Image Classification On Flowers 102
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Accuracy |
|---|---|
| when-vision-transformers-outperform-resnets | 87.9 |
| incorporating-convolution-designs-into-visual | 98.6 |
| neural-architecture-transfer | - |
| incorporating-convolution-designs-into-visual | 96.9 |
| when-vision-transformers-outperform-resnets | 91.1 |
| resmlp-feedforward-networks-for-image | 97.4 |
| reduction-of-class-activation-uncertainty | 99.75 |
| neural-architecture-transfer | 98.1% |
| with-a-little-help-from-my-friends-nearest | 95.1 |
| bamboo-building-mega-scale-vision-dataset | 99.7 |
| resmlp-feedforward-networks-for-image | 97.9 |
| incorporating-convolution-designs-into-visual | 97.8 |
| effect-of-large-scale-pre-training-on-full | 98.21 |
| escaping-the-big-data-paradigm-with-compact | 99.76 |
| going-deeper-with-image-transformers | 99.1 |
| domain-adaptive-transfer-learning-on-visual | 98.9% |
| vision-models-are-more-robust-and-fair-when | 96.3 |
| effect-of-large-scale-pre-training-on-full | 99.49 |
| incorporating-convolution-designs-into-visual | 98.2 |
| transboost-improving-the-best-imagenet | 97.85% |
| three-things-everyone-should-know-about | 98.5 |
| convmlp-hierarchical-convolutional-mlps-for | 99.5 |
| resnet-strikes-back-an-improved-training | 97.9 |
| grafit-learning-fine-grained-image | 99.1% |
| tresnet-high-performance-gpu-dedicated | 99.1% |
| when-vision-transformers-outperform-resnets | 91.5 |
| efficientnetv2-smaller-models-and-faster | 97.9 |
| levit-a-vision-transformer-in-convnet-s | 96.8 |
| levit-a-vision-transformer-in-convnet-s | 97.8 |
| levit-a-vision-transformer-in-convnet-s | 97.7 |
| when-vision-transformers-outperform-resnets | 91.8 |
| neural-architecture-transfer | 98.3% |
| global-filter-networks-for-image | 98.8 |
| efficientnet-rethinking-model-scaling-for | 98.8% |
| levit-a-vision-transformer-in-convnet-s | 98.3 |
| an-image-is-worth-16x16-words-transformers-1 | 99.68 |
| training-data-efficient-image-transformers | 98.8% |
| when-vision-transformers-outperform-resnets | 90 |
| classification-specific-parts-for-improving | 96.9% |
| efficientnetv2-smaller-models-and-faster | 98.5 |
| cvt-introducing-convolutions-to-vision | 99.72 |
| when-vision-transformers-outperform-resnets | 90 |
| sharpness-aware-minimization-for-efficiently-1 | 99.65% |
| neural-architecture-transfer | 97.9% |
| spinalnet-deep-neural-network-with-gradual-1 | 99.30 |
| scaling-up-visual-and-vision-language | 99.65% |
| large-scale-learning-of-general-visual | 99.63 |
| efficientnetv2-smaller-models-and-faster | 98.8 |
| convmlp-hierarchical-convolutional-mlps-for | 99.5 |
| large-scale-learning-of-general-visual | 99.30 |
| your-diffusion-model-is-secretly-a-zero-shot | - |