Comments (8)
caffe环境上和warpctc-caffe没有区别,网络框架是 cnn-lstm-ctc,warpctc-caffe是 lstm-ctc,所以 tree/master/src 是没有区别的
from crnn.caffe.
哦,谢谢,好像warpctc-caffe的输入LSTM的就直接是原图哦,那你这个网络结构和
https://github.com/dlunion/CaffeLSTM-OCR 的识别验证码的差不多,不过我现在在用https://github.com/dlunion/CaffeLSTM-OCR 做,它那个项目输入LSTM的没有像你一样有个Reshape层,但是也是能收敛的哦,你具体可以看下 dlunion/CaffeLSTM-OCR#1 我的问题哦
from crnn.caffe.
Reshape层只是reshape对网络效果没有影响。这个是cnn处理图像得到特征向量输入到LSTM
from crnn.caffe.
你这个输入LSTM的Shape是 32x64x512 (time_step:32 batch_size:64)
CaffeLSTM-OCR 的输入LSTM的Shape 19 1 512 8 (time_step:19 batch_size:1)
https://zhuanlan.zhihu.com/p/28054589 按照这里介绍RNN结构的,你的这个输入xi是1x512的,而CaffeLSTM-OCR 的输入是1x512x8的? 还是就是1*512的(后面8的信息没了)? 不知道LSTM的输入到底是需要什么Shape哦。
from crnn.caffe.
那输入LSTM的只是是向量? 2D的矩阵不行的吗? 还有你做车牌识别的话 time_step是设置了为32喽?我现在在搞银行卡号识别,time_step设置八九十收敛很慢,设置四五十好像识别率又不是很高
from crnn.caffe.
因为某个维度为1,就可以把这个维度删掉了,我的reshape做得是这个工作。cnn的输出是64x512x1x32(Batch_sizie x num_output x h x w)
from crnn.caffe.
那假如cnn的输出是64x512x4x32(Batch_sizie x num_output x h x w)
,那经过permuted_data层输入LSTM的是32x64x512x4,这样也是可以的吗?
from crnn.caffe.
我觉得可以,你可以试试。
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Related Issues (20)
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