Classification On Mhist
평가 지표
Accuracy
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | Accuracy | Paper Title | Repository |
|---|---|---|---|
| SwAV (ResNet-50) | 77.99 | Benchmarking Self-Supervised Learning on Diverse Pathology Datasets | |
| MoCo-v2 (ResNet-50) | 88.03 | Improved transferability of self-supervised learning models through batch normalization finetuning | - |
| SwAV (ResNet-50) | 83.21 | Improved transferability of self-supervised learning models through batch normalization finetuning | - |
| Supervised (ViT-S/16) | 81.68 | Benchmarking Self-Supervised Learning on Diverse Pathology Datasets | |
| MoCo-v2 (ResNet-50) | 85.88 | Benchmarking Self-Supervised Learning on Diverse Pathology Datasets | |
| Barlow Rwins (ResNet-50) | 81.27 | Benchmarking Self-Supervised Learning on Diverse Pathology Datasets | |
| Supervised (ResNet-50) | 78.92 | Benchmarking Self-Supervised Learning on Diverse Pathology Datasets | |
| DINO (ViT-S/16) | 79.43 | Benchmarking Self-Supervised Learning on Diverse Pathology Datasets | |
| Barlow Twins (ResNet-50) | 84.03 | Improved transferability of self-supervised learning models through batch normalization finetuning | - |
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