Case3 Object Detection Dataset and Pre-Trained Model by Pendetection

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Explore Dataset

Here are a few use cases for this project:

Quality Control in Manufacturing: "Case3" could be used in a factory assembly line to identify defective or improperly assembled parts. The 'wrong' and 'corrected' class identification can help flag parts for further inspection or repairs.
Interactive Learning Tools: This model could be implemented in educational platforms for teaching purposes. For example, students solve problems, and the system can identify whether the solution is 'wrong' or 'corrected,' providing instant feedback.
Retail: It could be utilized in stores to verify whether items on display or in stock are misplaced. Products wrongly placed could be identified and corrected, improving shelf management and inventory control.
Handwriting Analysis: The model can be used to examine written text or hand-drawn diagrams, identifying errors and alterations. This could be particularly useful in fields such as document verification, or learning platforms teaching handwriting or drawing.
Crime Scene Investigation: Forensic scientists could use "Case3" to identify whether a potential piece of evidence has been mishandled or altered, thus ensuring the integrity of the evidence.

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{
                            case3_dataset,
                            title = { Case3 Dataset },
                            type = { Open Source Dataset },
                            author = { Pendetection },
                            howpublished = { \url{ https://universe.roboflow.com/pendetection-d2ibn/case3 } },
                            url = { https://universe.roboflow.com/pendetection-d2ibn/case3 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jul },
                            note = { visited on 2024-04-25 },
                            }