English(EN) I Trained Two CNNs on 101 Food Categories. The Gap Was 42%

从头开始训练的CNN模型与预训练模型相比存在显著差距

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-23 00:01

一项在Food-101数据集上比较两个卷积神经网络（CNN）的实验揭示了使用预训练权重的重要优势。从头开始训练的TinyVGG在30个epoch后仅达到23%的准确率，而利用ImageNet预训练权重并仅训练分类器头的ResNet50在同期内达到了超过65%的准确率。初始训练epoch就显示出显著差异，TinyVGG为2%，ResNet50为52%，凸显了预训练模型中嵌入的先验知识的价值。 AI

影响证明了迁移学习和预训练权重在图像分类任务中实现高性能的关键重要性。

排序理由该条目描述了一个在数据集上比较两个CNN模型的实验，详细说明了它们基于训练方法的性能差异。[lever_c_demoted from research: ic=1 ai=1.0]

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Towards AI TIER_1 English(EN) · Yokeswaran · 2026-07-23 00:01

我用101种食物类别训练了两个CNN。差距是42%

<h4>What that number taught me about pretrained weights, building end-to-end AI systems, and why reading about deep learning is never enough.</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*LoGbecRg8KKndUkwiULxHw.png" /><figcaption>Image generated by ChatG…

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我用101种食物类别训练了两个CNN。差距是42%

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