Couplet Object Detection Dataset and Pre-Trained Model by PVC

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Explore Dataset

Here are a few use cases for this project:

Cardiac Monitoring: Couplet can be incorporated into patient monitoring systems to detect the various types of couplets in real-time, allowing healthcare professionals to identify arrhythmias and other cardiac anomalies. Early intervention can lead to better patient outcomes.
Telemedicine: Couplet can be integrated into telemedicine platforms to remotely assess a patient's electrocardiogram (ECG) signals. This helps improve access to care for patients who live in rural or hard-to-reach areas, allowing physicians to review their ECG and determine if there are any issues that require immediate attention.
Wearable Device Analysis: Couplet can be embedded into wearable health devices, such as smartwatches or fitness trackers, to analyze ECG data in real-time. This can help users monitor their heart health and alert them to any abnormalities, encouraging proactive healthcare management.
Medical Research: Couplet can be used by researchers studying the prevalence and causes of various couplet types. Automating the identification process can save time and improve accuracy, allowing researchers to focus on understanding the underlying mechanisms and developing effective treatments.
Medical Training and Education: Couplet can be applied to educational materials and training modules, helping medical students and professionals learn to identify different couplet types. By automating the recognition process, trainees can test their skills and gain confidence in their ability to diagnose conditions related to couplet classes.

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{
                            couplet_dataset,
                            title = { Couplet Dataset },
                            type = { Open Source Dataset },
                            author = { PVC },
                            howpublished = { \url{ https://universe.roboflow.com/pvc-kcao8/couplet } },
                            url = { https://universe.roboflow.com/pvc-kcao8/couplet },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { dec },
                            note = { visited on 2024-06-20 },
                            }