Waste Classification with VGG16 & ResNet50 | Transfer Learning | Deep Learning
Neural Arc
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Waste Classification with VGG16 & ResNet50 | Transfer Learning | Deep Learning
3 050 просмотров · 1 г. назад
Neural Arc
760 подписчиков
3 050 просмотров · 1 г. назад
🚀 Smart Waste Classifier: VGG16 & ResNet50 | Data Augmentation, EarlyStopping, Model Checkpointing
🔗 Resources and Code:
Dataset Used:
https://www.kaggle.com/datasets/techs...
Explore the full project Code:
https://www.kaggle.com/code/codingloa...
📖 In this project, I developed a Smart Waste Classification system using deep learning models VGG16 and ResNet50. The models were trained on an augmented dataset to classify different types of waste efficiently, promoting smarter recycling solutions. I incorporated powerful training techniques like Data Augmentation, EarlyStopping, and Model Checkpointing to enhance performance and prevent overfitting.
🎯 The project aims to assist in automatic waste sorting by achieving high accuracy through advanced CNN architectures and careful model training strategies.
🔍 What’s Covered?
✅ Data Augmentation for better generalization
✅ Transfer Learning with VGG16 and ResNet50
✅ EarlyStopping to prevent overfitting
✅ Model Checkpointing to save the best model
✅ Evaluation and comparison of both models' performance
✅ Visualization of training metrics and results
🔥 Perfect for AI Developers, ML Enthusiasts, and Environmental Tech Innovators looking to apply deep learning to real-world sustainability problems.
📌 Keywords for SEO:
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