Image Classification on WikiArt
79.48Top-1 AccuracyIndividual
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| IndividualBackbone=DeiT-S2022.03 | 79.48 | — | — | — | — | |
| IndividualBackbone=T2T-ViT-122022.03 | 73.51 | — | — | — | — | |
| MEATBackbone=DeiT-S2022.03 | 73.43 | — | — | — | — | |
| IndividualBackbone=DeiT-Ti2022.03 | 72.13 | — | — | — | — | |
| Adaptor-BBackbone=DeiT-S2022.03 | 71.33 | — | — | — | — | |
| HATBackbone=DeiT-S2022.03 | 70.43 | — | — | — | — | |
| PiggybackBackbone=DeiT-S2022.03 | 68.09 | — | — | — | — | |
| LwFBackbone=DeiT-S2022.03 | 65.64 | — | — | — | — | |
| MEATBackbone=DeiT-Ti2022.03 | 64.63 | — | — | — | — | |
| PiggybackBackbone=DeiT-Ti2022.03 | 62.42 | — | — | — | — | |
| HATBackbone=DeiT-Ti2022.03 | 61.84 | — | — | — | — | |
| MEATBackbone=T2T-ViT-122022.03 | 61.2 | — | — | — | — | |
| PiggybackBackbone=T2T-ViT-122022.03 | 60.34 | — | — | — | — | |
| Adaptor-BBackbone=T2T-ViT-122022.03 | 59.01 | — | — | — | — | |
| HATBackbone=T2T-ViT-122022.03 | 58.53 | — | — | — | — | |
| Adaptor-BBackbone=DeiT-Ti2022.03 | 57.04 | — | — | — | — | |
| GPT-4o2025.10 | 56.3 | — | — | — | — | |
| Offline ICDBackbone=LLaVA-OneVision2025.10 | 53.3 | — | — | — | 100 | |
| LwFBackbone=T2T-ViT-122022.03 | 51.24 | — | — | — | — | |
| ICDBackbone=LLaVA-OneVision2025.10 | 49 | — | — | — | 20.4 | |
| LwFBackbone=DeiT-Ti2022.03 | 46.88 | — | — | — | — | |
| Offline ICDBackbone=InternVL2.52025.10 | 44.1 | — | — | — | 100 | |
| ClassifierBackbone=DeiT-S2022.03 | 43.85 | — | — | — | — | |
| BestofNBackbone=LLaVA-OneVision2025.10 | 43.6 | — | — | — | — | |
| 0-shotBackbone=LLaVA-OneVision2025.10 | 43.1 | — | — | — | — | |
| ICDBackbone=InternVL2.52025.10 | 41.3 | — | — | — | 11.1 | |
| Self-labelingBackbone=LLaVA-OneVision2025.10 | 40.4 | — | — | — | — | |
| ClassifierBackbone=DeiT-Ti2022.03 | 38.64 | — | — | — | — | |
| CoTBackbone=LLaVA-OneVision2025.10 | 36.5 | — | — | — | — | |
| CoTBackbone=InternVL2.52025.10 | 35.9 | — | — | — | — | |
| ClassifierBackbone=T2T-ViT-122022.03 | 35.57 | — | — | — | — | |
| BestofNBackbone=InternVL2.52025.10 | 34.4 | — | — | — | — | |
| 0-shotBackbone=InternVL2.52025.10 | 34.1 | — | — | — | — | |
| Self-labelingBackbone=InternVL2.52025.10 | 32.5 | — | — | — | — | |
| Classifier OnlyBackbone=VGG-16, # Models=1, Size=537 MB2018.01 | — | 49.53 | — | — | — | |
| Classifier OnlyBackbone=VGG-16 BN2018.01 | — | 48.05 | — | — | — | |
| Classifier OnlyBackbone=ResNet-502018.01 | — | 44.4 | — | — | — | |
| Classifier OnlyBackbone=DenseNet-1212018.01 | — | 45.08 | — | — | — | |
| Individual NetworksBackbone=VGG-16, # Models=6, Size=3,222 MB2018.01 | — | 29.84 | — | — | — | |
| Individual NetworksBackbone=VGG-16 BN2018.01 | — | 26.68 | — | — | — | |
| Individual NetworksBackbone=ResNet-502018.01 | — | 24.4 | — | — | — | |
| Individual NetworksBackbone=DenseNet-1212018.01 | — | 23.59 | — | — | — | |
| PackNetBackbone=VGG-16, # Models=1, Size=587 MB, Task addition order=CUBS -> Sketch2018.01 | — | 32.8 | — | — | — | |
| PackNetBackbone=VGG-16, # Models=1, Size=587 MB, Task addition order=Sketch -> CUBS2018.01 | — | 31.48 | — | — | — | |
| PackNetBackbone=VGG-16 BN, Task addition order=↓2018.01 | — | 30.21 | — | — | — | |
| PackNetBackbone=VGG-16 BN, Task addition order=↑2018.01 | — | 29.59 | — | — | — | |
| PackNetBackbone=ResNet-50, Task addition order=↓2018.01 | — | 30.6 | — | — | — | |
| PackNetBackbone=ResNet-50, Task addition order=↑2018.01 | — | 29.69 | — | — | — | |
| PackNetBackbone=DenseNet-121, Task addition order=↓2018.01 | — | 33.66 | — | — | — | |
| PackNetBackbone=DenseNet-121, Task addition order=↑2018.01 | — | 30.81 | — | — | — | |
| PiggybackBackbone=VGG-16, # Models=1, Size=621 MB2018.01 | — | 29.91 | — | — | — | |
| PiggybackBackbone=VGG-16 BN2018.01 | — | 27.5 | — | — | — | |
| PiggybackBackbone=ResNet-502018.01 | — | 28.67 | — | — | — | |
| PiggybackBackbone=DenseNet-1212018.01 | — | 29.56 | — | — | — |