---
license: apache-2.0
task_categories:
- object-detection
pretty_name: Cubes on conveyor belt
size_categories:
- n<1K
---

This dataset has been collected by [Edge Impulse](https://edgeimpulse.com/) and used extensively to design the FOMO (Faster Objects, More Objects) object detection architecture. See [FOMO documentation](https://docs.edgeimpulse.com/docs/edge-impulse-studio/learning-blocks/object-detection/fomo-object-detection-for-constrained-devices) or the announcement [blog post](https://www.edgeimpulse.com/blog/announcing-fomo-faster-objects-more-objects/).

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6447cd97ab5c7251886d4a93/x0BT0j7OVPYUPdD2w_WY_.png)

The dataset is composed of 70 images including:
- 32 blue cubes,
- 32 green cubes,
- 30 red cubes
- 28 yellow cubes

Download link: [cubes on a conveyor belt dataset](https://cdn.edgeimpulse.com/datasets/cubes\_ei-object-detection-format.zip) in Edge Impulse Object Detection format. 

You can also retrieve this dataset from this [Edge Impulse public project](https://studio.edgeimpulse.com/public/230968/latest). 

**Data exported from an object detection project in the Edge Impulse Studio is exported in this format, see below to understand the format.**

## How to use this dataset

To import this data into a new Edge Impulse project, either use:

- The clone button in the [Edge Impulse public project](https://studio.edgeimpulse.com/public/230968/latest)
- via the Edge Impulse Studio. Go to **Data acquisition &gt; Upload data**.
- Or, via the Edge Impulse CLI (https://docs.edgeimpulse.com/docs/tools/edge-impulse-cli/cli-uploader), run with:

```
edge-impulse-uploader --clean --info-file info.labels
```

## Understand Edge Impulse object detection format

The Edge Impulse object detection acquisition format provides a simple and intuitive way to store images and associated bounding box labels. Folders containing data in this format will take the following structure:

```
.
├── testing
│   ├── bounding_boxes.labels
│   ├── cubes.23im33f2.jpg
│   ├── cubes.23j3rclu.jpg
│   ├── cubes.23j4jeee.jpg
│   ...
│   └── cubes.23j4k0rk.jpg
└── training
    ├── bounding_boxes.labels
    ├── blue.23ijdngd.jpg
    ├── combo.23ijkgsd.jpg
    ├── cubes.23il4pon.jpg
    ├── cubes.23im28tb..jpg
    ...
    └── yellow.23ijdp4o.jpg

2 directories, 73 files
```

The subdirectories contain image files in JPEG or PNG format. Each image file represents a sample and is associated with its respective bounding box labels in the `bounding_boxes.labels` file.

The `bounding_boxes.labels` file in each subdirectory provides detailed information about the labeled objects and their corresponding bounding boxes. The file follows a JSON format, with the following structure:

* `version`: Indicates the version of the label format.
* `files`: A list of objects, where each object represents an image and its associated labels.
  * `path`: The path or file name of the image.
  * `category`: Indicates whether the image belongs to the training or testing set.
  * (optional) `label`: Provides information about the labeled objects.
    * `type`: Specifies the type of label (e.g., a single label).
    * `label`: The actual label or class name of the object.
  * (Optional) `metadata`: Additional metadata associated with the image, such as the site where it was collected, the timestamp or any useful information.
  * `boundingBoxes`: A list of objects, where each object represents a bounding box for an object within the image.
    * `label`: The label or class name of the object within the bounding box.
    * `x`, `y`: The coordinates of the top-left corner of the bounding box.
    * `width`, `height`: The width and height of the bounding box.

`bounding_boxes.labels` example:

```json
{
    "version": 1,
    "files": [
        {
            "path": "cubes.23im33f2.jpg",
            "category": "testing",
            "label": {
                "type": "label",
                "label": "cubes"
            },
            "metadata": {
                "version": "2023-1234-LAB"
            },
            "boundingBoxes": [
                {
                    "label": "green",
                    "x": 105,
                    "y": 201,
                    "width": 91,
                    "height": 90
                },
                {
                    "label": "blue",
                    "x": 283,
                    "y": 233,
                    "width": 86,
                    "height": 87
                }
            ]
        },
        {
            "path": "cubes.23j3rclu.jpg",
            "category": "testing",
            "label": {
                "type": "label",
                "label": "cubes"
            },
            "metadata": {
                "version": "2023-4567-PROD"
            },
            "boundingBoxes": [
                {
                    "label": "red",
                    "x": 200,
                    "y": 206,
                    "width": 74,
                    "height": 75
                },
                {
                    "label": "yellow",
                    "x": 370,
                    "y": 245,
                    "width": 79,
                    "height": 73
                }
            ]
        }
    ] 
}
```

## Additional resources

A variation of this dataset has been used in the [FOMO Self-Attention article](https://www.edgeimpulse.com/blog/fomo-self-attention/)