65 lines
5.8 KiB
Markdown
65 lines
5.8 KiB
Markdown
# HRNet系列
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## 概述
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HRNet是2019年由微软亚洲研究院提出的一种全新的神经网络，不同于以往的卷积神经网络，该网络在网络深层仍然可以保持高分辨率，因此预测的关键点热图更准确，在空间上也更精确。此外，该网络在对分辨率敏感的其他视觉任务中，如检测、分割等，表现尤为优异。
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该系列模型的FLOPS、参数量以及T4 GPU上的预测耗时如下图所示。
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目前PaddleClas开源的这类模型的预训练模型一共有7个，其指标如图所示，其中HRNet_W48_C指标精度异常的原因可能是因为网络训练的正常波动。
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## 精度、FLOPS和参数量
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| Models | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPS<br>(G) | Parameters<br>(M) |
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|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
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| HRNet_W18_C | 0.769 | 0.934 | 0.768 | 0.934 | 4.140 | 21.290 |
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| HRNet_W18_C_ssld | 0.816 | 0.958 | 0.768 | 0.934 | 4.140 | 21.290 |
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| HRNet_W30_C | 0.780 | 0.940 | 0.782 | 0.942 | 16.230 | 37.710 |
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| HRNet_W32_C | 0.783 | 0.942 | 0.785 | 0.942 | 17.860 | 41.230 |
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| HRNet_W40_C | 0.788 | 0.945 | 0.789 | 0.945 | 25.410 | 57.550 |
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| HRNet_W44_C | 0.790 | 0.945 | 0.789 | 0.944 | 29.790 | 67.060 |
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| HRNet_W48_C | 0.790 | 0.944 | 0.793 | 0.945 | 34.580 | 77.470 |
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| HRNet_W48_C_ssld | 0.836 | 0.968 | 0.793 | 0.945 | 34.580 | 77.470 |
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| HRNet_W64_C | 0.793 | 0.946 | 0.795 | 0.946 | 57.830 | 128.060 |
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| SE_HRNet_W64_C_ssld | 0.847 | 0.973 | | | 57.830 | 128.970 |
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## 基于V100 GPU的预测速度
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| Models | Crop Size | Resize Short Size | FP32<br>Batch Size=1<br>(ms) |
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|-------------|-----------|-------------------|--------------------------|
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| HRNet_W18_C | 224 | 256 | 7.368 |
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| HRNet_W18_C_ssld | 224 | 256 | 7.368 |
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| HRNet_W30_C | 224 | 256 | 9.402 |
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| HRNet_W32_C | 224 | 256 | 9.467 |
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| HRNet_W40_C | 224 | 256 | 10.739 |
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| HRNet_W44_C | 224 | 256 | 11.497 |
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| HRNet_W48_C | 224 | 256 | 12.165 |
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| HRNet_W48_C_ssld | 224 | 256 | 12.165 |
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| HRNet_W64_C | 224 | 256 | 15.003 |
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## 基于T4 GPU的预测速度
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| Models | Crop Size | Resize Short Size | FP16<br>Batch Size=1<br>(ms) | FP16<br>Batch Size=4<br>(ms) | FP16<br>Batch Size=8<br>(ms) | FP32<br>Batch Size=1<br>(ms) | FP32<br>Batch Size=4<br>(ms) | FP32<br>Batch Size=8<br>(ms) |
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|-------------|-----------|-------------------|------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
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| HRNet_W18_C | 224 | 256 | 6.79093 | 11.50986 | 17.67244 | 7.40636 | 13.29752 | 23.33445 |
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| HRNet_W18_C_ssld | 224 | 256 | 6.79093 | 11.50986 | 17.67244 | 7.40636 | 13.29752 | 23.33445 |
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| HRNet_W30_C | 224 | 256 | 8.98077 | 14.08082 | 21.23527 | 9.57594 | 17.35485 | 32.6933 |
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| HRNet_W32_C | 224 | 256 | 8.82415 | 14.21462 | 21.19804 | 9.49807 | 17.72921 | 32.96305 |
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| HRNet_W40_C | 224 | 256 | 11.4229 | 19.1595 | 30.47984 | 12.12202 | 25.68184 | 48.90623 |
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| HRNet_W44_C | 224 | 256 | 12.25778 | 22.75456 | 32.61275 | 13.19858 | 32.25202 | 59.09871 |
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| HRNet_W48_C | 224 | 256 | 12.65015 | 23.12886 | 33.37859 | 13.70761 | 34.43572 | 63.01219 |
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| HRNet_W48_C_ssld | 224 | 256 | 12.65015 | 23.12886 | 33.37859 | 13.70761 | 34.43572 | 63.01219 |
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| HRNet_W64_C | 224 | 256 | 15.10428 | 27.68901 | 40.4198 | 17.57527 | 47.9533 | 97.11228 |
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| SE_HRNet_W64_C_ssld | 224 | 256 | 32.33651 | 69.31189 | 116.07245 | 31.69770 | 94.99546 | 174.45766 |
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