# From Logistic Regression to ResNet18: A Complete Experimental Comparison of Image Classification on CIFAR-10 > A systematic deep learning experiment project starting from simple logistic regression, gradually comparing the performance of various models (fully connected neural networks, convolutional neural networks, batch normalization, data augmentation, and ResNet18) on the CIFAR-10 dataset, and providing complete learning curves and performance analysis. - 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo) - 发布时间: 2026-06-17T01:37:17.000Z - 最近活动: 2026-06-17T01:51:55.908Z - 热度: 154.8 - 关键词: CIFAR-10, 深度学习, 卷积神经网络, ResNet, 图像分类, PyTorch, 神经网络对比, 批归一化, 数据增强, 机器学习实验 - 页面链接: https://www.zingnex.cn/en/forum/thread/resnet18-cifar-10 - Canonical: https://www.zingnex.cn/forum/thread/resnet18-cifar-10 - Markdown 来源: floors_fallback --- ## [Introduction] From Logistic Regression to ResNet18: CIFAR-10 Image Classification Experiment Comparison Project Title: From Logistic Regression to ResNet18: A Complete Experimental Comparison of Image Classification on CIFAR-10 Abstract: A systematic deep learning experiment project starting from simple logistic regression, gradually comparing the performance of various models (fully connected neural networks, convolutional neural networks, batch normalization, data augmentation, and ResNet18) on the CIFAR-10 dataset, and providing complete learning curves and performance analysis. Original Author and Source: - **Original Author/Maintainer**: uchidaGHub - **Source Platform**: GitHub - **Original Project Title**: cifarClassificationExperiment - **Project Link**: https://github.com/uchidaGHub/cifarClassificationExperiment - **Publication Date**: 2026-06-17 ## Project Background and Objectives ## Project Background and Objectives In the field of deep learning, understanding the performance differences between different model architectures is key to mastering core machine learning concepts. This project helps learners understand the evolution path of image classification tasks from basic to advanced levels by systematically comparing various classic and modern neural network models on the CIFAR-10 dataset. As a standard benchmark dataset in computer vision, CIFAR-10 contains 60,000 32x32 color images across 10 categories, making it an ideal choice for testing model capabilities. ## Experimental Design and Model Evolution ## Experimental Design and Model Evolution The project adopts a progressive experimental design, moving from simple models to complex architectures: ### Phase 1: Basic Models Starting with **logistic regression** (as a baseline comparison), then introducing **fully connected neural networks** to demonstrate the advantages of multi-layer perceptrons and the problem of overfitting. ### Phase 2: Introduction of Convolutional Neural Networks Implementing **two-layer convolutional networks** (local receptive field, weight sharing) and **three-layer convolutional networks** to show the ability of convolutional layers to capture spatial structures of images. ### Phase 3: Optimization Techniques Applying **Adam optimizer** (accelerating convergence), **batch normalization** (solving internal covariate shift), and **data augmentation** (random flipping/cropping to improve generalization). ### Phase 4: ResNet18 Implementing the **ResNet18** architecture, which solves the gradient vanishing problem in deep networks through residual connections. ## Experimental Results and Performance Comparison ## Experimental Results and Performance Comparison The project provides training/test learning curves for each model, showing: - The relationship between model complexity and overfitting risk - The impact of different optimization strategies on convergence speed - The improvement of generalization performance by data augmentation - The help of residual connections for training deep networks Hyperparameter configurations are compared through cross-validation to provide references for practical applications. ## Code Structure and Usage ## Code Structure and Usage The project code is modular: - `dataset.py`: Loads and preprocesses CIFAR-10, implements data augmentation - `model.py`: Defines all model architectures (from linear layers to ResNet blocks) - `utils.py`: General functions for training, evaluation, plotting, and cross-validation - `main.py`: Experiment entry point with an interactive selection interface Model checkpoints are saved in `models/checkpoints`, and final models are stored in the `models` directory. ## Learning Value and Practical Significance ## Learning Value and Practical Significance This project is a deep learning learning guide. Reproducing the experiments allows you to: 1. Understand the evolution of models (from linear to residual networks) 2. Master PyTorch practice (model definition, training loop, evaluation) 3. Cultivate experimental thinking (hyperparameter tuning, model selection) 4. Establish a cognitive baseline for CIFAR-10 model performance. ## Summary and Recommendations ## Summary and Recommendations For beginners: Run the experiments in order, observe changes in learning curves, and think about the principles of technical improvements; refer to the project report `cifarClassificationReport.pdf` for in-depth understanding. For experienced developers: Draw on the code structure (modular design, logging, experiment management).