annotation 2.0 Object Detection Dataset and Pre-Trained Model by AnnotationEricsson

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Explore Dataset

Here are a few use cases for this project:

Maintenance and Inspection in Telecommunication Industry: Annotation 2.0 can efficiently identify various telecom equipment installed on towers or rooftops, such as antennas, RRU, TGBT, and more. This could help maintenance teams to quickly localize faulty equipment, streamline inspection processes, and prioritize repairs.
Remote Monitoring and Management of Electrical Infrastructure: By identifying equipment like batteries, DC cables, PDU, and PCU AC, Annotation 2.0 can be used to create real-time monitoring systems. This would enable utility companies or data centers to anticipate potential failures, improve energy efficiency, and enhance overall system management.
Training and Education in Technical Fields: The computer vision model can be used to develop interactive training modules and virtual simulations. Students and professionals can gain practical knowledge of installing, maintaining, or troubleshooting various equipment in industries such as telecommunications, electrical, or energy management.
Inventory and Asset Management: By recognizing equipment like BBS, DDF, and BFU, Annotation 2.0 can help companies automate inventory management and asset tracking. This would simplify auditing processes, prevent equipment loss, and improve overall logistics efficiency.
Safety Compliance and Quality Assurance: Annotation 2.0 can assist in ensuring that equipment installations and maintenance work, such as Jumper-Cable and TMA installation, adhere to the industry-specific safety standards and guidelines. This would reduce the risk of accidents, optimize installation workflows, and help organizations comply with necessary regulations.

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{
                            annotation-2.0_dataset,
                            title = { annotation 2.0 Dataset },
                            type = { Open Source Dataset },
                            author = { AnnotationEricsson },
                            howpublished = { \url{ https://universe.roboflow.com/annotationericsson/annotation-2.0 } },
                            url = { https://universe.roboflow.com/annotationericsson/annotation-2.0 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { jul },
                            note = { visited on 2024-04-27 },
                            }