Image Classification on CIFAR100 (Baseline Accuracy)
94.1AccuracyFranca
Evaluation Results
| Method | Links | |
|---|---|---|
| FrancaViT=L/14, Pre-training Data=IN-21k, Evaluation Protocol=Linear2026.05 | 94.1 | |
| DINOv2ViT=L/14, Pre-training Data=IN-21k, Evaluation Protocol=Linear2026.05 | 93.9 | |
| LoRA (r = 32)Backbone=DINOv2-ViT-B/14, Params (M)=4.722026.04 | 93.7 | |
| RandLoRA (r = 10)Backbone=DINOv2-ViT-B/14, Params (M)=3.592026.04 | 93.7 | |
| BaLoRABackbone=ViT-L/16, Pre-trained=ImageNet12K2026.04 | 93.62 | |
| Full Fine-TuningBackbone=DINOv2-ViT-B/14, Params (M)=86.732026.04 | 93.6 | |
| LoRABackbone=ViT-L/16, Pre-trained=ImageNet12K2026.04 | 93.59 | |
| FFBackbone=ViT-Large, Method=FF, # Params=303.3M2026.05 | 93.58 | |
| DoRABackbone=ViT-L/16, Pre-trained=ImageNet12K2026.04 | 93.44 | |
| DreamNet-3MAE-lType=DreamNet, Input Size=224^2, #Params=209.90, FLOPs=149.42024.09 | 93.4 | |
| DINOv2ViT=L/14, Pre-training Data=LVD-142M, Evaluation Protocol=Linear, Notes=distilled from DINOv2-G2026.05 | 93.4 | |
| FourierFTBackbone=ViT-Large, Method=FourierFT, # Params=480K2026.05 | 93.37 | |
| FourierFTBackbone=ViT-Large, Method=FourierFT, # Params=144K2026.05 | 93.31 | |
| Full Fine-tuningBackbone=ViT-L/16, Pre-trained=ImageNet12K2026.04 | 93.25 | |
| GiVA (r = 32, TopSV-B)Backbone=DINOv2-ViT-B/14, Params (M)=0.082026.04 | 93.1 | |
| LoRA (r = 32)Backbone=CLIP-ViT-L/14, Params (M)=12.582026.04 | 93.1 | |
| Uni-LoRABackbone=ViT-Large, Method=Uni-LoRA, # Params=144K2026.05 | 93.08 | |
| VeRA (r = 256)Backbone=DINOv2-ViT-B/14, Params (M)=0.092026.04 | 93 | |
| GiVA (r = 32, SecSV-B)Backbone=DINOv2-ViT-B/14, Params (M)=0.082026.04 | 93 | |
| GiVA (r = 32, Rand-B)Backbone=DINOv2-ViT-B/14, Params (M)=0.082026.04 | 93 | |
| RandLoRA (r = 10)Backbone=CLIP-ViT-L/14, Params (M)=12.782026.04 | 93 | |
| Full Fine-TuningBackbone=CLIP-ViT-L/14, Params (M)=303.282026.04 | 92.8 | |
| iBOTViT=L/16, Pre-training Data=IN-21k, Evaluation Protocol=Linear2026.05 | 92.8 | |
| GPartBackbone=ViT-Large, Method=GPart (Ours), # Params=144K2026.05 | 92.59 | |
| VeRA (r = 256)Backbone=CLIP-ViT-L/14, Params (M)=0.232026.04 | 92.4 | |
| GiVA (r = 32, TopSV-B)Backbone=CLIP-ViT-L/14, Params (M)=0.202026.04 | 92.4 | |
| GiVA (r = 32, Rand-B)Backbone=CLIP-ViT-L/14, Params (M)=0.202026.04 | 92.4 | |
| FFBackbone=ViT-Base, Method=FF, # Params=85.8M2026.05 | 92.38 | |
| SleepNet-3ViT-lType=SleepNet, Input Size=224^2, #Params=13.40, FLOPs=371.52024.09 | 92.3 | |
| DreamNet-3Type=DreamNet, Input Size=224^2, #Params=209.12, FLOPs=83.52024.09 | 92.3 | |
| DreamNet-3MAE-bType=DreamNet, Input Size=224^2, #Params=209.12, FLOPs=83.52024.09 | 92.3 | |
| GiVA (r = 32, SecSV-B)Backbone=CLIP-ViT-L/14, Params (M)=0.202026.04 | 92.3 | |
| SleepNet-3Type=SleepNet, Input Size=224^2, #Params=10.07, FLOPs=60.62024.09 | 92.2 | |
| SleepNet-3ViT-bType=SleepNet, Input Size=224^2, #Params=10.07, FLOPs=107.32024.09 | 92.2 | |
| GPartBackbone=ViT-Base, Method=GPart (Ours), # Params=72K2026.05 | 92.11 | |
| EfficientNetV2-LType=Conv only, Input Size=480^2, #Params=121.32, FLOPs=53.02024.09 | 92.1 | |
| CvT-W24Type=Conv + Trans, Input Size=384^2, #Params=277.33, FLOPs=193.22024.09 | 92.1 | |
| Uni-LoRABackbone=ViT-Base, Method=Uni-LoRA, # Params=72K2026.05 | 92.1 | |
| TC-JEPAViT=L/16, Pre-training Data=IN-21k, Evaluation Protocol=Linear2026.05 | 91.6 | |
| FourierFTBackbone=ViT-Base, Method=FourierFT, # Params=239K2026.05 | 91.45 | |
| MAElargeType=Transformer, Input Size=224^2, #Params=304.18, FLOPs=61.92024.09 | 91.3 | |
| ViTl-ACNType=Augmentation +Trans, Input Size=384^2, #Params=490.11, FLOPs=41.92024.09 | 91.2 | |
| TC-JEPAViT=L/16, Pre-training Data=CC27M, Evaluation Protocol=Linear2026.05 | 91.2 | |
| SigLIP2ViT=L/16, Pre-training Data=WebLI, Evaluation Protocol=Linear2026.05 | 91.2 | |
| FourierFTBackbone=ViT-Base, Method=FourierFT, # Params=72K2026.05 | 91.2 | |
| MAEbaseType=Transformer, Input Size=224^2, #Params=86.43, FLOPs=17.62024.09 | 91.1 | |
| SleepNet-4Type=SleepNet, Input Size=224^2, #Params=10.14, FLOPs=80.72024.09 | 91.1 | |
| ViTlargeType=Transformer, Input Size=384^2, #Params=307.12, FLOPs=190.72024.09 | 91 | |
| SleepNet-3MAE-lType=SleepNet, Input Size=224^2, #Params=10.09, FLOPs=126.52024.09 | 91 | |
| Web-DINOViT=L/16, Pre-training Data=MC-2B, Evaluation Protocol=Linear2026.05 | 90.7 | |
| SleepNet-3MAE-bType=SleepNet, Input Size=224^2, #Params=10.07, FLOPs=60.62024.09 | 90.2 | |
| EfficientNet-B7Type=Conv only, Input Size=600^2, #Params=66.44, FLOPs=37.02024.09 | 90.1 | |
| CvT-21Type=Conv + Trans, Input Size=384^2, #Params=32.41, FLOPs=24.92024.09 | 90.1 | |
| MAEl-MASType=Augmentation +Trans, Input Size=224^2, #Params=551.42, FLOPs=29.92024.09 | 90 | |
| SigLIPViT=L/16, Pre-training Data=WebLI, Evaluation Protocol=Linear2026.05 | 89.8 | |
| CAPIViT=L/14, Pre-training Data=IN-21k, Evaluation Protocol=Linear2026.05 | 89.2 | |
| I-JEPAViT=L/16, Pre-training Data=IN-21k, Evaluation Protocol=Linear2026.05 | 88.7 | |
| ViTbaseType=Transformer, Input Size=224^2, #Params=86.45, FLOPs=55.42024.09 | 87.3 | |
| ResNet50Type=Conv only, Input Size=224^2, #Params=25.12, FLOPs=3.82024.09 | 86.9 | |
| DreamNet-2Type=DreamNet, Input Size=224^2, #Params=208.31, FLOPs=52.32024.09 | 85.1 | |
| DreamNet-4Type=DreamNet, Input Size=224^2, #Params=212.03, FLOPs=118.42024.09 | 85.1 | |
| LPBackbone=ViT-Base, Method=LP, # Params=02026.05 | 84.28 | |
| LPBackbone=ViT-Large, Method=LP, # Params=02026.05 | 84.28 | |
| ResNet101Type=Conv only, Input Size=224^2, #Params=45.01, FLOPs=7.62024.09 | 84.1 | |
| SAFformerParam=6.342026.05 | 83.38 | |
| SleepNet-2Type=SleepNet, Input Size=224^2, #Params=10.01, FLOPs=40.52024.09 | 83.1 | |
| Max-FormerParam=6.602026.05 | 82.65 | |
| QB-LIFType=SNN Training, Architecture=ResNet-20, Timestep=42026.04 | 82.25 | |
| STAA-SNN2026.05 | 82.05 | |
| MSViTParam=7.592026.05 | 81.98 | |
| QB-LIFType=SNN Training, Architecture=ResNet-20, Timestep=22026.04 | 81.72 | |
| QB-LIFType=SNN Training, Architecture=ResNet-20, Timestep=12026.04 | 81.36 | |
| Spikinghash-MParam (M)=6.24, Time Step=42025.01 | 81.28 | |
| QKFormerParam (M)=6.74, Time Step=42025.01 | 81.15 | |
| QKFormerParam=6.742026.05 | 81.15 | |
| QKFormerParam (M)=6.74, Time Step (T)=42024.03 | 81.15 | |
| Trasnformer (ANN)Param (M)=9.32, Time Step (T)=12024.03 | 81.02 | |
| FACTSampling Ratio=10%, Backbone=ViT2026.06 | 80.66 | |
| ResNet18Type=Conv only, Input Size=224^2, #Params=11.23, FLOPs=1.82024.09 | 80.6 | |
| Burst+LIPoolingType=ANN2SNN, Architecture=ResNet-20, Timestep=2562026.04 | 80.57 | |
| FSTA-SNNParam=11.302026.05 | 80.42 | |
| Spikingformer-CMLParam (M)=9.32, Time Step=42025.01 | 80.37 | |
| AXISArchitecture=ViT-B-322025.08 | 80.13 | |
| CMLParam (M)=9.32, Time Step (T)=42024.03 | 80.02 | |
| Spikinghash-SParam (M)=3.87, Time Step=42025.01 | 79.51 | |
| KvLIFNetwork Architecture=ResNet-18, Timestep=62026.03 | 79.3 | |
| SpikingResformer-TiParam (M)=10.79, Time Step=42025.01 | 79.28 | |
| aTLASArchitecture=ViT-B-322025.08 | 79.14 | |
| SpikingformerParam (M)=9.32, Time Step=42025.01 | 79.09 | |
| L3FACTSampling Ratio=10%, Backbone=ViT2026.06 | 78.78 | |
| RecDis-SNNType=SNN Training, Architecture=ResNet-19, Timestep=42026.04 | 78.53 | |
| ILIFNetwork Architecture=ResNet-18, Timestep=62026.03 | 78.51 | |
| DSRNetwork Architecture=PreAct-ResNet-18, Timestep=202026.03 | 78.5 | |
| BLS-SeTaBackbone=ResNet18, Pruning Rate=30%2026.04 | 78.5 | |
| SeTaBackbone=ResNet18, Pruning Rate=30%2026.04 | 78.4 | |
| BLS-InfoBatchBackbone=ResNet18, Pruning Rate=30%2026.04 | 78.4 | |
| Spike-driven TransformerParam (M)=9.32, Time Step=42025.01 | 78.4 | |
| S-TransformerParam=10.282026.05 | 78.4 | |
| S-TransformerParam (M)=10.28, Time Step (T)=42024.03 | 78.4 | |
| CLIFNetwork Architecture=ResNet-18, Timestep=62026.03 | 78.36 |