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=================================================== Required libraries for python2.7: =================================================== - caffe, h5py, scipy, scikit-image, numpy, pypng and joblib. =================================================== How to process the training dataset: =================================================== 1.) Download RAW NYU Depth v2. dataset (450GB) from http://horatio.cs.nyu.edu/mit/silberman/nyu_depth_v2/nyu_depth_v2_raw.zip 2.) Extract the RAW dataset into a folder A (name not important) 3.) Download NYU Depth v2. toolbox from http://cs.nyu.edu/~silberman/code/toolbox_nyu_depth_v2.zip 4.) Extract scripts from the toolbox to folder 'tools' in folder A 5.) Run process_raw.m in folder A 6.) Download labeled NYU Depth v2. dataset from http://horatio.cs.nyu.edu/mit/silberman/nyu_depth_v2/nyu_depth_v2_labeled.mat 7.) Download splits.mat containing official train/test split http://horatio.cs.nyu.edu/mit/silberman/indoor_seg_sup/splits.mat 8.) Make sure that labeled dataset and splits.mat are in the same folder, let's call it folder B 9.) Run get_train_scenes.m in the folder B 10.) Run split_train_set.sh in the folder B and pass it a single argument, path to folder A ('......./path/to/folder/A') 11.) Run scripts train_augment0.py, train_augment1.py, train_augment2.py in folder B 11.) Run create_train_lmdb.sh in folder B and pass it a path to caffe folder as an argument 12.) You should now have folders 'train_raw0_lmdb' (dataset version Data0), 'train_raw1_lmdb' (dataset version Data1), 'train_raw2_lmdb' (dataset version Data2) in folder B *Note: all referenced scripts can be foun in folder 'train' =================================================== How to process the testing dataset: =================================================== 1.) Download labeled NYU Depth v2. dataset from http://horatio.cs.nyu.edu/mit/silberman/nyu_depth_v2/nyu_depth_v2_labeled.mat 2.) Download splits.mat containing official train/test split http://horatio.cs.nyu.edu/mit/silberman/indoor_seg_sup/splits.mat 3.) Place all downloaded files into single folder 4.) Run script process_test.sh 5.) Run create_test_lmdb.sh and pass it a path to caffe folder as an argument 6.) You should now have a folder 'test_lmdb' in your working directory *Note: all referenced scripts can be found in folder 'test' *Note2: files crop.py, _structure_classes.py, _solarized.py come from https://github.com/deeplearningais/curfil/wiki/Training-and-Prediction-with-the-NYU-Depth-v2-Dataset