Fill level Object Detection Dataset and Pre-Trained Model by Majid Alikhani

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Explore Dataset

Here are a few use cases for this project:

Smart Recycling Kiosks: The "Fill Level" model can be used in recycling machines or kiosks, helping to determine whether bottles are empty or not before they're accepted for recycling. If a bottle is not empty, the machine could return it for it to be emptied, avoiding potential spills and messes.
Fitness Applications: In a fitness app, the model can track the consumption of water during workouts, reminding users to stay hydrated by keeping count of the water they've consumed and the amount left in the bottle.
Beverage Industry Quality Control: Companies producing bottled beverages can use the model to ensure each bottle is filled to the correct level during the production process, improving quality control and consistency across all units.
Retail Inventory Management: In retail environments, the model could be used to verify that no products have been partially consumed or tampered with before being returned to the shelves.
Environment Monitoring: For environmental studies or organizations, this model can be employed to monitor water usage in public places, helping to measure public consumption patterns and implement water conservation measures.

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{
                            fill-level_dataset,
                            title = { Fill level Dataset },
                            type = { Open Source Dataset },
                            author = { Majid Alikhani },
                            howpublished = { \url{ https://universe.roboflow.com/majid-alikhani/fill-level } },
                            url = { https://universe.roboflow.com/majid-alikhani/fill-level },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { oct },
                            note = { visited on 2024-05-10 },
                            }