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Description

Deep Learning Detection Suite (dl-DetectionSuite) consists of a set of utilities oriented to simplify developing and testing solutions based on object detection.

Utilities

DeepLearningSuite

DeepLearningSuite is a tool designed to experiment upon Datasets and Networks using various FrameWorks. Currently it has 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 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 ⚡️ .