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@@ -21,4 +21,82 @@ dataset_info:
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  num_examples: 7997
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  download_size: 1178990085
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  dataset_size: 1258281789.658
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  num_examples: 7997
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  download_size: 1178990085
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  dataset_size: 1258281789.658
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+ task_categories:
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+ - object-detection
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+ tags:
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+ - ui
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+ - design
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+ - detection
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+ size_categories:
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+ - n<1K
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  ---
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+
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+ # Dataset: Mobile UI Design Detection
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+
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+ ## Introduction
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+
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+ 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.
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+
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+ ## Dataset Content
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+
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+ Load the dataset and take a look at an example:
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+
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+ ```python
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+ >>> from datasets import load_dataset
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+ >>>> ds = load_dataset("mrtoy/mobile-ui-design")
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+ >>> example = ds[0]
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+ >>> example
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+ {'width': 375,
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+ 'height': 667,
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+ 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=375x667>,
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+ 'objects': {'bbox': [[0.0, 0.0, 375.0, 667.0],
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+ [0.0, 0.0, 375.0, 667.0],
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+ [0.0, 0.0, 375.0, 20.0],
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+ ...
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+ ],
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+ 'category': ['artboard',
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+ 'rectangle',
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+ 'rectangle',
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+ ...]}}
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+ ```
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+
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+ The dataset has the following fields:
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+
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+ - image: PIL.Image.Image object containing the image.
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+ - height: The image height.
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+ - width: The image width.
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+ - objects: A dictionary containing bounding box metadata for the objects in the image:
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+ - bbox: The object’s bounding box (xmin,ymin,width,height).
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+ - category: The object’s category, with possible values including artboard、rectangle、text、group、...
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+
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+ You can visualize the bboxes on the image using some internal torch utilities.
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+
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+ ```python
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+ import torch
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+ from torchvision.ops import box_convert
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+ from torchvision.utils import draw_bounding_boxes
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+ from torchvision.transforms.functional import pil_to_tensor, to_pil_image
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+
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+ item = ds[0]
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+ boxes_xywh = torch.tensor(item['objects']['bbox'])
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+ boxes_xyxy = box_convert(boxes_xywh, 'xywh', 'xyxy')
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+ to_pil_image(
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+ draw_bounding_boxes(
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+ pil_to_tensor(item['image']),
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+ boxes_xyxy,
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+ labels=item['objects']['category'],
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+ )
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+ )
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+ ```
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+
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+
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+ ## Applications
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+
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+ This dataset can be used for various applications, such as:
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+
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+ - Training and evaluating object detection models for mobile UI designs.
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+ - Identifying design patterns and trends to aid UI designers and developers in creating high-quality mobile app UIs.
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+ - Enhancing the automation process in generating UI design templates.
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+ - Improving image recognition and analysis in the field of mobile UI design.
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+
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+