Image Classification on ImageNet-A 1.0 (val)
74.5Top-1 AccFAN-L-Hybrid
Evaluation Results
| Method | Links | |
|---|---|---|
| FAN-L-HybridParams (M)=76.8, Pre-trained=ImageNet-22K, Resolution=384x3842022.04 | 74.5 | |
| ConvNeXt-XLData/Size=22K/384^2, FLOPs (G)=179, Params (M)=350.22022.01 | 69.3 | |
| ConvNeXt-LData/Size=22K/384^2, FLOPs (G)=101, Params (M)=197.82022.01 | 65.5 | |
| ConvNeXt-BData/Size=22K/384^2, FLOPs (G)=45.1, Params (M)=88.62022.01 | 62.3 | |
| ConvNeXt-BParams (M)=88.6, Pre-trained=ImageNet-22K, Resolution=384x3842022.04 | 62.3 | |
| FAN-L-HybridParams (M)=76.8, Pre-trained=ImageNet-22K, Resolution=224x2242022.04 | 60.7 | |
| MAE-VIT-LParams (M)=307, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 57.1 | |
| RVT-B*Pre-training=ImageNet-22K, Fine-tuning=ImageNet-1K2021.05 | 42.27 | |
| FAN-L-HybridParams (M)=76.8, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 41.8 | |
| RVT-BPre-training=ImageNet-22K, Fine-tuning=ImageNet-1K2021.05 | 41 | |
| FAN-B-HybridParams (M)=50.0, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 39.6 | |
| DeiT-BPre-training=ImageNet-22K, Fine-tuning=ImageNet-1K2021.05 | 38.01 | |
| FAN-L-VITParams (M)=80.5, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 37.2 | |
| ConvNeXt-BData/Size=1K/224^2, FLOPs (G)=15.4, Params (M)=88.62022.01 | 36.7 | |
| ConvNeXt-BParams (M)=88.6, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 36.7 | |
| MAE-VIT-BParams (M)=86, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 35.9 | |
| Swin-BData/Size=1K/224^2, FLOPs (G)=15.4, Params (M)=87.82022.01 | 35.8 | |
| Swin-SParams (M)=50, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 35.8 | |
| Swin-BParams (M)=87.8, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 35.8 | |
| FAN-B-VITParams (M)=54.0, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 35.4 | |
| FAN-S-HybridParams (M)=26.0, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 33.9 | |
| ConvNeXt-SParams (M)=50.2, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 31.2 | |
| FAN-S-ViTParams (M)=28.0, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 29.1 | |
| RVT-B*Data/Size=1K/224^2, FLOPs (G)=17.7, Params (M)=91.82022.01 | 28.5 | |
| RVT-B*Params (M)=91.8, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 28.5 | |
| XCiT-S24Params (M)=47.7, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 27.8 | |
| RVT-S*Data/Size=1K/224^2, FLOPs (G)=4.7, Params (M)=23.32022.01 | 25.7 | |
| RVT-S*Params (M)=23.3, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 25.7 | |
| XCIT-S12Params (M)=26.3, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 25 | |
| ConvNeXt-TData/Size=1K/224^2, FLOPs (G)=4.5, Params (M)=28.62022.01 | 24.2 | |
| ConvNeXt-TParams (M)=28.6, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 24.2 | |
| Swin-TData/Size=1K/224^2, FLOPs (G)=4.5, Params (M)=28.32022.01 | 21.6 | |
| Swin-TParams (M)=28.3, Pre-trained=ImageNet-1K, Resolution=224x2242022.04 | 21.6 | |
| ResNet-50Data/Size=1K/224^2, FLOPs (G)=4.1, Params (M)=25.62022.01 | 0 |