---
license: apache-2.0
dataset_info:
  features:
  - name: width
    dtype: int64
  - name: height
    dtype: int64
  - name: image
    dtype: image
  - name: objects
    struct:
    - name: bbox
      sequence:
        sequence: float64
    - name: category
      sequence: string
    - name: color
      list:
      - name: alpha
        dtype: float64
      - name: blue
        dtype: float64
      - name: green
        dtype: float64
      - name: red
        dtype: float64
    - name: radius
      sequence: float64
    - name: text
      sequence: string
  splits:
  - name: train
    num_bytes: 1253458059.322
    num_examples: 7846
  download_size: 1160884066
  dataset_size: 1253458059.322
task_categories:
- object-detection
tags:
- ui
- design
- detection
size_categories:
- n<1K
---

# Dataset: Mobile UI Design Detection

## Introduction

This dataset is designed for object detection tasks with a focus on detecting elements in mobile UI designs. The targeted objects include text, images, and groups. The dataset contains images and object detection boxes, including class labels and location information.

## Dataset Content

Load the dataset and take a look at an example:

```python
>>> from datasets import load_dataset
>>>> ds = load_dataset("mrtoy/mobile-ui-design")
>>> example = ds[0]
>>> example
{'width': 375,
 'height': 667,
 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=375x667>,
 'objects': {'bbox': [[0.0, 0.0, 375.0, 667.0],
   [0.0, 0.0, 375.0, 667.0],
   [0.0, 0.0, 375.0, 20.0],
   ...
  ],
  'category': ['text',
   'rectangle',
   'rectangle',
   ...]}}
```

The dataset has the following fields:

- image: PIL.Image.Image object containing the image.
- height: The image height.
- width: The image width.
- objects: A dictionary containing bounding box metadata for the objects in the image:
    - bbox: The object’s bounding box (xmin,ymin,width,height).
    - category: The object’s category, with possible values including rectangle、text、group、image
    - color: The object’s color, text color or rectangle color, or None
    - radius: The object’s color, rectangle radius, or None
    - text: text content, or None

You can visualize the bboxes on the image using some internal torch utilities. 

```python
import torch
from torchvision.ops import box_convert
from torchvision.utils import draw_bounding_boxes
from torchvision.transforms.functional import pil_to_tensor, to_pil_image

item = ds[0]
boxes_xywh = torch.tensor(item['objects']['bbox'])
boxes_xyxy = box_convert(boxes_xywh, 'xywh', 'xyxy')
to_pil_image(
    draw_bounding_boxes(
        pil_to_tensor(item['image']),
        boxes_xyxy,
        labels=item['objects']['category'],
    )
)
```
![image](9b8671a5-b529-41dc-b951-b29a8b29da64.png)

![image](11c03c2c-39ac-442b-9c1a-67e1e0a2aea7.png)

![image](ec197c72-f8ba-4f79-81fa-ceaf533cb5e3.png)

## Applications

This dataset can be used for various applications, such as:

- Training and evaluating object detection models for mobile UI designs.
- Identifying design patterns and trends to aid UI designers and developers in creating high-quality mobile app UIs.
- Enhancing the automation process in generating UI design templates.
- Improving image recognition and analysis in the field of mobile UI design.