fMoW-final-code/README at master · jianminsun/fMoW-final-code · GitHub

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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.