Yelp Restaurant Photo Classification

My solution that scored 0.82246 and finished Yelp Restaurant Photo Classification on 22 position out of 355 teams (top 10%).

Implements the following pipeline:

Extract image features using four Caffe models. Results in eight 9GB image feature files (two train/test sets for every model)
Combine image features into business features in two different ways. Results in 16 feature sets, much smaller this time
Train Logistic Regression classifiers on business features via 10-Fold CV on every business feature set. Stack classifier output into single set of meta features. Train Logistc Regression on meta features and generate an intermediate submission
Train Neural Net and XGBoost on meta features, predict label probabilities, average predictions, generate submission without F1 Maximization
Adjust probabilities via Maximum Expected Utility Framework for F-Measure Maximization, generate final submission.

Caffe, deep learning framework
Scientific Python Stack (including NumPy, SciPy, Pandas. All this can be obtaned with Anaconda distribution)
XGBoost
Theano
Keras
About 100 GB of free disk space is needed for train/test images, extracted image features, model dumps.

NVIDIA GPU is not required but recommended. Extracting image features on CPU may take several days.

The training and test datasets and other data can be downloaded from here
Get pretrained Caffe models BVLC Reference CaffeNet and BVLC AlexNet as described here. Download the other two models from Places CNN project: Places205-AlexNet, Hybrid-AlexNet

Notice: customized prototxts and mean files already available in the models folder

After you downloaded and extracted datasets and models, adjust paths in paths.py and set caffe_mode (currently set to CPU)
Successively run (make sure you have enough disk space, see above):
python Stage1_ExtractImageFeatures.py
python Stage2_CreateBuisnessFeatures.py
python Stage3_BlendLRModelsCV.py
python Stage4_KerasXGBoostMEUFsubmission.py
You will get three submissions:
all_models_blendLR_CV.csv
keras_xgboost_blend_noMEUF.csv
keras_xgboost_blend_MEUF.csv

Enjoy!