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Datasets:
Francesco
/
road-traffic

Tasks:
Object Detection
Modalities:
Image
Formats:
parquet
Languages:
English
Size:
< 1K
Tags:
rf100
Libraries:
Datasets
pandas
License:
Dataset card Viewer Files Community
Dataset Viewer
Auto-converted to Parquet
Full Screen
Search is not available for this dataset
image_id
int64
0
493
image
imagewidth (px)
640
640
width
int32
640
640
height
int32
640
640
objects
sequence
486
640
640
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{ "id": [ 200, 201, 202, 203, 204, 205, 206 ], "area": [ 1479, 18, 2600, 3152, 6215, 99000, 623 ], "bbox": [ [ 584, 145, 43.5, 34 ], [ 588, 216, 4, 4.5 ], [ 542, 205, 52, 50 ], [ 548, 242, 48.5, 65 ], [ 583, 224, 56.5, 110 ], [ 244, 164, 300, 330 ], [ 588, 121, 29, 21.5 ] ], "category": [ 7, 2, 7, 7, 7, 2, 7 ] }
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{ "id": [ 909, 910 ], "area": [ 109138, 66420 ], "bbox": [ [ 363, 47, 277, 394 ], [ 370, 394, 270, 246 ] ], "category": [ 2, 7 ] }
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{ "id": [ 927, 928, 929 ], "area": [ 3145, 8407, 3232 ], "bbox": [ [ 347, 286, 74, 42.5 ], [ 65, 289, 147.5, 57 ], [ 245, 300, 64, 50.5 ] ], "category": [ 7, 7, 7 ] }
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{ "id": [ 1464 ], "area": [ 40693 ], "bbox": [ [ 91, 67, 123.5, 329.5 ] ], "category": [ 6 ] }
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{ "id": [ 920 ], "area": [ 65450 ], "bbox": [ [ 0, 131, 175, 374 ] ], "category": [ 5 ] }
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{ "id": [ 672, 673 ], "area": [ 40183, 20995 ], "bbox": [ [ 154, 182, 281, 143 ], [ 0, 218, 170, 123.5 ] ], "category": [ 7, 7 ] }
22
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{ "id": [ 93, 94, 95 ], "area": [ 5698, 13398, 25056 ], "bbox": [ [ 556, 417, 77, 74 ], [ 391, 415, 132, 101.5 ], [ 354, 544, 261, 96 ] ], "category": [ 7, 7, 7 ] }
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{ "id": [ 1187, 1188 ], "area": [ 9547, 21042 ], "bbox": [ [ 363, 117, 100.5, 95 ], [ 205, 103, 167, 126 ] ], "category": [ 1, 1 ] }
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{ "id": [], "area": [], "bbox": [], "category": [] }
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{ "id": [ 1302 ], "area": [ 68478 ], "bbox": [ [ 438, 301, 202, 339 ] ], "category": [ 7 ] }
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{ "id": [ 129 ], "area": [ 96940 ], "bbox": [ [ 263, 0, 262, 370 ] ], "category": [ 6 ] }
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{ "id": [ 1089 ], "area": [ 79520 ], "bbox": [ [ 0, 261, 355, 224 ] ], "category": [ 2 ] }
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{ "id": [ 447, 448 ], "area": [ 53950, 6359 ], "bbox": [ [ 222, 265, 332, 162.5 ], [ 229, 353, 91.5, 69.5 ] ], "category": [ 2, 1 ] }
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{ "id": [ 467, 468, 469, 470, 471, 472 ], "area": [ 11074, 15557, 16254, 29006, 38143, 20169 ], "bbox": [ [ 527, 135, 113, 98 ], [ 462, 152, 127, 122.5 ], [ 292, 229, 126, 129 ], [ 172, 232, 162.5, 178.5 ], [ 43, 290, 212.5, 179.5 ], [ 0, 319, 81, 249 ] ], "category": [ 7, 7, 5, 5, 5, 5 ] }
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{ "id": [ 1306, 1307, 1308, 1309, 1310 ], "area": [ 7056, 8800, 2250, 1290, 42602 ], "bbox": [ [ 215, 137, 112, 63 ], [ 129, 162, 137.5, 64 ], [ 620, 215, 20, 112.5 ], [ 218, 270, 29, 44.5 ], [ 282, 266, 358, 119 ] ], "category": [ 7, 7, 7, 4, 3 ] }
End of preview. Expand in Dataset Viewer.

Dataset Card for road-traffic

** The original COCO dataset is stored at dataset.tar.gz**

Dataset Summary

road-traffic

Supported Tasks and Leaderboards

  • object-detection: The dataset can be used to train a model for Object Detection.

Languages

English

Dataset Structure

Data Instances

A data point comprises an image and its object annotations.

{
  'image_id': 15,
  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
  'width': 964043,
  'height': 640,
  'objects': {
    'id': [114, 115, 116, 117], 
    'area': [3796, 1596, 152768, 81002],
    'bbox': [
      [302.0, 109.0, 73.0, 52.0],
      [810.0, 100.0, 57.0, 28.0],
      [160.0, 31.0, 248.0, 616.0],
      [741.0, 68.0, 202.0, 401.0]
    ], 
    'category': [4, 4, 0, 0]
  }
}

Data Fields

  • image: the image id
  • image: PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0]
  • width: the image width
  • height: the image height
  • objects: a dictionary containing bounding box metadata for the objects present on the image
    • id: the annotation id
    • area: the area of the bounding box
    • bbox: the object's bounding box (in the coco format)
    • category: the object's category.

Who are the annotators?

Annotators are Roboflow users

Additional Information

Licensing Information

See original homepage https://universe.roboflow.com/object-detection/road-traffic

Citation Information 

@misc{ road-traffic,
    title = { road traffic Dataset },
    type = { Open Source Dataset },
    author = { Roboflow 100 },
    howpublished = { \url{ https://universe.roboflow.com/object-detection/road-traffic } },
    url = { https://universe.roboflow.com/object-detection/road-traffic },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2022 },
    month = { nov },
    note = { visited on 2023-03-29 },
}"

Contributions

Thanks to @mariosasko for adding this dataset.

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Edit dataset card
Homepage:
universe.roboflow.com
Point of Contact:
francesco.zuppichini@gmail.com
Size of downloaded dataset files:
37.5 MB
Size of the auto-converted Parquet files:
37.5 MB
Number of rows:
814