HyperAI超神経
ホームニュース最新論文チュートリアルデータセット百科事典SOTALLMモデルGPU ランキング学会
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HyperAI超神経

Fine Grained Image Classification On Oxford

評価指標

Accuracy
FLOPS
PARAMS

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

モデル名
Accuracy
FLOPS
PARAMS
Paper TitleRepository
ResNet50 (A1)97.9%4.124MResNet 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-20198.29--A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification-
PC Bilinear CNN93.65%--Pairwise Confusion for Fine-Grained Visual Classification
ResMLP-1297.4%--ResMLP: Feedforward networks for image classification with data-efficient training
NAT-M398.1250M3.7MNeural Architecture Transfer
µ2Net (ViT-L/16)99.61%--An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems
TNT-B99.0%-65.6MTransformer in Transformer
DenseNet-201(Spinal FC)98.36--A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification-
NAT-M1-152M3.3MNeural Architecture Transfer
BiT-M (ResNet)99.30%--Big Transfer (BiT): General Visual Representation Learning
ResMLP-2497.9%--ResMLP: Feedforward networks for image classification with data-efficient training
CCT-14/7x2-15G22.5MEscaping the Big Data Paradigm with Compact Transformers
AutoAugment95.36%--AutoAugment: Learning Augmentation Policies from Data
IELT99.64%--Fine-Grained Visual Classification via Internal Ensemble Learning Transformer
NAT-M297.9195M3.4MNeural Architecture Transfer
FixInceptionResNet-V295.7%--Fixing the train-test resolution discrepancy
Assemble-ResNet98.9%--Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network
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