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Car Object Detection and Localisation Records
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About
Identifying vehicles within digital imagery is a cornerstone of modern transport technology and autonomous systems. This collection offers a curated set of 326 images, each meticulously annotated to support the development of object detection models. By including a wide variety of car sizes and perspectives, the data assists developers in building systems that can reliably perceive automobiles in diverse environments, from urban streets to open motorways.
Columns
- ID: A unique numerical identifier for the summary metadata entry within the sample file.
- Number of samples for training: The total volume of annotated images and associated files allocated for the initial training of deep learning models.
- Number of samples for testing: The specific count of independent images and annotations used to validate and verify the accuracy of the detection algorithm.
Distribution
The information is provided as a collection of 326 images accompanied by individual annotation files in XML format. The structure is split between 269 training samples and 57 testing samples to facilitate a standard machine learning workflow. A metadata file titled
number_of_samples.csv (73 B) is included to summarise the distribution. The resource has achieved a usability score of 10.00 and is expected to be updated on a quarterly basis.Usage
This resource is perfect for training neural networks to perform real-time vehicle localisation and classification. It can be applied to the development of smart city infrastructure, such as automated toll collection or traffic flow analysis. Developers can also use the XML annotations to practice transfer learning, adapting pre-trained models to specific automotive recognition tasks using common computer vision frameworks.
Coverage
The data captures passenger cars from a multitude of random angles and scales to simulate real-world visual variety. The scope is primarily focused on the visual identification of cars within a static frame, making it suitable for training models intended for diverse lighting and positioning scenarios. The samples are organised into dedicated training and testing folders to ensure a structured approach to model evaluation.
License
CC0: Public Domain
Who Can Use It
Data scientists and machine learning practitioners can utilise these files to build and test car detection prototypes for deep learning projects. Academic researchers might find the pre-split training and testing folders useful for teaching computer vision fundamentals. Additionally, developers in the automotive industry can use the diverse imagery to improve the robustness of vehicle-sensing software.
Dataset Name Suggestions
- Automotive Vision: Multi-Scale Car Detection Set
- Annotated Vehicle Imagery for Machine Learning
- Car Object Detection and Localisation Records
- Quarterly Automotive Vision Training Library
- Visual Car Recognition: XML Annotated Dataset
Attributes
Original Data Source: Car Object Detection and Localisation Records
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