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Image Classification On Inaturalist

Metrics

Top 1 Accuracy

Results

Performance results of various models on this benchmark

Model Name
Top 1 Accuracy
Paper TitleRepository
MetaSAug63.28%MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition
AIMv2-1B79.7Multimodal Autoregressive Pre-training of Large Vision Encoders
SpineNet-14363.6%SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
iSQRT-COV-Net-Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
MAE (ViT-H, 448)83.4Masked Autoencoders Are Scalable Vision Learners
b_22DeiT-LT(ours)-DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets
AIMv2-H77.9Multimodal Autoregressive Pre-training of Large Vision Encoders
Hiera-H (448px)83.8Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
FixSENet-15475.4Fixing the train-test resolution discrepancy
IncResNetV2 SE67.3%The iNaturalist Species Classification and Detection Dataset
MetaFormer (MetaFormer-2,384,extra_info)83.4%MetaFormer: A Unified Meta Framework for Fine-Grained Recognition
MetaFormer (MetaFormer-2,384)80.4%MetaFormer: A Unified Meta Framework for Fine-Grained Recognition
TransFG71.7TransFG: A Transformer Architecture for Fine-grained Recognition
Graph-RISE (40M)31.12%Graph-RISE: Graph-Regularized Image Semantic Embedding
SEB+EfficientNet-B572.3On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition
AIMv2-3B81.5Multimodal Autoregressive Pre-training of Large Vision Encoders
AIMv2-L76Multimodal Autoregressive Pre-training of Large Vision Encoders
AIMv2-3B (448 res)85.9Multimodal Autoregressive Pre-training of Large Vision Encoders
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