yolo Object Detection Dataset and Pre-Trained Model by student 02

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Explore Dataset

Here are a few use cases for this project:

Precision Manufacturing: The YOLO model can be used in manufacturing settings to identify defects in precision-made parts such as engine components, circuit boards, or semiconductor wafers.
Quality Control in Food Industry: The model could be useful in identifying defective food items on a production line, for example, spoiled fruits in a packing house or irregularly shaped bakery products.
Pharmaceutical Packaging Quality Check: The computer vision system could be used to inspect pharmaceutical packages, detecting defects like broken seals, damaged packaging, or incorrect labeling.
Surface Defect Detection in Metal Industry: This model could be applied to identifying surface defects (cracks, pits, scratches) in metal plates or tubes during the production process.
Tyre Manufacturing: It can be used to find defects or irregular patterns in the manufacture of tires, enhancing quality control by detecting problems early in the process.

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{
                            yolo-jteva_dataset,
                            title = { yolo Dataset },
                            type = { Open Source Dataset },
                            author = { student 02 },
                            howpublished = { \url{ https://universe.roboflow.com/student-02/yolo-jteva } },
                            url = { https://universe.roboflow.com/student-02/yolo-jteva },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { apr },
                            note = { visited on 2024-05-13 },
                            }