Building a Convolutional Neural Network Image Recognition System from Scratch: Practical Analysis of CSE 144 Deep Learning Final Project

This article analyzes the final project of the CSE 144 Machine Learning and Deep Learning course, published by bli312 on GitHub (Project link: https://github.com/bli312/cnn-image-identifier, published on 2026-05-27). The goal of this project is to build a CNN-based image recognition system from scratch, helping students deeply understand the design, training, and optimization processes of deep learning models, and achieve the integration of theory and practice.

Image recognition is one of the most representative application scenarios of deep learning technology. Since AlexNet made a breakthrough in the ImageNet competition in 2012, CNN has become a core technology in the field of computer vision. For deep learning beginners, hands-on implementation of a CNN image recognition system is the best way to understand the principles. As the final assignment of the CSE144 course, this project aims to consolidate theoretical knowledge through practice and cultivate practical engineering capabilities.

CNN excels in image recognition due to its unique architecture:

• Convolutional Layer: Extracts local features by sliding convolution kernels, reduces parameters through weight sharing, and maintains spatial hierarchy sensitivity;
• Activation Function: ReLU is commonly used to introduce non-linearity, alleviate gradient vanishing, and enable efficient computation;
• Pooling Layer: Downsamples to reduce the size of feature maps, enhances translation invariance, and max pooling retains prominent features;
• Fully Connected Layer: Maps high-level features to classification outputs and makes judgments by integrating features.

Key steps to build a CNN image recognition system:

1. Data Preparation: Collect, clean, preprocess (normalize), and perform data augmentation to improve generalization ability;
2. Model Design: Determine hyperparameters such as network depth and convolution kernel size, often adjusting based on classic architectures like LeNet and AlexNet;
3. Training Process: Select cross-entropy loss function, Adam/SGD optimizer, and use Dropout, early stopping, etc., to prevent overfitting;
4. Evaluation Phase: Validate with an independent test set, calculate metrics like accuracy and F1 score, and analyze category performance differences through confusion matrices.

This project embodies the concept of 'learning by doing', allowing students to understand core concepts:

• Feature Hierarchy Learning: Simulates human vision, automatically learning features from low-level (edges) to high-level (objects);
• End-to-End Learning: End-to-end process from raw pixels to classification, simplifying development;
• Model Generalization: Understand the significance of dataset division and the trade-off between overfitting and underfitting;
• Computational Graph and Backpropagation: Intuitively understand the principles of automatic differentiation and backpropagation.

CNN has a wide range of applications: medical image analysis (tumor detection), autonomous driving (road sign recognition), industrial quality inspection (defect detection), and agriculture (crop disease recognition). In the future, we can explore advanced architectures like ResNet and DenseNet, or learn transfer learning techniques to accelerate the development of new tasks.

This project is a gateway to deep learning. Students not only master programming skills but also establish an intuitive understanding of the principles. It is recommended that readers who are learning machine learning implement similar projects by themselves (e.g., using MNIST/CIFAR-10 datasets), and attach importance to the integration of principles and practice, as the two complement each other.