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Deep Learning Detection Suite (dl-DetectionSuite) consists of a set of utilities oriented to simplify developing and testing solutions based on object detection.
DeepLearningSuite is a tool designed to experiment upon Datasets and Networks using various FrameWorks. Currently it has following Utilities:
Every Tool in DeepLearningSuite requires a config file to run, and currently YAML file format is supported. See Below on how to create a custom Config File.
Each tool may have different requirements for keys in Config File, and they can be known by passing the --help flag.
It is recommended to create and assign a dedicated directory for storing all datasets, weights and config files, for easier access and a cleaner appConfig.yml file.
For Instance we will be using /opt/datasets/ for demonstration purposes.
Create some directories in /opt/datasets/ such as cfg, names, weights and eval.
Again, these names are temporary and can be changed, but must also be changed in appConfig.yml.
cfg: This directory will store config files for various networks. For example, yolo-voc.cfg [2].
names: This directory will contain class names for various datasets. For example, voc.names [3].
weights: This directory will contain weights for various networks, such as yolo-voc.weights [1] for yolo or a frozen inference graph for tensorflow trained networks.
eval: Evaluations path
Once done, you can create you own custom appConfig.yml like the one mentioned below.
datasetPath: /opt/datasets/
evaluationsPath: /opt/datasets/eval
weightsPath: /opt/datasets/weights
netCfgPath: /opt/datasets/cfg
namesPath: /opt/datasets/names
inferencesPath: /opt/datasets
Place your weights in weights directory, config files in cfg directory, classname files in names. And you are ready to go.