scuff_fyp Object Detection Dataset and Pre-Trained Model by Scuff

Images

798 images

Explore Dataset

Here are a few use cases for this project:

Quality Control in Manufacturing: This model could be used in a manufacturing setting to identify imperfections on mass-produced objects like bottles, toys, appliances, etc. It can be effectively used for ensuring quality control by flagging objects with unacceptable or marginal scuffs.
Retail Product Assessment: Within the retail industry, 'scuff_fyp' model can help in assessing the condition of returned items. This can assist retailers in classifying products as sellable, marginally damaged, or unsellable, based on the scuff defects identified by the model.
Cosmetic or Pharmaceutical Packaging: In production lines of cosmetic or pharmaceutical products, this computer vision model could be used to spot defects on packaging surfaces, particularly on bottles of beauty products like shampoo, conditioners, or on medicinal bottles.
Antique Item Valuation: The system could be used to identify surface-level imperfections on antique items being prepped for auction or sale. The identified scuffs could then relate to the item's overall condition and value.
Car Industry Quality Checks: 'scuff_fyp' could be implemented within automobile manufacturing to identify surface scuffs on newly manufactured cars, contributing to ensuring the exterior quality of the vehicles leaving the production line.

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{
                            scuff_fyp_dataset,
                            title = { scuff_fyp Dataset },
                            type = { Open Source Dataset },
                            author = { Scuff },
                            howpublished = { \url{ https://universe.roboflow.com/scuff/scuff_fyp } },
                            url = { https://universe.roboflow.com/scuff/scuff_fyp },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jul },
                            note = { visited on 2024-05-08 },
                            }