A new deep learning baseline for image classification

Quick run tutorial:

1.Input two parameters to run, the first parameter “--config_file” specifies the path of file, the second “--run_type” specifies which test to run. 2.The results will be in the folder “PCANet1_results”.

• --config_file: the path of configuration file

• --run_type: specify which test to run

• --run_type d # run on demo dataset

• --run_type t # run on tiny dataset

• --run_type f # run on full dataset

• Sample command: python experiment_PCANet1_main.py --config_files PCANet1_configs\demo.json --run_type d

Script

experiment_PCANet1_main.py #representations of Fourier,Wavelet,.....

experiment_PCANet2_main.py #representation of 2d-Fourier on different datasets

Example

Command:

• python experiment_PCANet1_main.py --config_files PCANet1_configs\demo.json --run_type d

Result(basic-demo):

X.shape: (49, 48400)
filter.shape: (2, 7, 7)
I_layer1.shape: (100, 2, 28, 28)
int_img_list.shape: (100, 28, 28)
I_layer1.shape: (20, 2, 28, 28)
int_img_list.shape: (20, 28, 28)
train_feats.shape: (100, 256), test_feats.shape: (20, 256)
PCANet
l: 2
patch_size: (7, 7)
stride: 1
block_size: (7, 7)
block_stride: 3
method: Fourier
stage: 1
reduction_method: exponent
feature_method: histogram
fourier_basis_sel: magnitude
Mean Accuracy Score: 0.85