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# fMoW: Functional Map of the World This code was developed by Jianmin Sun. ## Dependencies The following libraries were used for training/testing the deep learning models: Mxnet 0.11.0 Keras 2.0.8 Tensorflow 1.3.0 fMoW/baseline from(https://github.com/fMoW/baseline) ## Dataset The following directory structure was used for training/testing: ``` fmow_dataset/ train/ airport/ airport_0/ airport_0_0_rgb.jpg airport_0_0_rgb.json ... airport_0_5_rgb.jpg airport_0_5_rgb.json ... ... zoo/ zoo_0/ zoo_0_0_rgb.jpg zoo_0_0_rgb.json ... zoo_0_8_rgb.jpg zoo_0_8_rgb.json ... test/ 0000000/ 0000000_0_rgb.jpg 0000000_0_rgb.json ... 0000000_5_rgb.jpg 0000000_5_rgb.json ... ``` ## Results Format This code will output txt files in the format required by Topcoder, where each line contains comma-separated values of the bounding box ID and a string containing the category. ## Running the Code To first prepare the dataset for training and testing, prepare the RGB-only version of the dataset in ./data: ``` docker build -t fmow . nvidia-docker run -v ./data/:/data -it fmow ``` for training (whole processes will take over 100 hours on g3.16xlarge) baseline each epoch takes about 7 hours, total 6x7 hours needed three mxnet models with eight epoch and one with six epoch mxnet model each epoch takes about 2.2 hours, total 2.2x8x4 hours needed bash train.sh /data/train bash test.sh /data/train /data/test /work/out.txt model Our best performing model is the CNN with meta data approach, which sums predictions over each temporal view and then takes an argmax. ## License The license is Apache 2.0. See LICENSE.