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Model Compression On Imagenet

Metrics

Top-1

Results

Performance results of various models on this benchmark

		Paper Title	Repository
ADLIK-MO-ResNet50+W4A4	77.878	Learned Step Size Quantization
ADLIK-MO-ResNet50+W3A4	77.34	Learned Step Size Quantization
ResNet-18 + 4bit-1dim model compression using DKM	70.52	R2 Loss: Range Restriction Loss for Model Compression and Quantization	-
MobileNet-v1 + 4bit-1dim model compression using DKM	69.63	R2 Loss: Range Restriction Loss for Model Compression and Quantization	-
ResNet-18 + 2bit-1dim model compression using DKM	68.63	R2 Loss: Range Restriction Loss for Model Compression and Quantization	-
MobileNet-v1 + 2bit-1dim model compression using DKM	67.62	R2 Loss: Range Restriction Loss for Model Compression and Quantization	-
ResNet-18 + 4bit-4dim model compression using DKM	66.1	R2 Loss: Range Restriction Loss for Model Compression and Quantization	-
ResNet-18 + 2bit-2dim model compression using DKM	64.7	R2 Loss: Range Restriction Loss for Model Compression and Quantization	-
MobileNet-v1 + 4bit-4dim model compression using DKM	61.4	R2 Loss: Range Restriction Loss for Model Compression and Quantization	-
ResNet-18 + 1bit-1dim model compression using DKM	59.7	R2 Loss: Range Restriction Loss for Model Compression and Quantization	-
MobileNet-v1 + 2bit-2dim model compression using DKM	53.99	R2 Loss: Range Restriction Loss for Model Compression and Quantization	-
MobileNet-v1 + 1bit-1dim model compression using DKM	52.58	R2 Loss: Range Restriction Loss for Model Compression and Quantization	-

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Model Compression On Imagenet | SOTA | HyperAI