Fine Grained Image Classification On Oxford

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Accuracy

FLOPS

PARAMS

평가 결과

이 벤치마크에서 각 모델의 성능 결과

				Paper Title
IELT	99.64%	-	-	Fine-Grained Visual Classification via Internal Ensemble Learning Transformer
BiT-L (ResNet)	99.63%	-	-	Big Transfer (BiT): General Visual Representation Learning
µ2Net (ViT-L/16)	99.61%	-	-	An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems
Wide-ResNet-101 (Spinal FC)	99.30%	-	-	SpinalNet: Deep Neural Network with Gradual Input
BiT-M (ResNet)	99.30%	-	-	Big Transfer (BiT): General Visual Representation Learning
Grafit (RegNet-8GF)	99.1%	-	-	Grafit: Learning fine-grained image representations with coarse labels
TResNet-L	99.1%	-	-	TResNet: High Performance GPU-Dedicated Architecture
TNT-B	99.0%	-	65.6M	Transformer in Transformer
Assemble-ResNet	98.9%	-	-	Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network
DeiT-B	98.8%	-	86M	Training data-efficient image transformers & distillation through attention
DenseNet-201(Spinal FC)	98.36	-	-	A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification
NAT-M4	98.3	400M	4.2M	Neural Architecture Transfer
DenseNet-201	98.29	-	-	A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification
NAT-M3	98.1	250M	3.7M	Neural Architecture Transfer
ResNet50 (A1)	97.9%	4.1	24M	ResNet strikes back: An improved training procedure in timm
ResMLP-24	97.9%	-	-	ResMLP: Feedforward networks for image classification with data-efficient training
NAT-M2	97.9	195M	3.4M	Neural Architecture Transfer
SR-GNN	97.9%	9.8	30.9	SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization
ResMLP-12	97.4%	-	-	ResMLP: Feedforward networks for image classification with data-efficient training
FixInceptionResNet-V2	95.7%	-	-	Fixing the train-test resolution discrepancy

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Fine Grained Image Classification On Oxford

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