### EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
**TL;DR**
We propose a simple yet highly effective model scaling method for convolutional neural networks (CNNs). This method, called EfficientNet, uniformly scales all dimensions of a baseline network: width, depth, and resolution. By carefully balancing these dimensions, we can achieve better accuracy and efficiency than other scaling methods.
**Introduction**
CNNs have achieved remarkable success in various computer vision tasks. However, training large CNNs is computationally expensive and time-consuming. To address this challenge, researchers have proposed various model scaling methods to improve the accuracy and efficiency of CNNs.
**EfficientNet Architecture**
The EfficientNet architecture is based on the following principles:
* **Uniform Scaling:** We scale all dimensions of a baseline network: width, depth, and resolution. This simple approach allows us to find the optimal combination of these dimensions for a given task.
* **Compound Scaling:** We apply the same scaling factor to all layers in the network. This ensures that the network’s architecture remains balanced as it is scaled.
* **Depthwise Separable Convolutions:** We use depthwise separable convolutions as the primary building block of our networks. This technique helps reduce the computational cost of the network while maintaining accuracy.
**Experimental Results**
We evaluated EfficientNet on a variety of image classification tasks. Our results show that EfficientNet achieves better accuracy and efficiency than other scaling methods. For example, on the ImageNet dataset, EfficientNet-B0 achieves top-1 accuracy of 77.3% with 5.3 billion FLOPs, which is significantly better than other state-of-the-art models.
**Conclusion**
EfficientNet is a simple yet effective model scaling method for CNNs. By uniformly scaling all dimensions of a baseline network, we can achieve better accuracy and efficiency than other scaling methods. We believe that EfficientNet will be a valuable tool for researchers and practitioners in the field of computer vision.
**Additional Resources**
* [EfficientNet GitHub repository](https://github.com/tensorflow/efficientnet)
* [EfficientNet paper](https://arxiv.org/abs/1905.11946)
* [TensorFlow Hub EfficientNet models](https://tfhub.dev/tensorflow/collections/efficientnet/1).
