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CNNs trained from scratch lag significantly behind pretrained models

By PulseAugur Editorial · [1 sources] ·

An experiment comparing two Convolutional Neural Networks (CNNs) on the Food-101 dataset revealed the significant advantage of using pretrained weights. TinyVGG, trained from scratch, achieved only 23% accuracy after 30 epochs, while ResNet50, utilizing pretrained weights from ImageNet and only training a classifier head, reached over 65% accuracy in the same timeframe. The initial training epoch showed a stark difference, with TinyVGG at 2% and ResNet50 at 52% accuracy, highlighting the value of prior knowledge embedded in pretrained models. AI

IMPACT Demonstrates the critical importance of transfer learning and pretrained weights in achieving high performance in image classification tasks.

RANK_REASON The item describes an experiment comparing two CNN models on a dataset, detailing their performance differences based on training methodology. [lever_c_demoted from research: ic=1 ai=1.0]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CNNs trained from scratch lag significantly behind pretrained models

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The item describes an experiment comparing two CNN models on a dataset, detailing their performance differences based on training methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Towards AI TIER_1 English(EN) · Yokeswaran ·

    I Trained Two CNNs on 101 Food Categories. The Gap Was 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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