Quantization On Imagenet

Metrics

Top-1 Accuracy (%)

Results

Performance results of various models on this benchmark

		Paper Title
FQ-ViT (ViT-L)	85.03	FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer
FQ-ViT (ViT-B)	83.31	FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer
FQ-ViT (Swin-B)	82.97	FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer
FQ-ViT (Swin-S)	82.71	FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer
FQ-ViT (DeiT-B)	81.20	FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer
FQ-ViT (Swin-T)	80.51	FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer
FQ-ViT (DeiT-S)	79.17	FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer
Xception W8A8	78.972	HPTQ: Hardware-Friendly Post Training Quantization
ADLIK-MO-ResNet50-W4A4	77.878	Learned Step Size Quantization
ADLIK-MO-ResNet50-W3A4	77.34	Learned Step Size Quantization
EfficientNet-B0 ReLU W8A8	77.092	HPTQ: Hardware-Friendly Post Training Quantization
ResNet50-W4A4 (paper)	76.7	Learned Step Size Quantization
EfficientNet-B0-W8A8	76.4	HMQ: Hardware Friendly Mixed Precision Quantization Block for CNNs
EfficientNet-B0-W4A4	76	HMQ: Hardware Friendly Mixed Precision Quantization Block for CNNs
ResNet50-W3A4	75.45	HMQ: Hardware Friendly Mixed Precision Quantization Block for CNNs
EfficientNet-B0 W8A8	74.216	HPTQ: Hardware-Friendly Post Training Quantization
MPT (80) +BN	74.03	Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network
EfficientNet-W4A4	73.8	LSQ+: Improving low-bit quantization through learnable offsets and better initialization
DenseNet-121 W8A8	73.356	HPTQ: Hardware-Friendly Post Training Quantization
MixNet-W4A4	71.7	LSQ+: Improving low-bit quantization through learnable offsets and better initialization

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Quantization On Imagenet

Metrics

Results