Image Classification On Omnibenchmark
Metriken
Average Top-1 Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
Modellname | Average Top-1 Accuracy |
---|---|
bamboo-building-mega-scale-vision-dataset | 45.4 |
parameter-efficient-transfer-learning-for-nlp | 44.5 |
beit-bert-pre-training-of-image-transformers | 30.1 |
billion-scale-semi-supervised-learning-for | 40.4 |
inception-v4-inception-resnet-and-the-impact | 32.3 |
re-labeling-imagenet-from-single-to-multi | 30.8 |
neural-prompt-search | 47.6 |
masked-autoencoders-are-scalable-vision | 30.6 |
manifold-mixup-better-representations-by | 31.6 |
mlp-mixer-an-all-mlp-architecture-for-vision | 32.2 |
deep-residual-learning-for-image-recognition | 37.4 |
deep-residual-learning-for-image-recognition | 34.3 |
momentum-contrast-for-unsupervised-visual | 34.8 |
swin-transformer-hierarchical-vision | 46.4 |
mopro-webly-supervised-learning-with-momentum | 36.1 |
efficientnet-rethinking-model-scaling-for | 35.8 |
learning-transferable-visual-models-from | 42.1 |
emerging-properties-in-self-supervised-vision | 38.9 |
large-scale-learning-of-general-visual | 40.4 |
unsupervised-learning-of-visual-features-by | 38.3 |
cutmix-regularization-strategy-to-train | 31.1 |
meal-v2-boosting-vanilla-resnet-50-to-80-top | 36.6 |