Vehicle Detection Image Set
Image dataset for the automatic detection of vehicles. Contains 17,760 annotated photos in two classes (vehicles, non-vehicles).
17,760 JPEG images, two classes (vehicles/non-vehicles)
Open Database License (ODbL)
Description
The dataset Vehicle Detection Image Set is designed for visual classification tasks, including vehicle detection. It contains 17,760 images divided into two categories: “Vehicles” and “Non-Vehicles”. This corpus is intended for the training and validation of computer vision models, in particular in the field of transport, road surveillance and assisted driving.
What is this dataset for?
- Train binary classification models (vehicle vs. non-vehicle)
- Test object detection algorithms in various visual conditions
- Create a prototype for a road surveillance or smart parking system
Can it be enriched or improved?
Yes. It is possible to add metadata (e.g. angle, brightness), to increase the data by transformation (flips, rotations, blur), or to cross with additional annotations such as the type of vehicle or its exact position (bounding boxes).
In summary
🧠 Recommended for
- AI developers
- Computer vision students
- Road detection prototypes
🔧 Compatible tools
- TensorFlow
- PyTorch
- OpenCV
- Keras
💡 Tip
Combine this dataset with others that contain more complex annotations for a complete object detection pipeline.
Frequently Asked Questions
Does the dataset contain images annotated with bounding boxes?
No, the images are classified by folder according to the two labels only (vehicle/non-vehicle).
Can it be used to train a vehicle detector in real time?
Yes, with a lightweight architecture (e.g. MobileNet) and an increase in data, this dataset can be used as a basis for a prototype.
What is the average resolution of the images?
It varies from file to file, but images are generally moderate in size and suitable for quick training.
