Hello everyone, I am Weixue AI. Today I will introduce you to Deep Learning Practice 49-Application of Car Brand and Model Classification and Recognition Based on Convolutional Neural Network and Attention Mechanism. This project is like a smart and keen eye. , staring intently at the world of cars. This project uses PyTorch as a powerful tool to provide a deep learning arena, allowing us to design and train a powerful model. This model is like a powerful car engine, capable of extracting unique features from pictures of cars.
Table of contents
- introduction
- Dataset introduction
- Understanding Convolutional Neural Networks and Attention Mechanisms
- Build a model
- data preprocessing
- model training
- Model Evaluation and Results Visualization
- Summarize
1 Introduction
In the current field of deep learning, image classification has become a very mature field. This article will introduce how to use convolutional neural network (CNN) and attention mechanism to classify and recognize car makes and models. We will use PyTorch, a powerful deep learning framework, and the StanfordCars dataset to achieve this task.
This project uses an attention mechanism, like a spotlight, to focus on the most important parts of the picture. Through the attention mechanism, we can make the model smarter to find subtle differences related to car make and model, thereby improving the accuracy of classification.
To cultivate this intelligent model, we invested a lot of time and effort in training it using labeled datasets. These datasets are like an assortment of cars, each with a unique make and model. By feeding this data into the model, it is like feeding it an endless supply of cars, allowing it to gradually learn to recognize and classify different makes and models of cars.
When the project enters the testing phase, we will not