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TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Fine-Grained Image Classification	Birdsnap	EffNet-L2 (SAM)	Accuracy	90.07%	# 1
Image Classification	CIFAR-10	PyramidNet (SAM)	Percentage correct	98.6	# 12
Image Classification	CIFAR-10	PyramidNet (SAM)	Percentage error	1.4	# 9
Image Classification	CIFAR-10	EffNet-L2 (SAM)	Percentage correct	99.70	# 1
Image Classification	CIFAR-10	EffNet-L2 (SAM)	Percentage error	0.30	# 1
Image Classification	CIFAR-100	EffNet-L2 (SAM)	Percentage correct	96.08	# 1
Image Classification	CIFAR-100	EffNet-L2 (SAM)	Percentage error	3.92	# 1
Image Classification	CIFAR-100	PyramidNet (SAM)	Percentage correct	89.7	# 7
Image Classification	Fashion-MNIST	Shake-Shake (SAM)	Accuracy	96.41	# 2
Fine-Grained Image Classification	FGVC Aircraft	EffNet-L2 (SAM)	Top-1 Error Rate	4.82	# 1
Image Classification	Flowers-102	EffNet-L2 (SAM)	Accuracy	99.65%	# 1
Fine-Grained Image Classification	Food-101	EffNet-L2 (SAM)	Accuracy	96.18	# 1
Image Classification	ImageNet	ResNet-152 (SAM)	Top 1 Accuracy	81.6%	# 51
Image Classification	ImageNet	ResNet-152 (SAM)	Top 5 Accuracy	95.65%	# 34
Image Classification	ImageNet	EfficientNet-L2-475 (SAM)	Top 1 Accuracy	88.61%	# 3
Image Classification	ImageNet	EfficientNet-L2-475 (SAM)	Number of params	480M	# 4
Fine-Grained Image Classification	Oxford-IIIT Pets	EffNet-L2 (SAM)	Top-1 Error Rate	2.90%	# 1
Fine-Grained Image Classification	Oxford-IIIT Pets	EffNet-L2 (SAM)	Accuracy	97.10%	# 3
Fine-Grained Image Classification	Stanford Cars	EffNet-L2 (SAM)	Accuracy	95.96%	# 3
Image Classification	SVHN	WRN28-10 (SAM)	Percentage error	0.99	# 1

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