Image Classification on OrganSMNIST v2 (test)
85.28AccuracyOmniRad base
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| OmniRad baseSize=base2026.02 | 85.28 | 80.97 | 98.33 | |
| OmniRad smallSize=small2026.02 | 84.42 | 79.91 | 98.15 | |
| DINOv3 smallSize=small2026.02 | 83.66 | 78.92 | 98.16 | |
| Radio DINO baseSize=base2026.02 | 82.81 | 78.15 | 98.28 | |
| DINOv3 baseSize=base2026.02 | 82.65 | 78.44 | 98 | |
| DINO baseSize=base2026.02 | 82.44 | 77.59 | 97.21 | |
| Radio DINO smallSize=small2026.02 | 82.3 | 77.73 | 98.27 | |
| AutoKerasReference=[36]2026.02 | 81.3 | — | 97.4 | |
| Resnet18Reference=[33]2026.02 | 81.3 | — | 97.4 | |
| AutoKeras2026.02 | 81.3 | — | 97.4 | |
| Med ViT baseSize=base, Reference=[20]2026.02 | 80.6 | — | 97.3 | |
| Med ViT smallSize=small, Reference=[20]2026.02 | 80.5 | — | 98.7 | |
| DINO smallSize=small2026.02 | 80.11 | 74.66 | 97.97 | |
| Med ViT tinySize=tiny, Reference=[20]2026.02 | 78.9 | — | 97.2 | |
| Resnet50Reference=[36]2026.02 | 78.5 | — | 97.5 | |
| ResNet-50Input Resolution=2242026.02 | 78.5 | — | 97.5 | |
| ResNet-18Input Resolution=282026.02 | 78.2 | — | 97.2 | |
| DINOv2 smallSize=small2026.02 | 77.87 | 71.82 | 97.62 | |
| Resnet18Reference=[36]2026.02 | 77.8 | — | 97.4 | |
| ResNet-18Input Resolution=2242026.02 | 77.8 | — | 97.4 | |
| ResNet-50Input Resolution=282026.02 | 77 | — | 97.2 | |
| Google AutoML VisionReference=[36]2026.02 | 74.9 | — | 96.4 | |
| Google AutoML Vision2026.02 | 74.9 | — | 96.4 | |
| DINOv2 baseSize=base2026.02 | 73.97 | 66.7 | 97.01 | |
| auto-sklearnReference=[36]2026.02 | 67.2 | — | 94.5 | |
| auto-sklearn2026.02 | 67.2 | — | 94.5 | |
| G-LoG bi-filtration (MLP)Sigma (σ)=02026.02 | 60.5 | — | 92.7 | |
| G-LoG bi-filtration (MLP)Sigma (σ)=0.52026.02 | 58.8 | — | 92.9 | |
| G-LoG bi-filtration (MLP)Sigma (σ)=12026.02 | 57.3 | — | 91.8 | |
| G-LoG bi-filtration (MLP)Sigma (σ)=1.52026.02 | 56.5 | — | 91 | |
| Topo-Med (MLP)2026.02 | 53.2 | — | 91 |