<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Classification on 南巷</title><link>https://nx.wmlab.top/tags/classification/</link><description>Recent content in Classification on 南巷</description><generator>Hugo -- gohugo.io</generator><language>zh-CN</language><managingEditor>liuwenhao1968@163.com (南巷)</managingEditor><webMaster>liuwenhao1968@163.com (南巷)</webMaster><copyright>© 2026 南巷</copyright><lastBuildDate>Sat, 15 Feb 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://nx.wmlab.top/tags/classification/index.xml" rel="self" type="application/rss+xml"/><item><title>Classification：通用图像分类训练模板</title><link>https://nx.wmlab.top/portfolio/kaggle-leaf-classification/</link><pubDate>Sat, 15 Feb 2025 00:00:00 +0000</pubDate><author>liuwenhao1968@163.com (南巷)</author><guid>https://nx.wmlab.top/portfolio/kaggle-leaf-classification/</guid><description>面向研究和工程复用的 PyTorch 图像分类训练框架，支持替换主流网络、自定义数据集、SwanLab 日志监控和推理可视化，并以树叶分类作为完整示例。</description></item><item><title>Kaggle 树叶图像分类</title><link>https://nx.wmlab.top/competitions/kaggle-leaf-competition/</link><pubDate>Sat, 01 Feb 2025 00:00:00 +0000</pubDate><author>liuwenhao1968@163.com (南巷)</author><guid>https://nx.wmlab.top/competitions/kaggle-leaf-competition/</guid><description>基于 ResNet50 优化细粒度特征表达，结合 Label Smoothing、Focal Loss 与 AdamW + CosineAnnealing 策略。</description></item></channel></rss>