Fyp-Videos-Annotations Object Detection Dataset and Pre-Trained Model by FYPDataset

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Explore Dataset

Here are a few use cases for this project:

Urban Planning and Development: The model can be used by city planners and construction firms to understand the physical elements present in a certain area, such as trees, signs, sidewalks, stairs, etc. This can aid in making informed decisions about infrastructure development and urban planning.
Autonomous Vehicles Navigation: Self-driving car companies can leverage this model to better understand road situations, identify objects such as cars, bikes, pedestrians, signboards, fences on the road, assisting in improving vehicular navigation and safety.
Security and Monitoring Systems: The model can be used in CCTV systems to efficiently interpret the environment and identify any unusual activities based on object interaction (e.g., person opening a window or door at odd hours), contributing to enhanced security.
Augmented Reality Applications: Companies developing AR applications can utilize this model to better annotate the physical world, enabling more immersive experiences by allowing the AR system to understand and interact with a wide range of objects in a user's environment.
Waste Management: Municipal bodies can use this model to identify discarded objects like bottles, cups, or bowls in public spaces, aiding in more efficient waste management and the development of cleaner cities.

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.

YOLOv8

This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.

Cite This Project

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

@misc{
                            fyp-videos-annotations_dataset,
                            title = { Fyp-Videos-Annotations Dataset },
                            type = { Open Source Dataset },
                            author = { FYPDataset },
                            howpublished = { \url{ https://universe.roboflow.com/fypdataset-pw40c/fyp-videos-annotations } },
                            url = { https://universe.roboflow.com/fypdataset-pw40c/fyp-videos-annotations },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jan },
                            note = { visited on 2024-06-14 },
                            }