mirror of https://github.com/open-mmlab/mmdeploy.git synced 2025-01-14 08:09:43 +08:00

[Feature]add edsr result && super-resolution ncnn-int8 config (#1111 )

* add edsr result && ncnn-int8 config

* fix lint error

* fix lint error

* fix lint error && update benchmark.md

* add EDSRx2 pytorch result

* update edsrx4 result in benchmark

2022-10-18 10:04:56 +08:00

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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

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

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