newAss Computer Vision Project

simon lee

Updated 2 years ago

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Classes (22)
Crop_Top
Dress
Long skirt
Long-sleeved T-shirts
Mask
Miniskirt
Person
Ripped Jeans
Shirt and Blouse
Shorts
Sleeveless
Sweater and Hoodie
T-shirt Tops Trousers
bare-back
crop_top
low_neckline
low_nekcline
ripped_pants
shorts
sleeveless
Description

Here are a few use cases for this project:

  1. "Dress Code Verification": The "newAss" computer vision model can be deployed in schools, colleges, and corporate offices to enforce a particular dress code. The system can automatically recognize individuals who are not adhering to the institution's dress code and take necessary action.

  2. "Fashion Retail Store": Retail companies can use this model to assist their customers in finding clothes that suit their preferred style. This could be particularly beneficial in online shopping, where the system could identify different clothing items and filter them according to the user's preferences.

  3. "Media Content Filtering": Online platforms can apply the model to scan and filter inappropriate content, ensuring that no clothing violates their content policy. Platforms such as YouTube, Instagram, and Facebook could use it to maintain their community guidelines.

  4. "Public Venues Dress Code Compliance": Some public places like churches, temples, mosques, or upscale restaurants have strict dress codes. The "newAss" model can be used to detect if visitors are wearing acceptable clothing before they enter these places.

  5. "Movie and TV Shows Rating": Film and television show producers could utilize this model to determine the appropriateness of clothing worn by characters in different scenes. This helps in rating the content appropriately according to different age groups and viewer sensitivity.

Supervision

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Cite This Project

LICENSE
CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            newass_dataset,
                            title = { newAss Dataset },
                            type = { Open Source Dataset },
                            author = { simon lee },
                            howpublished = { \url{ https://universe.roboflow.com/simon-lee-ud1fw/newass } },
                            url = { https://universe.roboflow.com/simon-lee-ud1fw/newass },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { apr },
                            note = { visited on 2024-11-21 },
                            }
                        
                    

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