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HRNet series

Overview

HRNet is a brand new neural network proposed by Microsoft research Asia in 2019. Different from the previous convolutional neural network, this network can still maintain high resolution in the deep layer of the network, so the heat map of the key points predicted is more accurate, and it is also more accurate in space. In addition, the network performs particularly well in other visual tasks sensitive to resolution, such as detection and segmentation.

The FLOPS, parameters, and inference time on the T4 GPU of this series of models are shown in the figure below.

At present, there are 7 pretrained models of such models open-sourced by PaddleClas, and their indicators are shown in the figure. Among them, the reason why the accuracy of the HRNet_W48_C indicator is abnormal may be due to fluctuations in training.

Accuracy, FLOPS and Parameters

Models Top1 Top5 Reference
top1		 Reference
top5		 FLOPS
(G)		 Parameters
(M)
HRNet_W18_C 0.769 0.934 0.768 0.934 4.140 21.290
HRNet_W18_C_ssld 0.816 0.958 0.768 0.934 4.140 21.290
HRNet_W30_C 0.780 0.940 0.782 0.942 16.230 37.710
HRNet_W32_C 0.783 0.942 0.785 0.942 17.860 41.230
HRNet_W40_C 0.788 0.945 0.789 0.945 25.410 57.550
HRNet_W44_C 0.790 0.945 0.789 0.944 29.790 67.060
HRNet_W48_C 0.790 0.944 0.793 0.945 34.580 77.470
HRNet_W48_C_ssld 0.836 0.968 0.793 0.945 34.580 77.470
HRNet_W64_C 0.793 0.946 0.795 0.946 57.830 128.060
SE_HRNet_W64_C_ssld 0.847 0.973 57.830 128.970

Inference speed based on V100 GPU

Models Crop Size Resize Short Size FP32
Batch Size=1
(ms)
HRNet_W18_C 224 256 7.368
HRNet_W18_C_ssld 224 256 7.368
HRNet_W30_C 224 256 9.402
HRNet_W32_C 224 256 9.467
HRNet_W40_C 224 256 10.739
HRNet_W44_C 224 256 11.497
HRNet_W48_C 224 256 12.165
HRNet_W48_C_ssld 224 256 12.165
HRNet_W64_C 224 256 15.003

Inference speed based on T4 GPU

Models Crop Size Resize Short Size FP16
Batch Size=1
(ms)		 FP16
Batch Size=4
(ms)		 FP16
Batch Size=8
(ms)		 FP32
Batch Size=1
(ms)		 FP32
Batch Size=4
(ms)		 FP32
Batch Size=8
(ms)
HRNet_W18_C 224 256 6.79093 11.50986 17.67244 7.40636 13.29752 23.33445
HRNet_W18_C_ssld 224 256 6.79093 11.50986 17.67244 7.40636 13.29752 23.33445
HRNet_W30_C 224 256 8.98077 14.08082 21.23527 9.57594 17.35485 32.6933
HRNet_W32_C 224 256 8.82415 14.21462 21.19804 9.49807 17.72921 32.96305
HRNet_W40_C 224 256 11.4229 19.1595 30.47984 12.12202 25.68184 48.90623
HRNet_W44_C 224 256 12.25778 22.75456 32.61275 13.19858 32.25202 59.09871
HRNet_W48_C 224 256 12.65015 23.12886 33.37859 13.70761 34.43572 63.01219
HRNet_W48_C_ssld 224 256 12.65015 23.12886 33.37859 13.70761 34.43572 63.01219
HRNet_W64_C 224 256 15.10428 27.68901 40.4198 17.57527 47.9533 97.11228
SE_HRNet_W64_C_ssld 224 256 32.33651 69.31189 116.07245 31.69770 94.99546 174.45766