MachLearn Classification Dataset and Pre-Trained Model by MachLearn

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Explore Dataset

Here are a few use cases for this project:

Retail Analytics: You can use the "MachLearn" model to analyze consumer behavior within various store classes. By identifying and classifying different store classes, a business can gain insights on customer habits, popular areas of the store, time spent per store class, etc., which can help optimize store layout and product placement.
Safety and Security: The model can be used to monitor activity in public places like stores to detect any unusual activities (e.g., overcrowded stores, long queues, etc.). This can also support theft detection and prevention initiatives.
Urban Planning: City planners and governments can utilize this model for research and planning. By classifying stores, they can get an overview of the local business environment and strategically plan for more balanced economic development, including the allocation of resources and infrastructure.
Real-Time Store Classification: App developers can use this model for real-time location-based services. Users could get information on what kind of stores are around them just by pointing their camera around—useful for tourists, newcomers, or even local residents.
Commercial Real Estate: Real estate agencies and investors can utilize this model to identify and classify various store types in different neighborhoods or areas. This can help in determining commercial property values, predicting future trends, and making investment decisions.

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{
                            machlearn_dataset,
                            title = { MachLearn Dataset },
                            type = { Open Source Dataset },
                            author = { MachLearn },
                            howpublished = { \url{ https://universe.roboflow.com/machlearn-8ldow/machlearn } },
                            url = { https://universe.roboflow.com/machlearn-8ldow/machlearn },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { may },
                            note = { visited on 2024-05-08 },
                            }