CIFAR-10 Few-Shot Learning Model Evaluation
Performance analysis of different model architectures on few-shot learning for CIFAR-10 image classification.
Created on April 28|Last edited on April 28
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CIFAR-10 Few-Shot Learning Model Evaluation
Overview
This report documents the performance evaluation of four different CNN architectures on few-shot learning using the CIFAR-10 dataset. The evaluation was conducted on April 28, 2025, as part of the `cifar10-fewshot-eval` project.
Model Architectures Evaluated
Four popular convolutional neural network architectures were evaluated:
- VGG16
- ResNet18
- DenseNet121
- EfficientNet B0
Results
Accuracy Comparison
| Model Architecture | Few-Shot Accuracy |
|-------------------|-------------------|
| VGG16 | 55.55% |
| ResNet18 | 38.84% |
| DenseNet121 | 32.34% |
| EfficientNet B0 | 26.25% |
Key Findings
- **VGG16** demonstrates superior performance with 55.55% accuracy, significantly outperforming other architectures
- **ResNet18** achieves the second-best performance with 38.84% accuracy
- **DenseNet121** and **EfficientNet B0** show lower performance in this few-shot learning context
Analysis
The substantial performance gap between VGG16 and other architectures suggests that VGG16's architecture may be better suited for few-shot learning on the CIFAR-10 dataset. This could be due to:
1. VGG16's simpler, more uniform structure may transfer knowledge more effectively in low-data regimes
2. The larger parameter count in VGG16 might provide more capacity for learning from limited examples
3. The sequential nature of VGG16 without skip connections might benefit the specific transfer learning approach used
Run Information
- **Run Name:** fiery-paper-1
- **Run ID:** m32djilp
- **Runtime:** 184 seconds
- **Date:** April 28, 2025
Next Steps
Based on these findings, we recommend:
1. Further optimization of VGG16 hyperparameters for few-shot learning
2. Exploration of ensemble methods combining VGG16 with ResNet18
3. Investigation into why modern architectures like EfficientNet underperform in this specific task
4. Expansion of the few-shot protocol to include different shot counts (1-shot, 5-shot, 10-shot)
Run set
2
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