Image Classification On Inaturalist 2018

Metrics

Top-1 Accuracy

Results

Performance results of various models on this benchmark

Model Name	Top-1 Accuracy	Paper Title	Repository
µ2Net+ (ViT-L/16)	80.97	A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems	-
ResNet-50	49.7%	ClusterFit: Improving Generalization of Visual Representations	-
Barlow Twins (ResNet-50)	46.5	Barlow Twins: Self-Supervised Learning via Redundancy Reduction	-
LeViT-384	66.9%	LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference	-
ResNet-50	69.8%	Grafit: Learning fine-grained image representations with coarse labels	-
BS-CMO (ResNet-50)	74.0%	The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification	-
CaiT-M-36 U 224	78%	-	-
ResNeXt-101 (SAMix)	70.54%	Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup	-
GPaCo (ResNet-152)	78.1%	Generalized Parametric Contrastive Learning	-
RIDE (ResNet-50)	72.2%	Long-tailed Recognition by Routing Diverse Distribution-Aware Experts	-
ResNet-152	69.05%	Class-Balanced Loss Based on Effective Number of Samples	-
CeiT-T (384 finetune resolution)	72.2%	Incorporating Convolution Designs into Visual Transformers	-
CeiT-S (384 finetune resolution)	79.4%	Incorporating Convolution Designs into Visual Transformers	-
LeViT-128S	55.2%	LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference	-
RegNet-8GF	81.2%	Grafit: Learning fine-grained image representations with coarse labels	-
ResNet-50 (AutoMix)	64.73%	AutoMix: Unveiling the Power of Mixup for Stronger Classifiers	-
Hiera-H (448px)	87.3%	Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles	-
SWAG (ViT H/14)	86.0%	Revisiting Weakly Supervised Pre-Training of Visual Perception Models	-
LeViT-256	66.2%	LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference	-
LeViT-192	60.4%	LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference	-

0 of 60 row(s) selected.