|List of coordinates of galaxies and their known classification used for training, in CSV format.||Click Here|
|List of coordinates of galaxies and their known classification used for testing , in CSV format.||Click Here|
|A catalog of 111,838 galaxies from SDSS with predictions about the presence of bar, in CSV format.||Click Here|
|The Python code that uses the trained network for classifying input galaxies. (See instructions below for running the code.)||Click Here|
|The file containing the network structure for deployment.||Click Here|
|The trained Caffe model.||Click Here|
|The 'labels' file needed for running the classification code.||Click Here|
Instructions for running the code
In order to run the code, you will need to install the following dependencies.
- Python 2.7.x
- PIL (Python Imaging Library)
Once you have installed these dependencies, you should download the code, the Caffe model and the labels file given in the table above. The next step is to make a list of your files and store it in a plain ASCII file, let's call it 'imag_list'. You can then run the following command to use the neural network for classifying your galaxies.
$ python classify.py snapshot_iter_471780.caffemodel deploy.prototxt imag_list.lst --labels labels.txt
Before running the code, please be sure to understand the manner in which the network has been trained and its limitations.