| image-classification-on-stanford-cars | ResMLP-24 | #14 | Accuracy: 89.5 |
| image-classification-on-stanford-cars | ResMLP-12 | #19 | Accuracy: 84.6 |
| fine-grained-image-classification-on-oxford-102-flowers | ResMLP-24 | #15 | Accuracy: 97.9% |
| fine-grained-image-classification-on-oxford-102-flowers | ResMLP-12 | #18 | Accuracy: 97.4% |
| image-classification-on-inaturalist-2019 | ResMLP-24 | #16 | Top-1 Accuracy: 72.5 |
| image-classification-on-inaturalist-2019 | ResMLP-12 | #18 | Top-1 Accuracy: 71.0 |
| machine-translation-on-wmt2014-english-french | ResMLP-12 | #29 | BLEU score: 40.6 |
| machine-translation-on-wmt2014-english-french | ResMLP-6 | #33 | BLEU score: 40.3 |
| image-classification-on-imagenet-v2 | ResMLP-B24/8 22k | #31 | Top 1 Accuracy: 74.2 |
| image-classification-on-imagenet-v2 | ResMLP-B24/8 | #34 | Top 1 Accuracy: 73.4 |
| image-classification-on-imagenet-v2 | ResMLP-S24/16 | #40 | Top 1 Accuracy: 69.8 |
| image-classification-on-imagenet-v2 | ResMLP-S12/16 | #51 | Top 1 Accuracy: 66.0 |
| image-classification-on-flowers-102 | ResMLP24 | #37 | Accuracy: 97.9 |
| image-classification-on-flowers-102 | ResMLP12 | #44 | Accuracy: 97.4 |
| image-classification-on-inaturalist-2018 | ResMLP-24 | #44 | Top-1 Accuracy: 64.3 |
| image-classification-on-inaturalist-2018 | ResMLP-12 | #48 | Top-1 Accuracy: 60.2 |
| image-classification-on-cifar-100 | ResMLP-24 | #47 | Percentage correct: 89.5 |
| image-classification-on-cifar-100 | ResMLP-12 | #71 | Percentage correct: 87.0 |
| image-classification-on-imagenet-real | ResMLP-36 | #47 | Accuracy: 85.6%Params: 45M |
| image-classification-on-imagenet-real | ResMLP-24 | #48 | Accuracy: 85.3%Params: 30M |
| image-classification-on-imagenet-real | ResMLP-12 | #50 | Accuracy: 84.6%Params: 15M |
| image-classification-on-imagenet-real | ResMLP-B24/8 (22k) | #62 | Top 1 Accuracy: 84.4% |
| machine-translation-on-wmt2014-english-german | ResMLP-12 | #48 | BLEU score: 26.8 |
| machine-translation-on-wmt2014-english-german | ResMLP-6 | #52 | BLEU score: 26.4 |
| fine-grained-image-classification-on-stanford-cars | ResMLP-24 | #63 | Accuracy: 89.5% |
| fine-grained-image-classification-on-stanford-cars | ResMLP-12 | #65 | Accuracy: 84.6% |
| self-supervised-image-classification-on-imagenet | DINO (ResMLP-24) | #86 | Top 1 Accuracy: 72.8%Number of Params: 30M |
| self-supervised-image-classification-on-imagenet | DINO (ResMLP-12) | #102 | Top 1 Accuracy: 67.5%Number of Params: 15M |
| image-classification-on-imagenet | ResMLP-B24/8 | #441 | Top 1 Accuracy: 83.6%Number of params: 116M |
| image-classification-on-imagenet | ResMLP-S24 | #722 | Top 1 Accuracy: 80.8%Number of params: 30MGFLOPs: 6 |
| image-classification-on-imagenet | ResMLP-36 | #781 | Top 1 Accuracy: 79.7%Number of params: 45M |
| image-classification-on-imagenet | ResMLP-24 | #797 | Top 1 Accuracy: 79.4% |
| image-classification-on-imagenet | ResMLP-12 (distilled, class-MLP) | #859 | Top 1 Accuracy: 78.6%Number of params: 17.7MGFLOPs: 3 |
| image-classification-on-imagenet | ResMLP-S12 | #901 | Top 1 Accuracy: 77.8%Number of params: 15.4M |