| linear-probe-image-classification-on-sun397 | FLAVA | #1 | Accuracy: 82.17 |
| linear-probe-image-classification-on-caltech-101 | FLAVA | #3 | Accuracy: 95.74 |
| linear-probe-image-classification-on-dtd | FLAVA | #3 | Accuracy: 77.29 |
| image-retrieval-on-coco | FLAVA (zero-shot) | #4 | recall@1: 38.38recall@5: 67.47 |
| image-retrieval-on-coco | CLIP (zero-shot) | #5 | recall@1: 33.29recall@5: 62.47 |
| linear-probe-image-classification-on-fgvc-aircraft | FLAVA | #4 | Accuracy: 47.31 |
| linear-probe-image-classification-on-food-101 | FLAVA | #5 | Accuracy: 88.51 |
| image-to-text-retrieval-on-coco | FLAVA (ViT-B, zero-shot) | #6 | Recall@1: 42.74Recall@5: 76.76 |
| semantic-textual-similarity-on-sts-b-glue-dev | FLAVA | #10 | Pearson/Spearman Correlation: 85.7 |
| paraphrase-identification-on-mrpc-glue-dev | FLAVA | #11 | F1: 86.9 |
| linguistic-acceptability-on-cola-glue-dev | FLAVA | #13 | Matthews Correlation: 50.7 |
| natural-language-inference-on-rte-glue-dev | FLAVA | #14 | Accuracy: 57.8 |
| natural-language-inference-on-qnli-glue-dev | FLAVA | #17 | Accuracy: 87.3 |
| visual-question-answering-on-vqa-v2-test-dev | FLAVA | #21 | Accuracy: 72.8 |
| image-classification-on-flowers-102 | FLAVA | #48 | Accuracy: 96.37 |
| sentiment-analysis-on-sst-2-binary-classification | FLAVA | #85 | Dev Accuracy: 90.9 |
| image-classification-on-cifar-100 | FLAVA | #175 | Percentage correct: 77.68 |
| image-classification-on-cifar-10 | FLAVA | #210 | Percentage correct: 92.87 |
| image-classification-on-imagenet | FLAVA | #985 | Top 1 Accuracy: 75.54% |