Fine Grained Image Classification On Oxford

Métriques

Accuracy

FLOPS

PARAMS

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	FLOPS	PARAMS	Paper Title	Repository
ResNet50 (A1)	97.9%	4.1	24M	ResNet strikes back: An improved training procedure in timm
Grafit (RegNet-8GF)	99.1%	-	-	Grafit: Learning fine-grained image representations with coarse labels	-
AutoFormer-S \| 384	-	-	-	AutoFormer: Searching Transformers for Visual Recognition
Wide-ResNet-101 (Spinal FC)	99.30%	-	-	SpinalNet: Deep Neural Network with Gradual Input
DenseNet-201	98.29	-	-	A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification	-
PC Bilinear CNN	93.65%	-	-	Pairwise Confusion for Fine-Grained Visual Classification
ResMLP-12	97.4%	-	-	ResMLP: Feedforward networks for image classification with data-efficient training
NAT-M3	98.1	250M	3.7M	Neural Architecture Transfer
µ2Net (ViT-L/16)	99.61%	-	-	An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems
TNT-B	99.0%	-	65.6M	Transformer in Transformer
DenseNet-201(Spinal FC)	98.36	-	-	A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification	-
NAT-M1	-	152M	3.3M	Neural Architecture Transfer
BiT-M (ResNet)	99.30%	-	-	Big Transfer (BiT): General Visual Representation Learning
ResMLP-24	97.9%	-	-	ResMLP: Feedforward networks for image classification with data-efficient training
CCT-14/7x2	-	15G	22.5M	Escaping the Big Data Paradigm with Compact Transformers
AutoAugment	95.36%	-	-	AutoAugment: Learning Augmentation Policies from Data
IELT	99.64%	-	-	Fine-Grained Visual Classification via Internal Ensemble Learning Transformer
NAT-M2	97.9	195M	3.4M	Neural Architecture Transfer
FixInceptionResNet-V2	95.7%	-	-	Fixing the train-test resolution discrepancy
Assemble-ResNet	98.9%	-	-	Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network

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