What is DetectionSuite?

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 the following utilities:

  • Auto Evaluator
  • Viewer
  • Converter
  • Detector
  • Evaluator
  • Deployer

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.

Creating a custom appConfig.yml

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 the following directories in /opt/datasets/: 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.
  • names: This directory will contain class names for various datasets. For example, voc.names.
  • weights: This directory will contain weights for various networks, such as yolo-voc.weights for yolo or a frozen inference graph for tensorflow trained networks.
  • eval: Evaluations path.

Once done, you can create your own custom appConfig.yml like the one mentioned.

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 ⚡️ .

Examples of input and output