Efficientnet
EfficientNet is a family of convolutional neural networks (CNNs) that aims to achieve high performance with fewer computational resources compared to previous architectures. It was introduced by Mingxing Tan and Quoc V. Le from Google Research in their 2019 paper “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.” The core idea behind EfficientNet is a new scaling method that uniformly scales all dimensions of depth, width, and resolution using a compound coefficient.
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