Issues while interfacing caffe with c++ or python

Question

What I have read about the tutorials is that you create your data then write the model using protobuf and then you write the solver file. Finally you train the model and you get your generated file. All this is done though command line. Now there are two questions

1) Suppose I have the generated model now how do I load a new image not in the test folder and perform a forward pass. Should it be done though command line or from some language(c++, python) ?

2) I guess above was one way of doing it. What is the best way to train the classifier (command line train/ or though coding) and how to use the generated model file(after training) in your code.

I want to interface caffe with my code but I am not able to find a short tutorial which will give me step by step on any database say mnist and the model doesn't need to be as complicated as LeNet but a simple Fully connected layer will also do. But can anyone tell me how to just write a simple code using C++ or python and train any dataset from scratch.

A sample C++/python code for training a classifier and using it to predict new data using caffe would also be appreciated.

Answer 1

Training is best done using the command line. See this tutorial.

Once you trained a model and you have a myModel.caffemodel file (a binary file storing the wieghts of the different layers) and a deploy.prototxt file (a text file describing your net), you can use python interface to classify images.

You can run python script classify.py to classify image(s) from command line. This script wraps around classifier.py - a python object that holds a trained net and allows you to perform forward passes in python.