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:

>>> 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.

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'],
    )
)

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.