# Deep Learning Studio **Deep Learning Studio** is a graphical user interface that you can use to create datasets and train your deep learning tools. **Deep Learning Studio** is organized around the concept of projects. A project consists in: * A tool type. * A dataset (images and their annotations). * Several dataset splits. * The data augmentation settings. * The tools that you can train on the dataset. * The results and metrics for each tool to analyze their performance. The project is stored as a folder containing: * The project file with the `.edlproject` extension. * The `Images` folder with the imported images. * A dedicated folder for each tool. By default, the projects are stored in the folder named `Deep Learning Studio Projects` located in your `Documents` folder. After loading a project, the project file is automatically locked when it is used. This means that when a program opens a **Deep Learning** project file, this project file is inaccessible by other programs as long as the initial program doesn’t explicitly close the project. Here are the various file extensions used within a project: * `.edlproject`: the project file containing the dataset, the splits, the list of tools and the data augmentation settings. * `.edlsettings`: the settings for a tool. * `.edlmodel`: a deep learning model for the tool. * The training model is the model obtained after the last training iteration. * The inference model is the model that is applied to images. It is the model that gave the best performances during the training. * `.edltool`: a complete usable tool (obtained by exporting the tool from **Deep Learning Studio**). * `.edlmetrics`: the metrics for the tools. * `.edlresult`: a result for a given image and tool. * `.edldataset`: a dataset (obtained by exporting the dataset from the project). * In older versions of **eVision**, the settings, model and tool had the extension `.ecl`, the metrics `.edl`, and the dataset `.eds`. #### NOTE **Deep Learning Studio** never sends or shares resources over the internet. All images, annotations, models and results always remain on your local computer. ## Deep Learning Studio Workflow Illustration This illustrates the typical [workflow](easydeeplearning/index.html.md#easydeeplearning-workflow) to create a Deep Learning tool for your application. 1. Load or create a project. * Quickly go back to your work with the recent project lists. * Open existing projects or create new projects.  * Create a new project. * Specify the name and the type of the project. * If necessary, change the path to save the project. * See [Choose Your Tool](easydeeplearning/choose_your_tool.html.md#easydeeplearning-choose-your-tool).  2. Import images in the project dataset and annotate the images. See [Managing the Dataset and the Annotations](easydeeplearning/managing_the_dataset_and_the_annotations.html.md#easydeeplearning-dataset).  3. Split your dataset into training, validation, and test images. See [Managing the Dataset Splits](easydeeplearning/managing_the_dataset_splits.html.md#easydeeplearning-splits).  4. Configure and view the data augmentation settings. See [Using Data Augmentation](easydeeplearning/using_data_augmentation.html.md#easydeeplearning-data-augmentation).  5. Create, configure, and train your tools. See [Training a Deep Learning Tool](easydeeplearning/training_a_deep_learning_tool.html.md#easydeeplearning-training).  6. Analyze the performance and the results of the trained tools. Make predictions on new images. See [Using a Deep Learning Tool](easydeeplearning/using_a_deep_learning_tool.html.md#easydeeplearning-using).  7. Benchmark your tools to optimize the execution settings and maximize the inference speed on your hardware. See [Benchmarking a Deep Learning Tool](easydeeplearning/benchmarking_a_deep_learning_tool.html.md#easydeeplearning-benchmark).  ## Additional Resources * The **Deep Learning Additional Resources** package, separate from the **eVision** installer, provide several sample datasets as well as **Deep Learning Studio** projects containing a trained tool for these datasets: * For **EasyClassify**: the `MiniWaffle`, `Stone Tiles` and `CopperSilverTape` datasets. * For **EasySegment Unsupervised**: the `Fabric` dataset. * For **EasySegment Supervised**: the `Coffee` dataset. * For **EasyLocate Axis Aligned Bounding Box**: the `ElectronicComponentsBag` dataset. * For **EasyLocate Interest Point**: the `CeramicCapacitor` dataset.