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Datasets:
TrainingDataPro
/
license_plates

Tasks:
Image-to-Text
Languages: English
Tags: finance
License: cc-by-nc-nd-4.0
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[Errno 39] Directory not empty: '/storage/hf-datasets-cache/all/datasets/16221363745280-config-parquet-and-info-TrainingDataPro-license_p-a9dfc099/TrainingDataPro___license_plates/Brazil_youtube/0.0.0/2b67c723ffb6aa785c4a64f75b2eee0b1fa990f4a6321f651156096c249179af.incomplete'
Error code:   UnexpectedError

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bbox_id
uint32
bbox
string
image
image
labeled_image
image
license_plate.id
string
license_plate.visibility
string
license_plate.rows_count
uint8
license_plate.number
string
license_plate.serial
string
license_plate.country
string
license_plate.mask
string
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"[0.0, 0.6854166666666667, 0.09765625, 0.7847222222222222]"
"bdf2144d-036b-4909-a157-a4f59d772791"
"invisible"
0
"0"
"0.0"
"Brazil"
"0"
8
"[0.38255208333333335, 0.5884259259259259, 0.48802083333333335, 0.7356481481481482]"
"8c52e50a-a146-46d9-8d04-5cf6b31ee9f2"
"fully_visible"
1
"PLZ3B92"
"0.0"
"Brazil"
"[0.41041666666666665, 0.6458333333333334, 0.40989583333333335, 0.6592592592592592, 0.43020833333333336, 0.6583333333333333, 0.42994791666666665, 0.6462962962962963]"
15,847
"[0.6286458333333333, 0.649074074074074, 0.7614583333333333, 0.8157407407407408]"
"285e1a50-70ad-40ad-9465-3590b606aabf"
"fully_visible"
1
"LMI7G15"
"0.0"
"Brazil"
"[0.7026041666666667, 0.7643518518518518, 0.7263020833333333, 0.7671296296296296, 0.7260416666666667, 0.7810185185185186, 0.70234375, 0.7777777777777778]"
72,405
"[0.3015625, 0.6212962962962963, 0.4197916666666667, 0.7912037037037037]"
"d4a6e267-7a0e-4358-89b7-7711da990739"
"partly_visible_no_text"
0
"0"
"0.0"
"Brazil"
"0"
9,850
"[0.39427083333333335, 0.6314814814814815, 0.46171875, 0.7166666666666667]"
"c3fe935c-dec7-45be-b40d-5cf31bb4cca3"
"fully_visible"
1
"KYQ-6876"
"0.0"
"Brazil"
"[0.41302083333333334, 0.6925925925925925, 0.4265625, 0.6921296296296297, 0.4265625, 0.6995370370370371, 0.41328125, 0.7]"
17
"[0.7401041666666667, 0.6592592592592592, 0.9989583333333333, 0.9703703703703703]"
"e988a2e7-1d2e-4400-b1ec-365014949b5c"
"fully_visible"
1
"LTF6B00"
"0.0"
"Brazil"
"[0.9486979166666667, 0.887962962962963, 0.9958333333333333, 0.8962962962962963, 0.9979166666666667, 0.924537037037037, 0.94921875, 0.9171296296296296]"
48,921
"[0.0036458333333333334, 0.649074074074074, 0.19114583333333332, 1.010185185185185]"
"74f8cafe-54d5-4646-9082-e6ed19d63a53"
"partly_visible_no_text"
0
"0"
"0.0"
"Brazil"
"0"
11,692
"[0.8416666666666667, 0.49444444444444446, 0.9989583333333333, 0.6976851851851852]"
"8f3cb6e2-af72-4395-bbaf-576085a237ec"
"invisible"
0
"0"
"0.0"
"Brazil"
"0"
54,179
"[0.3729166666666667, 0.5537037037037037, 0.40989583333333335, 0.6351851851851852]"
"5dd0dca1-7c59-4719-b1c6-7fad504c533a"
"invisible"
0
"0"
"0.0"
"Brazil"
"0"
11
"[0.3846354166666667, 0.5050925925925925, 0.590625, 0.7828703703703703]"
"d8a19ac5-02d6-49db-bd77-80da910d13e7"
"fully_visible"
1
"GIU-0480"
"0.0"
"Brazil"
"[0.46822916666666664, 0.7106481481481481, 0.5203125, 0.7129629629629629, 0.5200520833333333, 0.7407407407407407, 0.46796875, 0.7388888888888889]"
42,951
"[0.002864583333333333, 0.48564814814814816, 0.33177083333333335, 0.7689814814814815]"
"de2cc5f7-b3fa-4587-8d94-7521363baaf0"
"fully_visible"
1
"EZE-8592"
"0.0"
"Brazil"
"[0.04114583333333333, 0.6416666666666667, 0.08385416666666666, 0.6476851851851851, 0.08046875, 0.6814814814814815, 0.03828125, 0.674537037037037]"

License Plates

Over 1.2 million annotated license plates from vehicles around the world. This dataset is tailored for License Plate Recognition tasks and includes images from both YouTube and PlatesMania. Annotation details are provided in the About section below.

Get the dataset

This is just an example of the data

Leave a request on https://trainingdata.pro/data-market to discuss your requirements, learn about the price and buy the dataset.

About

Variables in .csv files:

  • file_name - filename of the original car photo
  • license_plate.country - country where the vehicle was captured
  • bbox - normalized Bounding Box labeling of the car
  • license_plate.visibility - the visibility type of the license plate
  • license_plate.id - unique license plate's id
  • license_plate.mask - normalized coordinates of the license plate
  • license_plate.rows_count - single-line or double-line number
  • license_plate.number - recognized text of the license plate
  • license_plate.serial - only for UAE numbers - license plate series
  • license_plate.region - only for UAE numbers - license plate subregion
  • license_plate.color - only for Saudi Arabia - color of the international plate code

How it works: go to the folder of the country, CSV-file contains all labeling information about images located in the subfolder "photos" of the corresponding folder.

**TrainingData**

More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets

TrainingData's GitHub: https://github.com/Trainingdata-datamarket/TrainingData_All_datasets

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