Tag: ImageNet
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ViT vs Swin vs ConvNeXt: ImageNet Accuracy at 4.5G FLOPs
ConvNeXt-T beats ViT-S by 2.2% and Swin-T by 0.8% at 4.5G FLOPs. Here's the benchmark data and why pure convolutions still win at production scale.
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DeiT III vs DINOv2: ViT ImageNet Accuracy Without Labels
Compare DeiT III vs DINOv2 on ViT ImageNet accuracy: one needs labels, one doesn't. Discover which self-supervised approach wins and why it matters.
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ViT Review: 16×16 Patches That Replaced Convolutions
ViT-B/16 hits 77.9% top-1 accuracy on ImageNet with 16x16 patches and no convolutions. We review the architecture, training tricks, and where it breaks.
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Transfer Learning์ผ๋ก ์๋ ๋ฐ์ดํฐ ๊ณ ์ฅ ์ง๋จ ๊ตฌํ
Transfer Learning on 50 images: fine-tune ImageNet CNNs for 94% fault detection accuracy. Beats training from scratch with 1000+ samples.