Roboflow Universe Thesis Project Classification Model for Waste Materials in Residential Areas

Classification Model for Waste Materials in Residential Areas Computer Vision Project

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The thesis project aims to develop a multi-label classification model that can identify residential waste materials along with displaying their target categories (i.e., recyclable, non-recyclable, compostable, e-waste, and medical waste) in real-time for efficient waste management using deep learning techniques and sensor fusion.

The waste materials will be classified according to the waste segregation advisory of the DENR under five (5) waste categories: recyclable, non-recyclable, compostable, e-waste, and medical waste. The study will involve identifying the following twelve (12) classes:

Bottle
Tin Can
Cardboard
Paper
Soft Plastic
Tetra Packs
Leftovers
Food Peeling
Battery
Gadgets
Face Mask
Gloves

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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{
                            classification-model-for-waste-materials-in-residential-areas_dataset,
                            title = { Classification Model for Waste Materials in Residential Areas Dataset },
                            type = { Open Source Dataset },
                            author = { Thesis Project },
                            howpublished = { \url{ https://universe.roboflow.com/thesis-project-sacr3/classification-model-for-waste-materials-in-residential-areas } },
                            url = { https://universe.roboflow.com/thesis-project-sacr3/classification-model-for-waste-materials-in-residential-areas },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { may },
                            note = { visited on 2024-04-27 },
                            }