Datasets:

Francesco
/

road-traffic

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22	640	640	{ "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 ] }
345	640	640	{ "id": [ 1187, 1188 ], "area": [ 9547, 21042 ], "bbox": [ [ 363, 117, 100.5, 95 ], [ 205, 103, 167, 126 ] ], "category": [ 1, 1 ] }
8	640	640	{ "id": [], "area": [], "bbox": [], "category": [] }
374	640	640	{ "id": [ 1302 ], "area": [ 68478 ], "bbox": [ [ 438, 301, 202, 339 ] ], "category": [ 7 ] }
34	640	640	{ "id": [ 129 ], "area": [ 96940 ], "bbox": [ [ 263, 0, 262, 370 ] ], "category": [ 6 ] }
309	640	640	{ "id": [ 1089 ], "area": [ 79520 ], "bbox": [ [ 0, 261, 355, 224 ] ], "category": [ 2 ] }
133	640	640	{ "id": [ 447, 448 ], "area": [ 53950, 6359 ], "bbox": [ [ 222, 265, 332, 162.5 ], [ 229, 353, 91.5, 69.5 ] ], "category": [ 2, 1 ] }
140	640	640	{ "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 ] }
379	640	640	{ "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 ] }

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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.

Downloads last month: 94

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