EfficientNet-B0 Architecture Overview
The EfficientNet-B0 network consists of:
- Stem
- Initial layer with a standard convolution followed by a batch normalization and a ReLU6 activation.
- Convolution with 32 filters, kernel size 3×3, stride 2.
- Body
- Consists of a series of MBConv blocks with different configurations.
- Each block includes depthwise separable convolutions and squeeze-and-excitation layers.
- Example configuration for MBConv block:
- Expansion ratio: The factor by which the input channels are expanded.
- Kernel size: Size of the convolutional filter.
- Stride: The stride length for convolution.
- SE ratio: Ratio for squeeze-and-excitation.
- Head
- Includes a final convolutional block, followed by a global average pooling layer.
- A fully connected layer with a softmax activation function for classification.
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
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