chu_zhushu Object Detection Dataset and Pre-Trained Model by chuzhushu

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Explore Dataset

Here are a few use cases for this project:

Historical Document Analysis: The chu_zhushu model can be used by researchers and historians to analyze ancient texts and manuscripts, identifying different glyph and slip classes to better understand the context and meaning of the content.
Art Restoration and Conservation: Art conservators can use this model to study and examine old paintings, inscriptions, or any other artwork containing glyphs and slips. This can aid in the restoration process and help preserve the original intent of the artist.
Cultural Anthropology and Archaeology: Anthropologists and archaeologists can utilize the chu_zhushu model to recognize and categorize glyphs and slips found in various artifacts, enabling them to draw conclusions about the people and cultures that created them.
Language Processing and Translation: Language processing software could benefit from the ability to recognize glyph and slip classes, as this can help in deciphering ancient languages, constructing new historical dictionaries, and automating translations between languages or dialects.
Education and Training: The chu_zhushu model can be used as a tool for teaching students or professionals the different types of glyphs and slips, as well as their significance in understanding historical writings and the development of languages.

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{ chu_zhushu_dataset,
    title = { chu_zhushu Dataset },
    type = { Open Source Dataset },
    author = { chuzhushu },
    howpublished = { \url{ https://universe.roboflow.com/chuzhushu/chu_zhushu } },
    url = { https://universe.roboflow.com/chuzhushu/chu_zhushu },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2023 },
    month = { aug },
    note = { visited on 2023-11-30 },
}