AI-powered-Waste-Classification-System-using-deep-learning and Waste-Classification

These are competitors offering alternative CNN-based approaches to waste image classification, with A providing more granular multi-class categorization (cardboard-level specificity) versus B's binary organic/recyclable distinction.

AI-powered-Waste-Classification-System-using-deep-learning
35
Emerging
Waste-Classification
28
Experimental
Maintenance 10/25
Adoption 4/25
Maturity 9/25
Community 12/25
Maintenance 0/25
Adoption 5/25
Maturity 9/25
Community 14/25
Stars: 5
Forks: 1
Downloads: —
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stars: 9
Forks: 3
Downloads: —
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
No Package No Dependents
Stale 6m No Package No Dependents

About AI-powered-Waste-Classification-System-using-deep-learning

Salaar-Saaiem/AI-powered-Waste-Classification-System-using-deep-learning

AI-powered waste classification system using deep learning, Combines a custom CNN and EfficientNet (transfer learning). Achieves 99% training and 95% validation accuracy. Classifies images into cardboard, glass, metal, paper, plastic, and trash. Includes prediction, evaluation, and visualization tools.

About Waste-Classification

aniass/Waste-Classification

Waste image classification into organic or recyclable ones with CNN algorithm.

Related comparisons

AI-powered-Waste-Classification-System-using-deep-learning and CNN-Plastic-Waste-Classification AI-powered-Waste-Classification-System-using-deep-learning and CNN-Plastic-Waste-Classification

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