MATLAB Simulation of Target Recognition Algorithm Based on Efficient-Net
In recent years, with the development of computer vision technology and the rise of deep learning, object recognition algorithms have become one of the research hotspots. Among them, Efficient-Net is a lightweight convolutional neural network model, which greatly reduces the number of parameters while maintaining high accuracy, and achieves faster training and inference speeds, and has received widespread attention. This article will introduce how to use Efficient-Net to realize target recognition, and provide MATLAB code for simulation.
1. Introduction to Efficient-Net
Efficient-Net is a lightweight convolutional neural network model proposed by the Google Brain team. It expands network depth, width, and resolution in a balanced manner by using compound coefficients, thereby greatly reducing the number of parameters while maintaining high accuracy, and achieving faster training and inference speeds. The Efficient-Net model uses three key components: Depthwise Separable Convolution, Inverted Residual Connection, and Linear Upsampling.
2. Data set selection
In order to train and verify the target recognition algorithm, we need to prepare a suitable data set. This paper selects the COCO dataset for training and testing, which contains more than 330K labeled objects, covering 80 common objects of different categories.
3. Algorithm implementation
1. Load the dataset
In MATLAB, we can use the imagedatastore function to load a dataset into memory. Specific steps are as follows:
% 加载训练集
trainData =