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kaggle_bowl

README.md

  • 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

  • Validation

Run

sh gen_tr_va.sh train.lst

Then you will have tr.lst and va.lst as validation set list.