How to training/testing my own dataset in caffe?

I started with Caffe and the mnist example ran well.
I have the train and label data as data.mat. (I have 300 training data with 30 features and labels are (-1, +1) that have saved in data.mat).

However, I don't quite understand how I can use caffe to implement my own dataset?

Is there a step by step tutorial can teach me?

Many thanks!!!! Any advice would be appreciated!

Solution 1:

I think the most straight forward way to transfer data from Matlab to caffe is via HDF5 file.

First, save your data in Matlab in an HDF5 file using hdf5write. I assume your training data is stored in a variable name X of size 300-by-30 and the labels are stored in y a 300-by-1 vector:

hdf5write('my_data.h5', '/X', 
  single( permute(reshape(X,[300, 30, 1, 1]),[4:-1:1]) ) );
hdf5write('my_data.h5', '/label', 
  single( permute(reshape(y,[300, 1, 1, 1]),[4:-1:1]) ), 
  'WriteMode', 'append' );

Note that the data is saved as a 4D array: the first dimension is the number of features, second one is the feature's dimension and the last two are 1 (representing no spatial dimensions). Also note that the names given to the data in the HDF5 are "X" and "label" - these names should be used as the "top" blobs of the input data layer.

Why permute? please see this answer for an explanation.

You also need to prepare a text file listing the names of all hdf5 files you are using (in your case, only my_data.h5). File /path/to/list/file.txt should have a single line

/path/to/my_data.h5

Now you can add an input data layer to your train_val.prototxt

layer {
  type: "HDF5Data"
  name: "data"
  top: "X"     # note: same name as in HDF5
  top: "label" # 
  hdf5_data_param {
    source: "/path/to/list/file.txt"
    batch_size: 20
  }
  include { phase: TRAIN }
}

For more information regarding hdf5 input layer, you can see in this answer.