未验证 提交 a73573a3 编写于 作者: C cuicheng01 提交者: GitHub

Add se_hrnet_w64_c_ssld_pretrained (#452)

add se hrnet64_c
上级 1ecf8334
...@@ -7,6 +7,7 @@ ...@@ -7,6 +7,7 @@
PaddleClas is a toolset for image classification tasks prepared for the industry and academia. It helps users train better computer vision models and apply them in real scenarios. PaddleClas is a toolset for image classification tasks prepared for the industry and academia. It helps users train better computer vision models and apply them in real scenarios.
**Recent update** **Recent update**
- 2020.12.06 Add `SE_HRNet_W64_C_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 84.75%.
- 2020.11.23 Add `GhostNet_x1_3_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 79.38%. - 2020.11.23 Add `GhostNet_x1_3_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 79.38%.
- 2020.11.09 Add `InceptionV3` architecture and pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 79.1%. - 2020.11.09 Add `InceptionV3` architecture and pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 79.1%.
- 2020.09.17 Add `Res2Net50_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.1%. Add `Res2Net101_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.9%. - 2020.09.17 Add `Res2Net50_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.1%. Add `Res2Net101_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.9%.
...@@ -256,6 +257,7 @@ Accuracy and inference time metrics of HRNet series models are shown as follows. ...@@ -256,6 +257,7 @@ Accuracy and inference time metrics of HRNet series models are shown as follows.
| HRNet_W48_C | 0.7895 | 0.9442 | 13.70761 | 34.43572 | 34.58 | 77.47 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams) | | HRNet_W48_C | 0.7895 | 0.9442 | 13.70761 | 34.43572 | 34.58 | 77.47 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams) |
| HRNet_W48_C_ssld | 0.8363 | 0.9682 | 13.70761 | 34.43572 | 34.58 | 77.47 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams) | | HRNet_W48_C_ssld | 0.8363 | 0.9682 | 13.70761 | 34.43572 | 34.58 | 77.47 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams) |
| HRNet_W64_C | 0.7930 | 0.9461 | 17.57527 | 47.9533 | 57.83 | 128.06 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams) | | HRNet_W64_C | 0.7930 | 0.9461 | 17.57527 | 47.9533 | 57.83 | 128.06 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams) |
| SE_HRNet_W64_C_ssld | 0.8475 |  0.9726 | 31.69770 | 94.99546 | 57.83 | 128.97 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_HRNet_W64_C_ssld_pretrained.pdparams) |
<a name="Inception_series"></a> <a name="Inception_series"></a>
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...@@ -8,6 +8,7 @@ ...@@ -8,6 +8,7 @@
**近期更新** **近期更新**
- 2020.12.06 添加`SE_HRNet_W64_C_ssld`模型,在ImageNet-1k上Top-1 Acc可达84.75%。
- 2020.11.23 添加`GhostNet_x1_3_ssld `模型,在ImageNet-1k上Top-1 Acc可达79.38%。 - 2020.11.23 添加`GhostNet_x1_3_ssld `模型,在ImageNet-1k上Top-1 Acc可达79.38%。
- 2020.11.18 添加图像分类[常见问题2020第一季第三期](./docs/zh_CN/faq_series/faq_2020_s1.md) 5个新问题,并且计划以后每周会更新,欢迎大家持续关注。 - 2020.11.18 添加图像分类[常见问题2020第一季第三期](./docs/zh_CN/faq_series/faq_2020_s1.md) 5个新问题,并且计划以后每周会更新,欢迎大家持续关注。
- 2020.11.09 添加`InceptionV3 `结构和模型,在ImageNet-1k上Top-1 Acc可达79.14%。 - 2020.11.09 添加`InceptionV3 `结构和模型,在ImageNet-1k上Top-1 Acc可达79.14%。
...@@ -260,6 +261,7 @@ HRNet系列模型的精度、速度指标如下表所示,更多关于该系列 ...@@ -260,6 +261,7 @@ HRNet系列模型的精度、速度指标如下表所示,更多关于该系列
| HRNet_W48_C | 0.7895 | 0.9442 | 13.70761 | 34.43572 | 34.58 | 77.47 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams) | | HRNet_W48_C | 0.7895 | 0.9442 | 13.70761 | 34.43572 | 34.58 | 77.47 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams) |
| HRNet_W48_C_ssld | 0.8363 | 0.9682 | 13.70761 | 34.43572 | 34.58 | 77.47 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams) | | HRNet_W48_C_ssld | 0.8363 | 0.9682 | 13.70761 | 34.43572 | 34.58 | 77.47 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams) |
| HRNet_W64_C | 0.7930 | 0.9461 | 17.57527 | 47.9533 | 57.83 | 128.06 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams) | | HRNet_W64_C | 0.7930 | 0.9461 | 17.57527 | 47.9533 | 57.83 | 128.06 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams) |
| SE_HRNet_W64_C_ssld | 0.8475 | 0.9726 | 31.69770 | 94.99546 | 57.83 | 128.97 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_HRNet_W64_C_ssld_pretrained.pdparams) |
<a name="Inception系列"></a> <a name="Inception系列"></a>
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...@@ -30,6 +30,7 @@ At present, there are 7 pretrained models of such models open-sourced by PaddleC ...@@ -30,6 +30,7 @@ At present, there are 7 pretrained models of such models open-sourced by PaddleC
| HRNet_W48_C | 0.790 | 0.944 | 0.793 | 0.945 | 34.580 | 77.470 | | 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_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 | | 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 ## Inference speed based on V100 GPU
...@@ -62,3 +63,5 @@ At present, there are 7 pretrained models of such models open-sourced by PaddleC ...@@ -62,3 +63,5 @@ At present, there are 7 pretrained models of such models open-sourced by PaddleC
| HRNet_W48_C | 224 | 256 | 12.65015 | 23.12886 | 33.37859 | 13.70761 | 34.43572 | 63.01219 | | 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_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 | | 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 |
...@@ -144,3 +144,6 @@ Currently there are 32 pretrained models of the mobile series open source by Pad ...@@ -144,3 +144,6 @@ Currently there are 32 pretrained models of the mobile series open source by Pad
| ShuffleNetV2_x1_5 | 1.51054 | 2.4565 | 3.41738 | 1.45389 | 2.5203 | 3.99872 | | ShuffleNetV2_x1_5 | 1.51054 | 2.4565 | 3.41738 | 1.45389 | 2.5203 | 3.99872 |
| ShuffleNetV2_x2_0 | 1.95616 | 2.44751 | 4.19173 | 2.15654 | 3.18247 | 5.46893 | | ShuffleNetV2_x2_0 | 1.95616 | 2.44751 | 4.19173 | 2.15654 | 3.18247 | 5.46893 |
| ShuffleNetV2_swish | 2.50213 | 2.92881 | 3.474 | 2.5129 | 2.97422 | 3.69357 | | ShuffleNetV2_swish | 2.50213 | 2.92881 | 3.474 | 2.5129 | 2.97422 | 3.69357 |
| GhostNet_x0_5 | 2.64492 | 3.48473 | 4.48844 | 2.36115 | 3.52802 | 3.89444 |
| GhostNet_x1_0 | 2.63120 | 3.92065 | 4.48296 | 2.57042 | 3.56296 | 4.85524 |
| GhostNet_x1_3 | 2.89715 | 3.80329 | 4.81661 | 2.81810 | 3.72071 | 5.92269 |
...@@ -163,6 +163,7 @@ python tools/infer/predict.py \ ...@@ -163,6 +163,7 @@ python tools/infer/predict.py \
- [HRNet_W48_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams) - [HRNet_W48_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams)
- [HRNet_W48_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_ssld_pretrained.pdparams) - [HRNet_W48_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_ssld_pretrained.pdparams)
- [HRNet_W64_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams) - [HRNet_W64_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams)
- [SE_HRNet_W64_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_HRNet_W64_C_ssld_pretrained.pdparams)
- DPN and DenseNet series - DPN and DenseNet series
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# Release Notes # Release Notes
- 2020.12.06
* Add `SE_HRNet_W64_C_ssld` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 84.75%.
- 2020.11.23 - 2020.11.23
* Add `GhostNet_x1_3_ssld` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 79.38%. * Add `GhostNet_x1_3_ssld` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 79.38%.
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...@@ -29,6 +29,7 @@ HRNet是2019年由微软亚洲研究院提出的一种全新的神经网络, ...@@ -29,6 +29,7 @@ HRNet是2019年由微软亚洲研究院提出的一种全新的神经网络,
| HRNet_W48_C | 0.790 | 0.944 | 0.793 | 0.945 | 34.580 | 77.470 | | 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_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 | | 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 |
## 基于V100 GPU的预测速度 ## 基于V100 GPU的预测速度
...@@ -47,7 +48,6 @@ HRNet是2019年由微软亚洲研究院提出的一种全新的神经网络, ...@@ -47,7 +48,6 @@ HRNet是2019年由微软亚洲研究院提出的一种全新的神经网络,
## 基于T4 GPU的预测速度 ## 基于T4 GPU的预测速度
| 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) | | 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) |
...@@ -61,3 +61,4 @@ HRNet是2019年由微软亚洲研究院提出的一种全新的神经网络, ...@@ -61,3 +61,4 @@ HRNet是2019年由微软亚洲研究院提出的一种全新的神经网络,
| HRNet_W48_C | 224 | 256 | 12.65015 | 23.12886 | 33.37859 | 13.70761 | 34.43572 | 63.01219 | | 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_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 | | 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 |
...@@ -145,3 +145,6 @@ GhostNet是华为于2020年提出的一种全新的轻量化网络结构,通 ...@@ -145,3 +145,6 @@ GhostNet是华为于2020年提出的一种全新的轻量化网络结构,通
| ShuffleNetV2_x1_5 | 1.51054 | 2.4565 | 3.41738 | 1.45389 | 2.5203 | 3.99872 | | ShuffleNetV2_x1_5 | 1.51054 | 2.4565 | 3.41738 | 1.45389 | 2.5203 | 3.99872 |
| ShuffleNetV2_x2_0 | 1.95616 | 2.44751 | 4.19173 | 2.15654 | 3.18247 | 5.46893 | | ShuffleNetV2_x2_0 | 1.95616 | 2.44751 | 4.19173 | 2.15654 | 3.18247 | 5.46893 |
| ShuffleNetV2_swish | 2.50213 | 2.92881 | 3.474 | 2.5129 | 2.97422 | 3.69357 | | ShuffleNetV2_swish | 2.50213 | 2.92881 | 3.474 | 2.5129 | 2.97422 | 3.69357 |
| GhostNet_x0_5 | 2.64492 | 3.48473 | 4.48844 | 2.36115 | 3.52802 | 3.89444 |
| GhostNet_x1_0 | 2.63120 | 3.92065 | 4.48296 | 2.57042 | 3.56296 | 4.85524 |
| GhostNet_x1_3 | 2.89715 | 3.80329 | 4.81661 | 2.81810 | 3.72071 | 5.92269 |
...@@ -163,7 +163,7 @@ python tools/infer/predict.py \ ...@@ -163,7 +163,7 @@ python tools/infer/predict.py \
- [HRNet_W48_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams) - [HRNet_W48_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams)
- [HRNet_W48_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_ssld_pretrained.pdparams) - [HRNet_W48_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_ssld_pretrained.pdparams)
- [HRNet_W64_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams) - [HRNet_W64_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams)
- [SE_HRNet_W64_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_HRNet_W64_C_ssld_pretrained.pdparams)
- DPN与DenseNet系列 - DPN与DenseNet系列
- DPN系列<sup>[[14](#ref14)]</sup>([论文地址](https://arxiv.org/abs/1707.01629)) - DPN系列<sup>[[14](#ref14)]</sup>([论文地址](https://arxiv.org/abs/1707.01629))
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# 更新日志 # 更新日志
- 2020.12.06
* 添加SE_HRNet_W64_C_ssld模型,在ImageNet上Top-1 Acc可达0.8475。
- 2020.11.23 - 2020.11.23
* 添加GhostNet_x1_3_ssld模型,在ImageNet上Top-1 Acc可达0.7938。 * 添加GhostNet_x1_3_ssld模型,在ImageNet上Top-1 Acc可达0.7938。
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