Duplicate from mrtoy/mobile-ui-design

Co-authored-by: toy <mrtoy@users.noreply.huggingface.co>

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README.md

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+---
+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.
+

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