diff --git a/docs/en/models/HRNet_en.md b/docs/en/models/HRNet_en.md index 1cc6e81b717c1b59ee4932deac1a02d6a2d386c7..b27d0f15be6041897893ae82374d9148d839d128 100644 --- a/docs/en/models/HRNet_en.md +++ b/docs/en/models/HRNet_en.md @@ -22,6 +22,7 @@ At present, there are 7 pretrained models of such models open-sourced by PaddleC | 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 | @@ -35,6 +36,7 @@ At present, there are 7 pretrained models of such models open-sourced by PaddleC | 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 | @@ -50,6 +52,7 @@ At present, there are 7 pretrained models of such models open-sourced by PaddleC | 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 | diff --git a/docs/en/models/Mobile_en.md b/docs/en/models/Mobile_en.md index c3ebd8f737a45b5ad5e647763f1b4b798bf45c8b..66b208541f24ed3797d3e550738ed26069ce049c 100644 --- a/docs/en/models/Mobile_en.md +++ b/docs/en/models/Mobile_en.md @@ -48,6 +48,7 @@ Currently there are 32 pretrained models of the mobile series open source by Pad | MobileNetV3_small_
x0_75 | 0.660 | 0.863 | 0.654 | | 0.088 | 2.370 | | MobileNetV3_small_
x0_5 | 0.592 | 0.815 | 0.580 | | 0.043 | 1.900 | | MobileNetV3_small_
x0_35 | 0.530 | 0.764 | 0.498 | | 0.026 | 1.660 | +| MobileNetV3_small_
x0_35_ssld | 0.556 | 0.777 | 0.498 | | 0.026 | 1.660 | | MobileNetV3_large_
x1_0_ssld | 0.790 | 0.945 | | | 0.450 | 5.470 | | MobileNetV3_large_
x1_0_ssld_int8 | 0.761 | | | | | | | MobileNetV3_small_
x1_0_ssld | 0.713 | 0.901 | | | 0.123 | 2.940 | @@ -89,6 +90,7 @@ Currently there are 32 pretrained models of the mobile series open source by Pad | MobileNetV3_small_x0_75 | 5.284 | 9.600 | | MobileNetV3_small_x0_5 | 3.352 | 7.800 | | MobileNetV3_small_x0_35 | 2.635 | 6.900 | +| MobileNetV3_small_x0_35_ssld | 2.635 | 6.900 | | MobileNetV3_large_x1_0_ssld | 19.308 | 21.000 | | MobileNetV3_large_x1_0_ssld_int8 | 14.395 | 10.000 | | MobileNetV3_small_x1_0_ssld | 6.546 | 12.000 | @@ -130,6 +132,7 @@ Currently there are 32 pretrained models of the mobile series open source by Pad | MobileNetV3_small_x0_75 | 1.80617 | 2.64646 | 3.24513 | 1.93697 | 2.64285 | 3.32797 | | MobileNetV3_small_x0_5 | 1.95001 | 2.74014 | 3.39485 | 1.88406 | 2.99601 | 3.3908 | | MobileNetV3_small_x0_35 | 2.10683 | 2.94267 | 3.44254 | 1.94427 | 2.94116 | 3.41082 | +| MobileNetV3_small_x0_35_ssld | 2.10683 | 2.94267 | 3.44254 | 1.94427 | 2.94116 | 3.41082 | | MobileNetV3_large_x1_0_ssld | 2.20149 | 3.08423 | 4.07779 | 2.04296 | 2.9322 | 4.53184 | | MobileNetV3_small_x1_0_ssld | 1.73933 | 2.59478 | 3.40276 | 1.74527 | 2.63565 | 3.28124 | | ShuffleNetV2 | 1.95064 | 2.15928 | 2.97169 | 1.89436 | 2.26339 | 3.17615 | diff --git a/docs/zh_CN/models/HRNet.md b/docs/zh_CN/models/HRNet.md index f694f7b0c1d6d6c9b195fa61aa0cc9544564859d..f8448226cc8f4576d4140d02b08979b603599f09 100644 --- a/docs/zh_CN/models/HRNet.md +++ b/docs/zh_CN/models/HRNet.md @@ -21,6 +21,7 @@ HRNet是2019年由微软亚洲研究院提出的一种全新的神经网络， | 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 | @@ -34,6 +35,7 @@ HRNet是2019年由微软亚洲研究院提出的一种全新的神经网络， | 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 | @@ -49,6 +51,7 @@ HRNet是2019年由微软亚洲研究院提出的一种全新的神经网络， | 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 | diff --git a/docs/zh_CN/models/Mobile.md b/docs/zh_CN/models/Mobile.md index 2c35c93e4bfcc29a8a6b59d6369b5835925efc43..24bdf2392f98c38c6259b19558033d638fcf752a 100644 --- a/docs/zh_CN/models/Mobile.md +++ b/docs/zh_CN/models/Mobile.md @@ -49,6 +49,7 @@ GhostNet是华为于2020年提出的一种全新的轻量化网络结构，通 | MobileNetV3_small_
x0_75 | 0.660 | 0.863 | 0.654 | | 0.088 | 2.370 | | MobileNetV3_small_
x0_5 | 0.592 | 0.815 | 0.580 | | 0.043 | 1.900 | | MobileNetV3_small_
x0_35 | 0.530 | 0.764 | 0.498 | | 0.026 | 1.660 | +| MobileNetV3_small_
x0_35_ssld | 0.556 | 0.777 | 0.498 | | 0.026 | 1.660 | | MobileNetV3_large_
x1_0_ssld | 0.790 | 0.945 | | | 0.450 | 5.470 | | MobileNetV3_large_
x1_0_ssld_int8 | 0.761 | | | | | | | MobileNetV3_small_
x1_0_ssld | 0.713 | 0.901 | | | 0.123 | 2.940 | @@ -90,6 +91,7 @@ GhostNet是华为于2020年提出的一种全新的轻量化网络结构，通 | MobileNetV3_small_x0_75 | 5.284 | 9.600 | | MobileNetV3_small_x0_5 | 3.352 | 7.800 | | MobileNetV3_small_x0_35 | 2.635 | 6.900 | +| MobileNetV3_small_x0_35_ssld | 2.635 | 6.900 | | MobileNetV3_large_x1_0_ssld | 19.308 | 21.000 | | MobileNetV3_large_x1_0_ssld_int8 | 14.395 | 10.000 | | MobileNetV3_small_x1_0_ssld | 6.546 | 12.000 | @@ -131,6 +133,7 @@ GhostNet是华为于2020年提出的一种全新的轻量化网络结构，通 | MobileNetV3_small_x0_75 | 1.80617 | 2.64646 | 3.24513 | 1.93697 | 2.64285 | 3.32797 | | MobileNetV3_small_x0_5 | 1.95001 | 2.74014 | 3.39485 | 1.88406 | 2.99601 | 3.3908 | | MobileNetV3_small_x0_35 | 2.10683 | 2.94267 | 3.44254 | 1.94427 | 2.94116 | 3.41082 | +| MobileNetV3_small_x0_35_ssld | 2.10683 | 2.94267 | 3.44254 | 1.94427 | 2.94116 | 3.41082 | | MobileNetV3_large_x1_0_ssld | 2.20149 | 3.08423 | 4.07779 | 2.04296 | 2.9322 | 4.53184 | | MobileNetV3_small_x1_0_ssld | 1.73933 | 2.59478 | 3.40276 | 1.74527 | 2.63565 | 3.28124 | | ShuffleNetV2 | 1.95064 | 2.15928 | 2.97169 | 1.89436 | 2.26339 | 3.17615 |