# eVision

> eVision 26.10.0-beta.2.dev documentation


2026, Euresys

## Pages in this subsection

- [Workflow](index.html.md): Overview of the Deep Learning Studio workflow, from choosing a tool and annotating a dataset to training, inference, and benchmarking.
- [Choose Your Tool](choose_your_tool.html.md): Compares EasyClassify, EasySegment, and EasyLocate to help you choose the right deep learning tool for your application.
- [Managing the Dataset and the Annotations](managing_the_dataset_and_the_annotations.html.md): Explains how to add images to a dataset and annotate them with labels, segmentation, or objects for eVision deep learning tools.
- [Managing the Dataset Splits](managing_the_dataset_splits.html.md): Explains why and how to split a dataset into training, validation, and test sets to reliably evaluate a deep learning tool.
- [Using Data Augmentation](using_data_augmentation.html.md): Describes the geometric, color, and noise transformations available for data augmentation when training a deep learning tool.
- [Training a Deep Learning Tool](training_a_deep_learning_tool.html.md): Explains how to train a deep learning tool, covering iterations, batch size, deterministic training, and asynchronous execution.
- [Using a Deep Learning Tool](using_a_deep_learning_tool.html.md): Explains how to apply a trained deep learning tool to new images in Deep Learning Studio and through the API.
- [Benchmarking a Deep Learning Tool](benchmarking_a_deep_learning_tool.html.md): Explains how to benchmark a trained deep learning tool in Deep Learning Studio to measure throughput and latency under different execution settings.

## Optional

- [Top-level llms.txt](../../../llms.txt): Complete documentation index.