Suggestion: Explore Different Machine Learning Models for Garbage Classification #43
Description
Description:
To improve the accuracy and robustness of the garbage classification project, I suggest experimenting with various machine learning models. Here are some options:
- CNN Architectures
VGG: Simple yet deep, effective for image classification tasks.
ResNet: Uses residual connections for better training of deeper networks.
EfficientNet: Balances accuracy and efficiency for a range of applications. - Transfer Learning Models
MobileNet: Lightweight and efficient, ideal for mobile and edge deployments.
ResNet50: Widely used for transfer learning with strong performance.
InceptionV3: Good trade-off between accuracy and computational cost. - Classical Machine Learning Models (with extracted features)
Random Forest: Ensemble method, effective with structured feature data.
SVM (Support Vector Machine): Works well for high-dimensional data.
k-NN (k-Nearest Neighbors): Simple, intuitive, and effective for small datasets.