AI Notebooks - Tutorial - Create and train an image generation model

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.

Requirements

• Access to the OVHcloud Control Panel
• An AI Notebooks project created inside a Public Cloud project in your OVHcloud account
• A user for AI Notebooks
• A Kaggle account to download the dataset

Instructions

You can launch the notebook from the OVHcloud Control Panel or via the ovhai CLI.

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.

Launching a Jupyter notebook with "conda" via CLI

If you do not use our CLI yet, follow this guide 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:

ovhai capabilities framework get conda -o yaml

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:

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 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.

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

• Use Transfer Learning with ResNet50 for image classification

• Train YOLOv7 for American Sign Language recognition

If you need training or technical assistance to implement our solutions, contact your sales representative or click on this link 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