paper-with-me

Classification 벤치마크

Classification on MHIST

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Accuracy

77.99 80.5 83.01 85.52 88.03 2022-12 2026-11 MoCo-v2 (ResNet-50) — 85.88 (2022-12-09) Supervised (ViT-S/16) — 81.68 (2022-12-09) Barlow Rwins (ResNet-50) — 81.27 (2022-12-09) DINO (ViT-S/16) — 79.43 (2022-12-09) Supervised (ResNet-50) — 78.92 (2022-12-09) SwAV (ResNet-50) — 77.99 (2022-12-09) MoCo-v2 (ResNet-50) — 88.03 (2024-08-15) Barlow Twins (ResNet-50) — 84.03 (2024-08-15) SwAV (ResNet-50) — 83.21 (2024-08-15) MoCo-v2 (ResNet-50) — 85.88 (2022-12-09) MoCo-v2 (ResNet-50) — 88.03 (2024-08-15)
RankModel Accuracy Extra Training Data PaperCodeYear
1 MoCo-v2 (ResNet-50) 88.03 ✓ Improved transferability of self-supervised learning models through batch normalization finetuning vpulab/bn_finetuning 2024
2 MoCo-v2 (ResNet-50) 85.88 ✓ Benchmarking Self-Supervised Learning on Diverse Pathology Datasets kaiko-ai/eva 2022
3 Barlow Twins (ResNet-50) 84.03 ✓ Improved transferability of self-supervised learning models through batch normalization finetuning vpulab/bn_finetuning 2024
4 SwAV (ResNet-50) 83.21 ✓ Improved transferability of self-supervised learning models through batch normalization finetuning vpulab/bn_finetuning 2024
5 Supervised (ViT-S/16) 81.68 ✓ Benchmarking Self-Supervised Learning on Diverse Pathology Datasets kaiko-ai/eva 2022
6 Barlow Rwins (ResNet-50) 81.27 ✓ Benchmarking Self-Supervised Learning on Diverse Pathology Datasets kaiko-ai/eva 2022
7 DINO (ViT-S/16) 79.43 ✓ Benchmarking Self-Supervised Learning on Diverse Pathology Datasets kaiko-ai/eva 2022
8 Supervised (ResNet-50) 78.92 ✓ Benchmarking Self-Supervised Learning on Diverse Pathology Datasets kaiko-ai/eva 2022
9 SwAV (ResNet-50) 77.99 ✓ Benchmarking Self-Supervised Learning on Diverse Pathology Datasets kaiko-ai/eva 2022
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