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
细粒度图像分类
Fine Grained Image Classification On Birdsnap
Fine Grained Image Classification On Birdsnap
评估指标
Accuracy
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
Accuracy
Paper Title
EffNet-L2 (SAM)
90.07%
Sharpness-Aware Minimization for Efficiently Improving Generalization
FixSENet-154
84.3%
Fixing the train-test resolution discrepancy
EfficientNet-B7
84.3%
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
GPIPE
83.6%
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
NNCLR
61.4%
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations
0 of 5 row(s) selected.
Previous
Next
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
细粒度图像分类
Fine Grained Image Classification On Birdsnap
Fine Grained Image Classification On Birdsnap
评估指标
Accuracy
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
Accuracy
Paper Title
EffNet-L2 (SAM)
90.07%
Sharpness-Aware Minimization for Efficiently Improving Generalization
FixSENet-154
84.3%
Fixing the train-test resolution discrepancy
EfficientNet-B7
84.3%
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
GPIPE
83.6%
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
NNCLR
61.4%
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations
0 of 5 row(s) selected.
Previous
Next
Fine Grained Image Classification On Birdsnap | SOTA | HyperAI超神经