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1. Contents

• Algorithm model introduction
• Model usage training
• Model Evaluation
• Project expansion

2. Introduction to Algorithm Model

Image recognition is an important research direction in the field of computer vision. It has a wide range of applications in face recognition, object detection, image classification, and other fields. With the popularity of mobile devices and the limitation of computing resources, it is particularly important to design efficient image recognition algorithms. MobileNetV2 is a lightweight convolutional neural network model proposed by the Google team in 2018. It aims to greatly reduce the number of model parameters and computational complexity while maintaining accuracy, making it suitable for resource-constrained scenarios such as mobile devices and embedded systems.

background:

MobileNetV2 is the second generation model of the MobileNet series, which is a series of lightweight convolutional neural networks developed by the Google team specifically for mobile devices and embedded systems. MobileNetV2 is an improved version of MobileNetV1, which further improves the accuracy and efficiency of the model while maintaining its lightweight characteristics.

The MobileNetV2 algorithm was proposed to address the problem of poor performance of traditional convolutional neural networks on mobile devices, such as the large amount of computation and number of parameters, which make it impossible for the model to run efficiently in a resource-constrained environment.

principle:

The MobileNetV2 algorithm achieves efficient image recognition through a series of technical strategies, including:

1. Basic building blocks: inverted residual structure

MobileNetV2 uses a basic building block called "inverted residual structure", namely Inverted Residual Block. This structure is opposite to the traditional residual block. It first reduces the dimension (reducing the number of channels with 1x1 convolution) and then increases the dimension (increasing the number of channels with 3x3 depth-separable convolution) to achieve lightweight and reduce model complexity.

2. Activation function: Rectified Linear Unit (ReLU6)

MobileNetV2 uses ReLU6 as the activation function. Compared with the traditional ReLU function, ReLU6 outputs 0 in the negative part and a maximum value of 6 in the positive part, making the model easier to train and more robust.

3. Depthwise Separable Convolution

MobileNetV2 widely uses Depthwise Separable Convolution, which decomposes the standard convolution operation into depth convolution and point-by-point convolution, thereby greatly reducing the amount of computation and the number of parameters.

4. Network architecture design

MobileNetV2 builds the network by introducing multiple feature maps of different resolutions. Using these feature maps at different levels enables the network to learn the semantic features of images at different scales, improving the accuracy of image recognition.

application:

MobileNetV2 is widely used in image recognition tasks on mobile devices and embedded systems due to its lightweight characteristics and efficient computing power. In practical applications, we can use the pre-trained MobileNetV2 model and migrate it to specific image recognition tasks, thereby achieving high-quality image recognition with limited resources.

MobileNetV2 performs well in tasks such as image classification, object detection, and face recognition, and has become one of the preferred algorithms for mobile image recognition.

3. Model Usage and Training

In this article, in order to demonstrate how to implement an image recognition and classification system, five common fruit datasets are selected, and their folder structure is shown in the figure below.

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