Meta Semantic Segmentation Dataset and Pre-Trained Model by HF

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Explore Dataset

Here are a few use cases for this project:

Medical Education and Training: Meta can be used as a teaching aid for medical students, residents, and professionals to help them accurately identify and differentiate various metacarpal bones and their classes, aiding in the understanding of human anatomy and improving diagnostic skills.
Virtual and Augmented Reality Applications: The Meta model can be integrated into virtual and augmented reality applications to create immersive and interactive experiences for learning or exploring the human skeletal system, specifically focusing on the metacarpus.
Rehabilitation and Orthopedic Care: Meta can assist physiotherapists and orthopedic specialists in diagnosing and assessing hand injuries by providing quick and accurate classification of metacarpal bones, helping to determine appropriate treatment plans and monitor progress during rehabilitation.
Forensic Anthropology: The computer vision model can be applied in forensic anthropology to analyze skeletal remains, allowing experts to identify metacarpus classes in the remains, which can provide valuable information about the individual, such as age, sex, or ancestry.
Bioarchaeological research: Meta can be utilized in bioarchaeological studies to better understand the evolution of the metacarpus in human populations and identify morphological variations across different geographical regions or time periods, contributing to broader evolutionary and anthropological research.

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{
                            meta-mkxdc_dataset,
                            title = { Meta Dataset },
                            type = { Open Source Dataset },
                            author = { HF },
                            howpublished = { \url{ https://universe.roboflow.com/hf-w1rwi/meta-mkxdc } },
                            url = { https://universe.roboflow.com/hf-w1rwi/meta-mkxdc },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { sep },
                            note = { visited on 2024-04-27 },
                            }