Graphene Detector Instance Segmentation Dataset and Pre-Trained Model by kyunghee univ

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Explore Dataset

Here are a few use cases for this project:

Nanotechnology Research: This model can be employed in nanotechnology research where accurate identification of distinct classes of graphene's thickness is important. Enhance the progress of experiments or studies involving the manipulation of atom-thin graphene layers.
Quality Control in Graphene Production: Industries producing graphene could use the Graphene Detector to ascertain the quality of their finished products. This can help in ensuring only products that meet the specification reach the market.
Microscopy Imaging: Researchers using electron or atomic force microscopy could leverage this model to automatically identify and categorize images of graphene. It could save valuable research time and reduce human errors.
Educational Demonstrations: Educators in the field of materials science and engineering might use this model to visualize and explain the differences between Thin, Thick, and Very Thin graphene to students.
Consumer Electronics Manufacturing: Manufacturers of consumer electronics, especially in battery technology and next-gen electronic devices, can use this model to detect and sort the different classes of graphene used in the manufacturing process. It would enhance the efficiency and productivity of the production line.

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{
                            graphene-detector_dataset,
                            title = { Graphene Detector Dataset },
                            type = { Open Source Dataset },
                            author = { kyunghee univ },
                            howpublished = { \url{ https://universe.roboflow.com/kyunghee-univ-bzp0k/graphene-detector } },
                            url = { https://universe.roboflow.com/kyunghee-univ-bzp0k/graphene-detector },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jul },
                            note = { visited on 2024-04-28 },
                            }