YOLOV5 Object Detection Dataset and Pre-Trained Model by Thesis

Images

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Explore Dataset

Here are a few use cases for this project:

Military Training Applications: The model can be used in simulation-based military training environments to educate personnel about different types of weaponry, armored vehicles, and aircraft types. This can also assist in playing out various battlefield scenarios and strategy formulation. The search keywords would be "Military Training", "Simulation", "Battlefield Scenarios".
Surveillance and Security Systems: The computer vision model can be employed in surveillance applications to detect and classify threats in real-time. Relevant keywords include "Surveillance", "Real-time Threat Detection", "Security Systems".
Battlefield Damage Assessment: The AI model can be crucial in assessing damage or loss after military operations by identifying present military objects from aerial or ground-level images. Keywords related to this would be "Damage Assessment", "Military Operations", "Post-Conflict Analysis".
Museum Display Categorization: Museums displaying military history could use this computer vision model to automatically categorize their exhibits and assist in automated guided tours. Keywords associated with this would be "Museum Exhibit Categorization", "Guided Tours", "Military History".
Video Games Development: In the development of realistic warfare games, this model can help in identifying, distinguishing, and properly rendering different military objects. Search keywords include "Game Development", "Realistic Warfare Games", "3D Object Rendering".

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

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

@misc{
                            yolov5-fgeu9_dataset,
                            title = { YOLOV5 Dataset },
                            type = { Open Source Dataset },
                            author = { Thesis },
                            howpublished = { \url{ https://universe.roboflow.com/thesis-tstpo/yolov5-fgeu9 } },
                            url = { https://universe.roboflow.com/thesis-tstpo/yolov5-fgeu9 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { sep },
                            note = { visited on 2024-05-08 },
                            }