In this project, we use
the cats & dogs database:
TRAINING PHASE:
In training process, we use
a DELL Workstation. And, the
Ubuntu 14.02, the tensorflow with GPU and the Cuda,
etc. were installed on the computer.
The training code was modified
to save the
weights of each layers at the convolution
neural network as text files.
TESTING PHASE in a TARGET SYSTEM WITHOUT INSTALLING
TENSORFLOW (The RaspberryPi,
the OrangePi, the NanoPi, etc.):
The convolution
neural network (CNN) model is writen
in C language on the target systems without installing the tensorflow. These text files
obtained in the training phase were used to
determine all weights of the CNN. We use the
python language as a glue. The Python
forms the framework, and call the CNN as import C file. The python obtains images from the
camera of the computer at real-time by using the
opencv library. At below 1 second, the Python framework
made a decision by using the
camera and import CNN file.
The Following
Works were done for these purposes:
1) RGB-images
(with two-dimensional) were transfered from the python
to C, or from the C to
the Python.
2) The convolution
neural network (CNN) model is writen
in C language. The program written in C was shared among the
cores of a Embedded Linux System
by using the omp library
to increase the speed. The
CNN program written in C was
compiled on the Embedded
Linux System (The RaspberryPi, the OrangePi, the NanoPi,
etc.). The file with (.so) extansion
was called from the python
as a import file.