Dataset 1 Object Detection Dataset and Pre-Trained Model by Tese

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Explore Dataset

Here are a few use cases for this project:

Industrial Maintenance: Applying the "Dataset 1" vision model in factories to detect faulty machinery or structures. The regular inspection of equipment like boilers, pipework, conveyers, etc. can be automated to swiftly identify signs of damage or wear such as missing bolts, dents, scratches, or cracks.
Transport Safety Checks: The model could be used in transport sectors, from aeronautics to railways, to spot potential defects in vehicles. Ensuring quick detection of cracks, missing bolts, or other surface inconsistencies will aid in preventing accidents and ensuring smoother operations.
Construction Industry: The tool could be applied in the construction field to inspect the integrity of structures, buildings, or bridges. It would help in detecting cracks or other types of damage in these infrastructures, ensuring safety and timely maintenance.
Quality Control in Manufacturing: The model can be utilized in product quality checks to identify any surface defects on products like cars, appliances, furniture, or electronics. It would provide a streamlined process of detecting dents, scratches, or missing parts prior to shipping.
Art and Artifact Restoration: The model could be used to inspect art pieces or historical artifacts for signs of damage that need restoration. It could prove valuable in maintaining and preserving these creations by flagging defects like cracks or missing portions.

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{
                            dataset-1-cpmgg_dataset,
                            title = { Dataset 1 Dataset },
                            type = { Open Source Dataset },
                            author = { Tese },
                            howpublished = { \url{ https://universe.roboflow.com/tese-s64ix/dataset-1-cpmgg } },
                            url = { https://universe.roboflow.com/tese-s64ix/dataset-1-cpmgg },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jul },
                            note = { visited on 2024-05-09 },
                            }