Image Classification On Inaturalist
Métriques
Top 1 Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Top 1 Accuracy |
|---|---|
| metasaug-meta-semantic-augmentation-for-long | 63.28% |
| multimodal-autoregressive-pre-training-of | 79.7 |
| spinenet-learning-scale-permuted-backbone-for | 63.6% |
| deep-cnns-meet-global-covariance-pooling | - |
| masked-autoencoders-are-scalable-vision | 83.4 |
| deit-lt-distillation-strikes-back-for-vision | - |
| multimodal-autoregressive-pre-training-of | 77.9 |
| hiera-a-hierarchical-vision-transformer | 83.8 |
| fixing-the-train-test-resolution-discrepancy | 75.4 |
| the-inaturalist-species-classification-and | 67.3% |
| metaformer-a-unified-meta-framework-for-fine | 83.4% |
| metaformer-a-unified-meta-framework-for-fine | 80.4% |
| transfg-a-transformer-architecture-for-fine | 71.7 |
| graph-rise-graph-regularized-image-semantic | 31.12% |
| on-the-eigenvalues-of-global-covariance | 72.3 |
| multimodal-autoregressive-pre-training-of | 81.5 |
| multimodal-autoregressive-pre-training-of | 76 |
| multimodal-autoregressive-pre-training-of | 85.9 |