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Algerian Vehicle Image Classification Set
Data Science and Analytics
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About
Explaining data drawn from a collection of images featuring vehicle models most frequently utilised in Algeria. This resource is composed of 3,236 image files categorised into 20 distinct classes. It is specifically tailored for development in Computer Vision or Multi-Class Deep Learning projects. The dataset was created with the intent of providing a scaled-down, manageable alternative to much larger resources like the Stanford Cars Dataset, focusing on local vehicle relevance.
Columns
The core data consists of vehicle images, but supporting information detailing the class breakdown is available in the
number_of_samples.csv file:- Car Image Files: The primary data product, featuring visual representation of car models. All images have been pre-processed to remove backgrounds and are uniformly sized at 224x224 pixels to ensure high precision for training models.
- Vehicle Class Name: Defining the specific model name (e.g., Golf, Clio, Picanto).
- Number of Pictures: Indicating the count of image samples available for each corresponding vehicle class.
Distribution
The dataset includes a total of 3,236 image files, logically organised across 20 distinct classes. For quality control and standardisation, every image has been resized to a single dimension (224x224) and backgrounds have been removed from all visual material. The distribution across classes varies; for instance, the data includes 344 files for 'Clio', 280 files for 'Duster', 160 files for 'Golf', and 80 files for 'nemo citroen'.
Usage
This dataset is ideal for:
- Training and evaluation of Multi-Class Deep Learning classification algorithms.
- Developing Computer Vision models focused on vehicle recognition.
- Projects requiring specific, regionally relevant automotive image data, particularly those concerning the Algerian market.
Coverage
The primary geographic scope of this data is specific to vehicle models commonly used in Algeria. No information regarding the time range or demographic scope of the image collection is available in the provided details.
License
License URL is not provided in the source material.
Who Can Use It
- AI Researchers and Academics: Utilizing the data for comparative studies in image standardisation and deep learning efficiency.
- Data Scientists: Building and refining classification models for specific vehicle identification tasks.
- Computer Vision Developers: Creating applications that require precise, pre-processed automotive imagery with standardised dimensions.
Dataset Name Suggestions
- Algerian Vehicle Image Classification Set
- Deep Learning Car Models of Algeria
- Standardised North African Vehicle Imagery
Attributes
Original Data Source:Algerian Vehicle Image Classification Set
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