• Resize all image to 48 X 48

mkdir /home/cxxnet/example/kaggle_bowl/data
python gen_train.py /home/data/bowl/train/ /home/cxxnet/example/kaggle_bowl/data/train/
python gen_test.py /home/data/bowl/test/ /home/cxxnet/example/kaggle_bowl/data/test/

• Generate img list

python gen_img_list.py train /home/data/bowl/sampleSubmission.csv data/train/ train.lst
python gen_img_list.py test /home/data/bowl/sampleSubmission.csv data/test/ test.lst

• Generate binary image file First build im2bin at ../../tools, then run

../../tools/im2bin train.lst ./ train.bin
../../tools/im2bin test.lst ./ test.bin

• Run CXXNET

mkdir models
../../bin/cxxnet bowl.conf

It take about 5 minute to train a deep conv net model on Geforece 780

• Run Prediction

../../bin/cxxnet pred.conf

It will write softmax result in test.txt

• Make a submission file

python make_submission.py /home/data/bowl/sampleSubmission.csv test.lst test.txt out.csv

• Submit out.csv, you will get a result of 1.382285