Since we use Darknet, there are special configurations needed. After having cloned the vision_dev repository, add the following line to your .bashrc, replacing the dummy path with the path to the vision_dev/sloth directory so that sloth can create darknet files.

export PYTHONPATH=/path/to/vision_dev/sloth:$PYTHONPATH

When attempting to use tagged images with darknet, the annotation file will need to be in the correct format. To convert to this format, use the following command

sloth convert <original file> <new_filename>.darknet

This will ask sloth to convert an annotation file in one format to the correct *.darknet format.

Once the file is in this format, you will need to run the `sloth_to_darknet.py` script which will generate the multiple files darknet requires based upon the compact information in the *.darknet file.

The following is an example command to generate said files.

./sloth_to_darknet.py -f /path/to/label/file -o /path/to/annotation/dir/ (optional)[-t training_list_filename.txt]

We use the download_data.sh and download_data_current.sh scripts from the vision_dev repository to get data from the server. Make sure that you always do a git pull before getting new data because the scripts change frequently.

First claim one or more batches to label from the current spreadsheet of data. The current spreadsheet is located here. Note which numbers you want.

Once you have claimed some batches run the following command in vision_dev to download and extract the data:

./download_data.sh bag_prefix_0 ## ## ##

For example if you want batch numbers 00 04 and 12 of the path_marker batch you would run:

./download_data.sh path_marker_0 00 04 12

Before downloading the data it will ask you for a username and password, please ask in either the it or general channels on slack