> ## Documentation Index
> Fetch the complete documentation index at: https://avala.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Create Your First Project

> Step-by-step tutorial for creating an annotation project

This tutorial walks you through creating a complete annotation project in Avala.

<iframe width="560" height="315" src="https://www.youtube.com/embed/YJ2jjWPxVsI" title="Connect a Dataset to Avala" frameBorder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowFullScreen />

## What You'll Learn

* How to create and configure a dataset
* How to set up an annotation project
* How to use the annotation tools
* How to review and export annotations

## Prerequisites

* An Avala account
* Sample images to annotate (10-20 images recommended)

## Step 1: Create a Dataset

1. Log in to [Avala](https://avala.ai)
2. Click **Datasets** in the left sidebar
3. Click **Add Dataset** in the top right
4. Fill in the dataset details:
   * **Name**: "My First Dataset"
   * **Description**: Brief description of your data
   * **Visibility**: Private (or Public if sharing)
5. Click **Create**

## Step 2: Upload Images

1. On your dataset page, click **Add Items**
2. Drag and drop your images into the upload zone
3. Alternatively, click to browse and select files
4. Wait for uploads to complete

<Info>
  Supported image formats: JPEG, PNG, WebP, BMP. Maximum file size: 20MB per image.
</Info>

## Step 3: Create a Project

1. Navigate to **Projects** in the sidebar
2. Click **Add Project**
3. Configure your project:
   * **Name**: "Object Detection Project"
   * **Dataset**: Select your dataset
   * **Task Type**: Bounding Box
4. Click **Next** to configure labels

## Step 4: Define Labels

Set up your label taxonomy:

1. Click **Add Label**
2. Enter label details:
   * **Name**: "car"
   * **Color**: Choose a color
3. Add more labels as needed (e.g., "pedestrian", "bicycle")
4. Click **Create Project**

## Step 5: Start Annotating

1. Open your dataset → **Items**
2. Click on an image to open the annotation editor
3. Use the **Box tool** (press `B`) to draw bounding boxes
4. Draw a box around an object:
   * Click and drag from corner to corner
   * Release to complete the box
5. Select a label from the dropdown
6. Repeat for all objects in the image
7. Click **Save** or press `Ctrl+S`

### Annotation Tips

* **Tight boxes**: Draw boxes that tightly fit the object
* **Occluded objects**: Include partially visible objects
* **Consistent labeling**: Use the same label for similar objects
* **Keyboard shortcuts**: Use shortcuts for faster annotation

## Step 6: Review Annotations

1. Go to **Projects** → your project
2. Click **Review** to see pending annotations
3. For each annotation:
   * **Accept**: Annotation is correct
   * **Reject**: Annotation needs correction (add feedback)
4. Track progress in the project dashboard

## Step 7: Export Data

1. Navigate to **Exports**
2. Click **Create Export**
3. Select:
   * **Source**: Your project
   * **Format**: COCO JSON (or your preferred format)
4. Click **Create**
5. Download when ready

## Keyboard Shortcuts

| Shortcut       | Action                     |
| -------------- | -------------------------- |
| `B`            | Box tool                   |
| `P`            | Polygon tool               |
| `V`            | Select/Move tool           |
| `Delete`       | Delete selected annotation |
| `Ctrl+S`       | Save                       |
| `←` / `→`      | Previous/Next item         |
| `Ctrl+Z`       | Undo                       |
| `Ctrl+Shift+Z` | Redo                       |

## Next Steps

* Learn about [Managing Datasets](/docs/annotation/guides/managing-datasets) in depth
* Set up [Team Permissions](/docs/annotation/guides/team-permissions) workflows
* Explore the [Data Pipeline Tutorial](/docs/annotation/tutorials/data-pipeline) for end-to-end workflows