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

[Docs] add the support information about MMPose Hourglass (#1118 )

* [Docs] add the support information about MMPose Hourglass

* * add Hourglass support config

* update benchmark doc

* modify Hourglass AP under pytorch backend

* add regression test for Hourglass

* *update 03-benchmark/benchmark.md

* * modify mmpose.yml to ignore ncnn int8 test

* add trt_fp16 test results

* * add openvino test result

* * modify supported_models.md

2022-10-10 16:38:54 +08:00

2.9 KiB

Raw Blame History

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.

2.9 KiB Raw Blame History