fashion_yolo_prism Object Detection Dataset and Pre-Trained Model by Prismfashion

Images

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Explore Dataset

Here are a few use cases for this project:

E-commerce Recommendation Systems: The fashion_yolo_prism model can be used by e-commerce platforms to categorize every uploaded picture of clothes into particular classes based on color and style. This functionality allows for personalized recommendations to customers based on previous purchases or browsing history.
Warehouse Automation Companies: Companies that automate warehouse operations could use this model to sort items based on their classes. It allows for easy identification of clothing items, making the inventory process smoother and quicker.
Personalized Clothing Apps: Developers can incorporate this model into apps that assist customers with organizing their wardrobes or creating outfits. It helps in identifying the color and type of clothes for effective organization and clothing pair suggestions.
Retail Theft Prevention: Retail stores can use this model in their surveillance systems to identify specific pieces of clothing. It can help identify when an item is moved or taken from the store, thus reducing theft.
Augmented Reality Shopping: AR applications can leverage this model to allow users to virtually "try on" clothes. By recognizing the type of clothing, users can see how different styles and colors will look on them without physically visiting the store.

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{
                            fashion_yolo_prism_dataset,
                            title = { fashion_yolo_prism Dataset },
                            type = { Open Source Dataset },
                            author = { Prismfashion },
                            howpublished = { \url{ https://universe.roboflow.com/prismfashion/fashion_yolo_prism } },
                            url = { https://universe.roboflow.com/prismfashion/fashion_yolo_prism },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-07-27 },
                            }