(源码)基于PyTorch框架的深度学习分类优化实战.zip

上传者: m0_62153576 | 上传时间: 2025-09-22 16:23:47 | 文件大小: 420KB | 文件类型: ZIP
# 基于PyTorch框架的深度学习分类优化实战 ## 项目简介 本项目是一个基于PyTorch框架的深度学习分类优化实战项目,专注于提高图像分类任务的模型准确率。项目通过实现和测试多种优化策略,包括数据增强、模型选择、优化器选择、学习率更新策略和损失函数选择,来提升模型在CIFAR100数据集上的分类性能。 ## 项目的主要特性和功能 1. 数据增强 实现多种数据增强技术,如随机裁剪、随机水平翻转、随机旋转、颜色抖动等,以增强模型的泛化能力。 高级数据增强技术,如随机擦除、MixUp、CutMix、AutoAugment等,通过实验对比选择最优方案。 2. 模型选择 选择并实现多种深度学习模型,包括ResNet、WideResNet、ShuffleNet、MobileNet等,通过实验对比选择最优模型。 探索最新的Transformer模型,如VIT、Swin、CaiT等,以进一步提升模型性能。

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