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SOTA
Domain Generalization
Domain Generalization On Vizwiz
Domain Generalization On Vizwiz
评估指标
Accuracy - All Images
Accuracy - Clean Images
Accuracy - Corrupted Images
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 - All Images
Accuracy - Clean Images
Accuracy - Corrupted Images
Paper Title
Repository
EfficientNet-B4
41.7
46.4
35.6
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
ResNet-18 (lpf3)
35.6
39.5
28.5
Making Convolutional Networks Shift-Invariant Again
EfficientNet-B4 (advprop+autoaug)
48.1
51.4
42.5
Adversarial Examples Improve Image Recognition
ResNet-101 (lpf5)
41
45.8
34.8
Making Convolutional Networks Shift-Invariant Again
ResNet-50 (lpf5)
41.5
45.3
35.2
Making Convolutional Networks Shift-Invariant Again
ResNet-101
46.3
50.1
40.5
Deep Residual Learning for Image Recognition
ResNeXt-101 32x16d
51.7
54.8
48.1
Aggregated Residual Transformations for Deep Neural Networks
ResNet-50 (SIN)
25.3
30
20.4
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
ResNet-50 (augmix)
42.2
46.4
35.9
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
EfficientNet-B2
38.1
42.8
31.4
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
VGG-16
34.7
39.5
28.5
Very Deep Convolutional Networks for Large-Scale Image Recognition
ResNet-34 (lpf2)
38.3
42.8
32.4
Making Convolutional Networks Shift-Invariant Again
DenseNet121 (lpf5)
38.7
42.7
32
Making Convolutional Networks Shift-Invariant Again
ResNet-50 (IN-C_brightness)
38.8
43.5
32.5
Measuring Robustness to Natural Distribution Shifts in Image Classification
EfficientNet-B5 (advprop+autoaug)
49.1
51.7
44
Adversarial Examples Improve Image Recognition
ResNet-50 (IN-C_motion_blur)
35.7
39.6
30.2
Measuring Robustness to Natural Distribution Shifts in Image Classification
EfficientNet-B6 (advprop+autoaug)
49.6
53.2
44.7
Adversarial Examples Improve Image Recognition
VGG-11 BN
32.9
37.1
25.8
Very Deep Convolutional Networks for Large-Scale Image Recognition
EfficientNet-B4 (autoaug)
44.3
48.6
38.2
AutoAugment: Learning Augmentation Strategies From Data
VOLO-D5
57.2
59.7
51.8
VOLO: Vision Outlooker for Visual Recognition
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