Variants of EfficientNet Model
EfficientNet offers several variants, denoted by scaling coefficients like B0, B1, B2, etc. These variants differ in depth, width, and resolution based on the compound scaling approach. For example:
- EfficientNet-B0: The baseline model with moderate depth, width, and resolution.
- EfficientNet-B1 to B7: Successively larger variants achieved by increasing the compound scaling coefficient φ.
- EfficientNet-Lite: Lightweight variants designed for mobile and edge devices, achieving a good balance between performance and efficiency.
Each variant of EfficientNet offers a trade-off between model size, computational cost, and performance, catering to various deployment scenarios and resource constraints.
Efficientnet Architecture
In the field of deep learning, the quest for more efficient neural network architectures has been ongoing. EfficientNet has emerged as a beacon of innovation, offering a holistic solution that balances model complexity with computational efficiency. This article embarks on a detailed journey through the intricate layers of EfficientNet, illuminating its architecture, design philosophy, training methodologies, performance benchmarks, and more.
Table of Content
- Efficientnet
- EfficientNet-B0 Architecture Overview
- EfficientNet-B0 Detailed Architecture
- Depth-wise Separable Convolution
- Inverted Residual Blocks
- Efficient Scaling:
- Efficient Attention Mechanism:
- Variants of EfficientNet Model:
- Performance Evaluation and Comparison
- Conclusion
- FAQs