poor performance and code error

[yang53]

in use_dictnet.py
line 21: I have 1st error, I think it should be open('dict2_architecture.json')
line 26: I have 2nd error, I think it should be self.model.load_weights('dict2_weights.h5')
line 51: I have 3rd error, I think it should be z = self.model.predict_classes

in use_charnet.py
line 59: I have 1st error, I think it should be filename = '../IMAGES/Chevron.jpg'

when I run use_dictnet.py and use_charnet.py, they all get right result for Chevron.jpg, but all wrong for
CondoleezzaRice.jpg and CMA_CGM.jpg, I want to know why poor performance？have i made something wrong？

[mathDR]

Thanks @yang53 I merged the bug fixes you describe. As for the poor performance, this is an issue I am still working on. It basically has everything to do with the preprocessing of the image.

Note that the original Jaderberg network was trained with preprocessed images in MATLAB. I tried to replicate this preprocessing in numpy/scipy/scikit-image and I had is working on Ubuntu Linux, but am struggling to replicate it on MAC OSX.

Question: what operating system are you using?

[yang53]

thanks Dan，I use Ubuntu Linux too.
I run Jaderberg network in matlab, get the following result( it‘s your code result in bracket ):
image 1 :./Image/CMA_CGM.jpg
Detection with CHAR method 0.06s
Predicted text: cmacgm (coacgoe)

Detection with DICT method 0.12s
Predicted text: canoeing (cambering)

Detection with ngram method 0.05s

---

image 2 :./Image/Chevron.jpg
Detection with CHAR method 0.04s
Predicted text: chevron (chevron)

Detection with DICT method 0.10s
Predicted text: chevron (chevron)

Detection with ngram method 0.05s

---

image 3 :./Image/CondoleezzaRice.jpg
Detection with CHAR method 0.04s
Predicted text: condlleeraacee (condeeeeaaaie)

Detection with DICT method 0.09s
Predicted text: nonobservance (nonobservance)

Detection with ngram method 0.06s

---

image 4 :./Image/intro.jpg
Detection with CHAR method 0.06s
Predicted text: introducing (introducing)

Detection with DICT method 0.10s
Predicted text: introducing (introducing)

Detection with ngram method 0.05s

so I think Jaderberg matlab network is only a little better than yours。 Maybe poor relsult from model

[mathDR]

If you have MATLAB available, check the equivalence of the preprocessed images (prior to inserting into the convnet). My suspicion is that when the two models differ in output, these preprocessed images will be different.