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Military Vehicle Recognition

Object Detection

1.2k images

Military Vehicle Recognition Computer Vision Project

MilitaryVehicleRecognition

Updated 4 months ago

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Metrics

mAP is equal to the average of the Average Precision metric across all classes in a model. Learn more

mAP

47.3%

Precision measures how often your model's predictions are correct. Learn more

Precision

62.9%

Recall measures what percentage of relevant labels were successfully identified. Learn more

Recall

42.1%

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Description

This dataset contains labeled aerial images of air fighters, bombers, armored personnel carriers, tanks and soldiers captured by reconnaissance drones during the russo-Ukrainian War, aimed at supporting the development of machine learning models for military object detection. The images simulate real-world conditions with diverse altitudes, angles, and lighting, providing a robust foundation for applications in automated surveillance and situational awareness. Ideal for defense and security contexts, this dataset enables precise detection and classification of military objects, aiding in real-time decision-making and enhancing overall reconnaissance capabilities.

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LICENSE

CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        
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@misc{
                            military-vehicle-recognition_dataset,
                            title = { Military Vehicle Recognition Dataset },
                            type = { Open Source Dataset },
                            author = { MilitaryVehicleRecognition },
                            howpublished = { \url{ https://universe.roboflow.com/militaryvehiclerecognition/military-vehicle-recognition } },
                            url = { https://universe.roboflow.com/militaryvehiclerecognition/military-vehicle-recognition },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { nov },
                            note = { visited on 2025-03-25 },
                            }