Image Classification On Inaturalist 2019
Metriken
Top-1 Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Top-1 Accuracy |
|---|---|
| levit-a-vision-transformer-in-convnet-s | 70.8 |
| incorporating-convolution-designs-into-visual | 78.9 |
| incorporating-convolution-designs-into-visual | 82.7 |
| resnet-strikes-back-an-improved-training | 75.0 |
| densenets-reloaded-paradigm-shift-beyond | 81.2 |
| conviformers-convolutionally-guided-vision | 82.85 |
| mixmim-mixed-and-masked-image-modeling-for | 83.9 |
| masked-autoencoders-are-scalable-vision | 88.3 |
| densenets-reloaded-paradigm-shift-beyond | 82.9 |
| hiera-a-hierarchical-vision-transformer | 88.5 |
| densenets-reloaded-paradigm-shift-beyond | 83.7 |
| levit-a-vision-transformer-in-convnet-s | 72.3 |
| levit-a-vision-transformer-in-convnet-s | 68.4 |
| incorporating-convolution-designs-into-visual | 72.8 |
| resmlp-feedforward-networks-for-image | 71.0 |
| incorporating-convolution-designs-into-visual | 77.9 |
| densenets-reloaded-paradigm-shift-beyond | 83.5 |
| levit-a-vision-transformer-in-convnet-s | 74.3 |
| levit-a-vision-transformer-in-convnet-s | 66.5 |
| going-deeper-with-image-transformers | 81.8 |
| grafit-learning-fine-grained-image | 84.1 |
| resmlp-feedforward-networks-for-image | 72.5 |