Neural Network Comparisons
Compare top neural networks and their performance
In this review, we analyze four leading neural networks—ResNet-50, EfficientNet-B0, Vision Transformer (ViT-B/16), and MobileNetV2— across accuracy, model size, and computational cost to help you choose the right architecture for your project.
1. Accuracy Comparison
The bar chart below illustrates Top-1 ImageNet accuracy for each model.
2. Model Complexity
The table summarizes the number of parameters and FLOPs required by each network.
| Model | Top-1 Accuracy (%) | Parameters (Millions) | FLOPs (Billions) |
|---|---|---|---|
| ResNet-50 | 76.0 | 25.6 | 4.1 |
| EfficientNet-B0 | 77.1 | 5.3 | 0.39 |
| ViT-B/16 | 81.8 | 86.0 | 17.6 |
| MobileNetV2 | 71.8 | 3.4 | 0.3 |
3. Choosing the Right Model
If you need high accuracy and have ample compute, ViT-B/16 is the top performer. For resource-constrained environments, EfficientNet-B0 offers a great balance, while MobileNetV2 is ideal for mobile applications.
Conclusion
Understanding trade-offs between accuracy, size, and FLOPs is crucial. Use these comparisons to guide architecture selection tailored to your deployment needs.