Anomaly Object Detection Dataset and Pre-Trained Model by sjsu

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Explore Dataset

Here are a few use cases for this project:

Security Systems: The "Anomaly" computer vision model could be used in security surveillance systems to detect anomalous events like unauthorized persons entering through a door or detecting if a door has been left open unintentionally.
Smart Home Automation: The model can be integrated into smart home systems to intelligently control parts of the home, such as automatically turning on lights when a person is detected or sending alerts when a door is left open while the home is unattended.
Architectural Design Software: The model can be implemented in architectural software tools to automatically classify and label different architectural elements, aiding in the design process or in understanding and modifying existing structures.
Elderly Care Technology: In facilities where elderly or disabled individuals live, the "Anomaly" model can be used to monitor living spaces to alert staff if a person has fallen down or if a door has been left open, which could potentially lead to safety risks.
Interior Design: For professionals in interior design, the model can be utilized to automatically detect and classify elements in different rooms, helping them to map out potential design changes or improvements.

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{
                            anomaly-dqrqf_dataset,
                            title = { Anomaly Dataset },
                            type = { Open Source Dataset },
                            author = { sjsu },
                            howpublished = { \url{ https://universe.roboflow.com/sjsu-ilbp6/anomaly-dqrqf } },
                            url = { https://universe.roboflow.com/sjsu-ilbp6/anomaly-dqrqf },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-05-03 },
                            }