bottle chips Object Detection Dataset and Pre-Trained Model by East West University

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Explore Dataset

Here are a few use cases for this project:

Recycling Industry: This model could be crucial for recycling plants to automatically sort through waste. It can identify items that are bottles, chips, or a compound of the two, thereby enhancing waste management efficiency.
Environmental Monitoring: Organizations aiming to monitor and reduce pollution in bodies of water can deploy this model. It can help in identifying and quantifying plastic bottles and chip packs that are discarded in water bodies.
Retail Inventory Management: Retailers can use this model to keep track of their inventory of bottled products and chips. The model can help recognize and count these items faster and more accurately.
Public Health Monitoring: Public health departments can use this model to monitor areas for potential health hazards. Recognizing and counting littered plastic bottles and chip packets can help identify areas that need more intensive waste management efforts.
Smart Homes: This model can be used in smart waste bins to properly sort recycling or compost materials, notifying residents when items are improperly disposed, or even automatically opening the correct bin when an item is detected.

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{
                            bottle-chips_dataset,
                            title = { bottle chips  Dataset },
                            type = { Open Source Dataset },
                            author = { East West University },
                            howpublished = { \url{ https://universe.roboflow.com/east-west-university/bottle-chips } },
                            url = { https://universe.roboflow.com/east-west-university/bottle-chips },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jul },
                            note = { visited on 2024-06-18 },
                            }