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  • Data Preparation
  • Overview
  • Data Directory
  • Preparing Your Data
  • LuxonisDataset

Data Preparation

Overview

LuxonisTrain supports multiple methods for loading data, providing flexibility to use different dataset formats. Here are the supported options:
  1. Data Directory: Use a data directory formatted in one of the supported structures. For more details, refer to the LuxonisParser.
  2. LuxonisDataset Format: Utilize an existing dataset in the custom LuxonisDataset format. For further guidance, see the LuxonisDataset documentation.
  3. Custom Loader: Implement a custom loader to meet specific data handling needs. To learn how to create and use custom loaders, visit the Customizations section.

Data Directory

Preparing Your Data

To use the LuxonisParser tool, you need to prepare your dataset in the following structure:
  1. Organize your dataset in one of the supported formats.
  2. Place your dataset in a directory accessible to the training script.
  3. Update the dataset_dir parameter in your configuration file to point to the dataset directory.
The dataset_dir can be one of the following:
  • Local path to the dataset directory.
  • URL to a remote dataset: The dataset will be downloaded to a "data" directory in the current working directory.
Supported URL protocols:
  • s3://bucket/path/to/directory for AWS S3
  • gs://bucket/path/to/directory for Google Cloud Storage
  • roboflow://workspace/project/version/format for RoboFlow
    • workspace: Name of the workspace the dataset belongs to.
    • project: Name of the project the dataset belongs to.
    • version: Version of the dataset.
    • format: One of coco, darknet, voc, yolov4pytorch, mt-yolov6, createml, tensorflow, folder, or png-mask-semantic.
Example:
Yaml
1loader:
2  params:
3    dataset_name: "coco_test"
4    dataset_dir: "roboflow://team-roboflow/coco-128/2/coco"

LuxonisDataset

To use the LuxonisDataset as a source of the data, specify the following in the config file:
Yaml
1loader:
2  params:
3    # Name of the dataset
4    dataset_name: "dataset_name"
5
6    # Storage type: one of 'local' (default), 's3', or 'gcs'
7    bucket_storage: "local"
To inspect the loader output you can use the inspect command. The inspect command shows the images and their corresponding annotations in the dataset.
Command Line
1luxonis_train inspect --config configs/detection_light_model.yaml