from crnn-with-stn.

想问下，STN加在batchnorm_7这个位置，有什么论文或者理论依据吗？？

from crnn-with-stn.

想问下，STN加在batchnorm_7这个位置，有什么论文或者理论依据吗？？

没有，STN整个部分相当于一个模块，我只是加在了CNN和RNN之间，你可以把这一模块放在网络的任意位置，说不定可以取得更好的效果。本项目只是对于CRNN的Keras实现，以及STN的一些尝试。

from crnn-with-stn.

在调用loc_net函数时报错
，请问如何解决，谢谢！

from crnn-with-stn.

from crnn-with-stn.