io7m | single-page | multi-page | Laurel User Manual 0.99.1-SNAPSHOT
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# 1. Overview

- 1.1. Introduction
- 1.2. Features

## 1.1. Introduction

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The laurel application attempts to provide tools to assist with image captioning within the context of machine learning.
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In particular, the application is geared towards the management of smaller datasets (in the range of thousands of images) for use in techniques such as LORA training.

## 1.2. Features

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The laurel application provides the following features:

### 1.2.2. Features

- A user interface for managing images and captions for those images.
- A caption categorization system for assisting with keeping captions consistent across large datasets.
- The ability to import captions and images into a dataset from a directory hierarchy.
- The ability to export captions and images into a directory for use in training scripts.
- A persistent undo/redo system that can store every change ever made to a dataset, including the ability to effectively revert to an earlier version at any time.
- A carefully-engineered Java API for manipulating datasets; the command-line tools and user interface are thin shells over this API.
- Datasets are backed by SQLite for reliable, transactional updates, and a file format that is designed to endure for decades to come.
- Command line tools for automating operations such as importing, exporting, and interrogating metadata.
- The application is comprehensively documented; you are currently reading this documentation!

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io7m | single-page | multi-page | Laurel User Manual 0.99.1-SNAPSHOT