Paleoneutrinos-ImageJ Object Detection Dataset and Pre-Trained Model by PaleoNeutrinos

Images

6 images

Explore Dataset

Here are a few use cases for this project:

Artwork Categorization and Organization: Paleoneutrinos-ImageJ can be used in museums, galleries, and educational institutions to categorize and organize artworks into different classes, including ART movements, styles and time periods, making the artwork collection easy to explore and manage.
Digital Art Archiving and Restoration: Paleoneutrinos-ImageJ could be used to analyze and classify digital scans of artworks in need of restoration, helping archivists and conservators better understand the original style and technique of the artwork, and assisting them in restoring and preserving these works.
Enhanced Art Education: The model can be employed in the development of educational software and applications, providing art students and enthusiasts with relevant information about identified ART classes, styles, and techniques, thus enhancing their learning experience.
Recommender Systems for Art Lovers: The Paleoneutrinos-ImageJ model can be integrated into art discovery platforms or online marketplaces to provide personalized recommendations for users based on their preferred ART classes, broadening their exposure to new works relevant to their interests.
Art Fraud Detection: Paleoneutrinos-ImageJ can be used by art experts and authorities to analyze and compare works of art with known ART classes, assisting in the identification of forgeries, fakes, or misattributed artwork. This would help maintain the integrity and authenticity of the art market.

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{
                            paleoneutrinos-imagej_dataset,
                            title = { Paleoneutrinos-ImageJ Dataset },
                            type = { Open Source Dataset },
                            author = { PaleoNeutrinos },
                            howpublished = { \url{ https://universe.roboflow.com/paleoneutrinos/paleoneutrinos-imagej } },
                            url = { https://universe.roboflow.com/paleoneutrinos/paleoneutrinos-imagej },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-05-05 },
                            }