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算力平台
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SOTA
图像分类
Image Classification On Cifar 100
Image Classification On Cifar 100
评估指标
Percentage correct
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Percentage correct
Paper Title
EffNet-L2 (SAM)
96.08
Sharpness-Aware Minimization for Efficiently Improving Generalization
Swin-L + ML-Decoder
95.1
ML-Decoder: Scalable and Versatile Classification Head
µ2Net (ViT-L/16)
94.95
An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems
ViT-B-16 (ImageNet-21K-P pretrain)
94.2
ImageNet-21K Pretraining for the Masses
CvT-W24
94.09
CvT: Introducing Convolutions to Vision Transformers
ViT-B/16 (PUGD)
93.95
Perturbated Gradients Updating within Unit Space for Deep Learning
Heinsen Routing + BEiT-large 16 224
93.8
An Algorithm for Routing Vectors in Sequences
BiT-L (ResNet)
93.51
Big Transfer (BiT): General Visual Representation Learning
Astroformer
93.36
Astroformer: More Data Might not be all you need for Classification
VIT-L/16 (Spinal FC, Background)
93.31
Reduction of Class Activation Uncertainty with Background Information
CaiT-M-36 U 224
93.1
-
ViT-L (attn fine-tune)
93.0
Three things everyone should know about Vision Transformers
TResNet-L-V2
92.6
TResNet: High Performance GPU-Dedicated Architecture
EfficientNetV2-L
92.3
EfficientNetV2: Smaller Models and Faster Training
EfficientNetV2-M
92.2
EfficientNetV2: Smaller Models and Faster Training
BiT-M (ResNet)
92.17
Big Transfer (BiT): General Visual Representation Learning
CeiT-S (384 finetune resolution)
91.8
Incorporating Convolution Designs into Visual Transformers
CeiT-S
91.8
Incorporating Convolution Designs into Visual Transformers
EfficientNet-B7
91.7
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNetV2-S
91.5
EfficientNetV2: Smaller Models and Faster Training
0 of 210 row(s) selected.
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HyperAI
HyperAI超神经
首页
算力平台
文档
资讯
论文
教程
数据集
百科
SOTA
LLM 模型天梯
GPU 天梯
顶会
开源项目
全站搜索
关于
服务条款
隐私政策
中文
HyperAI
HyperAI超神经
Toggle Sidebar
全站搜索…
⌘
K
Command Palette
Search for a command to run...
算力平台
首页
SOTA
图像分类
Image Classification On Cifar 100
Image Classification On Cifar 100
评估指标
Percentage correct
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Percentage correct
Paper Title
EffNet-L2 (SAM)
96.08
Sharpness-Aware Minimization for Efficiently Improving Generalization
Swin-L + ML-Decoder
95.1
ML-Decoder: Scalable and Versatile Classification Head
µ2Net (ViT-L/16)
94.95
An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems
ViT-B-16 (ImageNet-21K-P pretrain)
94.2
ImageNet-21K Pretraining for the Masses
CvT-W24
94.09
CvT: Introducing Convolutions to Vision Transformers
ViT-B/16 (PUGD)
93.95
Perturbated Gradients Updating within Unit Space for Deep Learning
Heinsen Routing + BEiT-large 16 224
93.8
An Algorithm for Routing Vectors in Sequences
BiT-L (ResNet)
93.51
Big Transfer (BiT): General Visual Representation Learning
Astroformer
93.36
Astroformer: More Data Might not be all you need for Classification
VIT-L/16 (Spinal FC, Background)
93.31
Reduction of Class Activation Uncertainty with Background Information
CaiT-M-36 U 224
93.1
-
ViT-L (attn fine-tune)
93.0
Three things everyone should know about Vision Transformers
TResNet-L-V2
92.6
TResNet: High Performance GPU-Dedicated Architecture
EfficientNetV2-L
92.3
EfficientNetV2: Smaller Models and Faster Training
EfficientNetV2-M
92.2
EfficientNetV2: Smaller Models and Faster Training
BiT-M (ResNet)
92.17
Big Transfer (BiT): General Visual Representation Learning
CeiT-S (384 finetune resolution)
91.8
Incorporating Convolution Designs into Visual Transformers
CeiT-S
91.8
Incorporating Convolution Designs into Visual Transformers
EfficientNet-B7
91.7
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNetV2-S
91.5
EfficientNetV2: Smaller Models and Faster Training
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Image Classification On Cifar 100 | SOTA | HyperAI超神经