---
title: "AI Notebooks - Tutoriel - Créer et entraîner un modèle de génération d'images (EN)"
description: "Découvrez le fonctionnement des GANs et comment générer vos propres images synthétiques"
url: https://docs.ovhcloud.com/fr/guides/public-cloud/ai-machine-learning/ai-notebooks-tuto-image-generation-dcgan
lang: fr
lastUpdated: 2023-08-04
---
# AI Notebooks - Tutoriel - Créer et entraîner un modèle de génération d'images (EN)

## Objective

Over the past few years, the field of **computer vision** has experienced a significant growth. It encompasses a wide range of methods for acquiring, processing, analyzing and understanding digital images.

Among these methods, one is called **image generation**.

The purpose of this tutorial is to show you how it is possible to build and train an image generation model with **OVHcloud AI Notebooks**. We will use a popular convolutional GAN named **DCGAN**.

At the end of this tutorial, you will have learnt the concepts of generative models, model evaluation, and how to generate your own images by training a DCGAN on your dataset.


:::info
We will train the DCGAN on the _[Chest X-Ray dataset](https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia)_. We will show you how you can easily download the dataset in the notebook tutorial.

:::

## Requirements

- An AI Notebooks project created inside a [Public Cloud project](https://www.ovhcloud.com/fr/public-cloud/) in your OVHcloud account
- A user for AI Notebooks
- A [Kaggle](https://www.kaggle.com/) account to download the dataset


***

### OVHcloud Control Panel Access

- **Direct link:** <ManagerLink to="/#/pci/projects">Public Cloud Projects</ManagerLink>
- **Navigation path:** <code className="action">Public Cloud</code> > Select your project

***


## Instructions

You can launch the notebook from the OVHcloud Control Panel
 or via the ovhai [CLI](/fr/guides/public-cloud/ai-machine-learning/ai-cli-run-notebook.md)
.
### Launching a Jupyter notebook with "Conda" via UI (Control Panel)

To launch your notebook from the OVHcloud Control Panel
, refer to the following steps.
#### Code editor

Choose the `Jupyterlab` code editor.

#### Framework

In this tutorial, the `conda` framework is used.

#### Resources

Using GPUs is recommended because manipulating images is a training intensive task.

:::info
Here, using `1 GPU` is sufficient.

:::

### Launching a Jupyter notebook with "conda" via CLI

_If you do not use our CLI yet, follow [this guide](/fr/guides/public-cloud/ai-machine-learning/ai-cli-install-client.md) to install it._

If you want to launch your notebook with the OVHcloud AI CLI, choose the `jupyterlab` editor and the `conda` framework.

To access the different versions of `conda` available, run the following command:

```console
ovhai capabilities framework get conda -o yaml
```

:::info
If you do not specify a version, your notebook starts with the default version of `conda`.

:::

You will also need to choose the number of GPUs to use in your notebook using `<nb-gpus>`.

To launch your notebook, run the following command:

```console
ovhai notebook run conda jupyterlab \
		--name <notebook-name> \
		--framework-version <conda-version> \
		--gpu <nb-gpus>

```

You can then reach your notebook’s URL once the notebook is running.

### Accessing the notebooks

Once our [AI examples repository](https://github.com/ovh/ai-training-examples/) has been cloned in your environment, find the fine-tuning notebook tutorial by following this path: `ai-training-examples` > `notebooks` > `computer-vision` > `image-generation` > `miniconda` > `dcgan-image-generation` > `notebook_chest_image_generation_dcgan.ipynb`.

A preview of this notebook can be found on GitHub [here](https://github.com/ovh/ai-training-examples/blob/main/notebooks/computer-vision/image-generation/miniconda/dcgan-image-generation/notebook_chest_image_generation_dcgan.ipynb).

## Go further

There are many other tasks that exist in the computer vision field. Check our other tutorials to learn how to:

- [Perform Brain tumor segmentation using U-Net](/fr/guides/public-cloud/ai-machine-learning/ai-notebooks-tuto-image-segmentation-unet-tumors.md)

- [Use Transfer Learning with ResNet50 for image classification](/fr/guides/public-cloud/ai-machine-learning/ai-notebooks-transfer-learning-resnet.md)

- [Train YOLOv7 for American Sign Language recognition](/fr/guides/public-cloud/ai-machine-learning/ai-notebooks-yolov7-sign-language.md)

If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/fr/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project.

## Feedback

Please send us your questions, feedback and suggestions to improve the service:

- On the OVHcloud [Discord server](https://discord.gg/ovhcloud)