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Quantization test result

Currently mmdeploy support ncnn quantization

Quantize with ncnn

mmcls

model	dataset	fp32 top-1 (%)	int8 top-1 (%)
ResNet-18	Cifar10	94.82	94.83
ResNeXt-32x4d-50	ImageNet-1k	77.90	78.20*
MobileNet V2	ImageNet-1k	71.86	71.43*
HRNet-W18*	ImageNet-1k	76.75	76.25*

Note:

Because of the large amount of imagenet-1k data and ncnn has not released Vulkan int8 version, only part of the test set (4000/50000) is used.
The accuracy will vary after quantization, and it is normal for the classification model to increase by less than 1%.

OCR detection

model	dataset	fp32 hmean	int8 hmean
PANet	ICDAR2015	0.795	0.792 @thr=0.9
TextSnake	CTW1500	0.817	0.818

Note: mmocr Uses 'shapely' to compute IoU, which results in a slight difference in accuracy

Pose detection

model	dataset	fp32 AP	int8 AP
Hourglass	COCO2017	0.717	0.713
S-ViPNAS-MobileNetV3	COCO2017	0.687	0.683
S-ViPNAS-Res50	COCO2017	0.701	0.696
S-ViPNAS-MobileNetV3	COCO Wholebody	0.459	0.445
S-ViPNAS-Res50	COCO Wholebody	0.484	0.476
S-ViPNAS-MobileNetV3_dark	COCO Wholebody	0.499	0.481
S-ViPNAS-Res50_dark	COCO Wholebody	0.520	0.511

Note: MMPose models are tested with flip_test explicitly set to False in model configs.

Super Resolution

model	dataset	fp32 PSNR/SSIM	int8 PSNR/SSIM
EDSRx2	Set5	35.7733/0.9365	35.4266/0.9334
EDSRx4	Set5	30.2194/0.8498	29.9340/0.8409

mmseg

model	dataset	fp32 mIoU	int8 mIoU
Fast-SCNN	cityscapes	70.96	70.24

Note:

Int8 models of the Fast-SCNN requires ncnnoptimize.
NCNN will extract 512 images from the train as a calibration dataset