Comparing the usage of Visual Language Models (VLM) and Convolutional Neural Network to classify garbage images of the dataset "Garbage Image Dataset" by Farzad Nekouei.
Tested models:
| Model Name | Params | Accuracy | Precision | Recall | F1-Score |
|---|---|---|---|---|---|
| EfficientNetV2B2 | 8M | 90.51% | 92.11% | 88.18% | 89.62% |
| SmolVLM-1.7B (Zero-Shot Prompting) | 1.7B | 79.05% | 68.57% | 69.19% | 68.62% |
| SmolVLM-500M (Zero-Shot Prompting) | 500M | 62.85% | 62.25% | 54.98% | 55.32% |
| SmolVLM-1.7B (Few-Shot Prompting) | 1.7B | 54.55% | 62.68% | 50.68% | 48.70% |
| SmolVLM-500M (Few-Shot Prompting) | 500M | 53.36% | 51.34% | 50.73% | 45.13% |
| SmolVLM-256M (Zero-Shot Prompting) | 256M | 50.20% | 48.91% | 43.58% | 44.38% |
| SmolVLM-256M (Few-Shot Prompting) | 256M | 50.20% | 52.75% | 46.30% | 43.60% |
- conda==25.3.1
- python==3.10
- tensorflow
- keras
- pandas
- numpy
- matplotlib
- seaborn
- jupyterlab
- opencv-python
- scikit-learn
- pytorch
- transfomers
See the complete configuration in environment.win.yml