<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Vision on Jeanphilo Blog</title><link>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/</link><description>Recent content in Vision on Jeanphilo Blog</description><generator>Hugo -- 0.159.2</generator><language>zh-cn</language><lastBuildDate>Sat, 24 Jan 2026 16:36:30 +0800</lastBuildDate><atom:link href="https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/index.xml" rel="self" type="application/rss+xml"/><item><title>单阶段 vs 双阶段目标检测：从候选集合到 NMS 的工程算账</title><link>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/single-stage-vs-two-stage-object-detection/</link><pubDate>Sat, 24 Jan 2026 16:36:30 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/single-stage-vs-two-stage-object-detection/</guid><description>系统对比单阶段与双阶段目标检测的流程、复杂度与工程场景：候选集合规模、NMS/后处理成本、focal loss 与采样策略，并给出纯 NumPy 可运行示例用于算账与验证。</description></item><item><title>Anchor-Based vs Anchor-Free：目标检测两条路线</title><link>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/anchor-based-vs-anchor-free/</link><pubDate>Sat, 24 Jan 2026 16:36:19 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/anchor-based-vs-anchor-free/</guid><description>对比 Anchor-based 与 Anchor-free 检测框架的核心差异、工程取舍与实战场景。</description></item><item><title>IoU 是什么：目标检测评估的核心指标</title><link>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/iou-explained/</link><pubDate>Sat, 24 Jan 2026 16:34:42 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/iou-explained/</guid><description>从公式到工程实践解释 IoU（交并比），并给出可运行示例与评估细节。</description></item><item><title>空洞卷积（Dilated Convolution）：扩大感受野的工程利器</title><link>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/dilated-convolution/</link><pubDate>Sat, 24 Jan 2026 16:33:00 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/dilated-convolution/</guid><description>系统讲清空洞卷积的原理、复杂度与工程应用，并给出最小 PyTorch 示例。</description></item><item><title>NMS 描述：非极大值抑制的原理与工程实践</title><link>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/nms-overview/</link><pubDate>Sat, 24 Jan 2026 16:32:59 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/nms-overview/</guid><description>系统讲清 NMS 的核心流程、IoU 计算与工程取舍，并给出最小 PyTorch 示例。</description></item><item><title>CNN 参数量计算：从卷积核到整网规模</title><link>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/cnn-parameter-count/</link><pubDate>Sat, 24 Jan 2026 16:28:40 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/cnn-parameter-count/</guid><description>系统讲清 CNN 参数量计算方法与常见陷阱，并给出最小 PyTorch 示例。</description></item><item><title>图像自编码是怎么做的：原理、流程与最小实现</title><link>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/image-autoencoder-how/</link><pubDate>Sat, 24 Jan 2026 16:26:15 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/image-autoencoder-how/</guid><description>系统讲清图像自编码的结构、训练目标与工程场景，并给出最小 PyTorch 示例。</description></item><item><title>ViT 结构描述：从 Patch Embedding 到 Transformer 编码器</title><link>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/vit-architecture-overview/</link><pubDate>Sat, 24 Jan 2026 16:25:35 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/ai/vision/vit-architecture-overview/</guid><description>系统讲清 ViT 的结构组件、工作流程与工程实践，并给出最小 PyTorch 示例。</description></item></channel></rss>