提交 d4533682 编写于 作者: littletomatodonkey's avatar littletomatodonkey

fix res2net code and doc

上级 50e5000a
...@@ -6,15 +6,13 @@ ...@@ -6,15 +6,13 @@
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**
- 2020.09.17 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%.
- 2020.10.12 Add Paddle-Lite demo。 - 2020.10.12 Add Paddle-Lite demo。
- 2020.10.10 Add cpp inference demo and improve FAQ tutorial. - 2020.10.10 Add cpp inference demo and improve FAQ tutorial.
- 2020.09.17 Add `HRNet_W48_C_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.62%. Add `ResNet34_vd_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 79.72%. - 2020.09.17 Add `HRNet_W48_C_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.62%. Add `ResNet34_vd_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 79.72%.
- 2020.09.07 Add `HRNet_W18_C_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 81.16%. - 2020.09.07 Add `HRNet_W18_C_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 81.16%.
- 2020.07.14 Add `Res2Net200_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 85.13%. Add `Fix_ResNet50_vd_ssld_v2` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 84.00%. - 2020.07.14 Add `Res2Net200_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 85.13%. Add `Fix_ResNet50_vd_ssld_v2` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 84.00%.
- 2020.06.17 Add English documents.
- 2020.06.12 Add support for training and evaluation on Windows or CPU.
- [more](./docs/en/update_history_en.md) - [more](./docs/en/update_history_en.md)
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...@@ -7,13 +7,12 @@ ...@@ -7,13 +7,12 @@
飞桨图像分类套件PaddleClas是飞桨为工业界和学术界所准备的一个图像分类任务的工具集,助力使用者训练出更好的视觉模型和应用落地。 飞桨图像分类套件PaddleClas是飞桨为工业界和学术界所准备的一个图像分类任务的工具集,助力使用者训练出更好的视觉模型和应用落地。
**近期更新** **近期更新**
- 2020.10.20 添加 `Res2Net50_vd_26w_4s_ssld `模型,在ImageNet-1k上Top-1 Acc可达83.1%;添加 `Res2Net101_vd_26w_4s_ssld `模型,在ImageNet-1k上Top-1 Acc可达83.9%。
- 2020.10.12 添加Paddle-Lite demo。 - 2020.10.12 添加Paddle-Lite demo。
- 2020.10.10 添加cpp inference demo,完善`FAQ 30问`教程。 - 2020.10.10 添加cpp inference demo,完善`FAQ 30问`教程。
- 2020.09.17 添加 `HRNet_W48_C_ssld `模型,在ImageNet-1k上Top-1 Acc可达83.62%;添加 `ResNet34_vd_ssld `模型,在ImageNet-1k上Top-1 Acc可达79.72%。 - 2020.09.17 添加 `HRNet_W48_C_ssld `模型,在ImageNet-1k上Top-1 Acc可达83.62%;添加 `ResNet34_vd_ssld `模型,在ImageNet-1k上Top-1 Acc可达79.72%。
- 2020.09.07 添加 `HRNet_W18_C_ssld `模型,在ImageNet-1k上Top-1 Acc可达81.16%;添加 `MobileNetV3_small_x0_35_ssld `模型,在ImageNet-1k上Top-1 Acc可达55.55%。 - 2020.09.07 添加 `HRNet_W18_C_ssld `模型,在ImageNet-1k上Top-1 Acc可达81.16%;添加 `MobileNetV3_small_x0_35_ssld `模型,在ImageNet-1k上Top-1 Acc可达55.55%。
- 2020.07.14 添加 `Res2Net200_vd_26w_4s_ssld `模型,在ImageNet-1k上Top-1 Acc可达85.13%;添加 `Fix_ResNet50_vd_ssld_v2 `模型,在ImageNet-1k上Top-1 Acc可达84.0%。 - 2020.07.14 添加 `Res2Net200_vd_26w_4s_ssld `模型,在ImageNet-1k上Top-1 Acc可达85.13%;添加 `Fix_ResNet50_vd_ssld_v2 `模型,在ImageNet-1k上Top-1 Acc可达84.0%。
- 2020.06.17 添加英文文档。
- 2020.06.12 添加对windows和CPU环境的训练与评估支持。
- [more](./docs/zh_CN/update_history.md) - [more](./docs/zh_CN/update_history.md)
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...@@ -30,8 +30,10 @@ At present, there are a total of 24 pretrained models of the three categories op ...@@ -30,8 +30,10 @@ At present, there are a total of 24 pretrained models of the three categories op
|:--:|:--:|:--:|:--:|:--:|:--:|:--:| |:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| Res2Net50_26w_4s | 0.793 | 0.946 | 0.780 | 0.936 | 8.520 | 25.700 | | Res2Net50_26w_4s | 0.793 | 0.946 | 0.780 | 0.936 | 8.520 | 25.700 |
| Res2Net50_vd_26w_4s | 0.798 | 0.949 | | | 8.370 | 25.060 | | Res2Net50_vd_26w_4s | 0.798 | 0.949 | | | 8.370 | 25.060 |
| Res2Net50_vd_26w_4s_ssld | 0.831 | 0.966 | | | 8.370 | 25.060 |
| Res2Net50_14w_8s | 0.795 | 0.947 | 0.781 | 0.939 | 9.010 | 25.720 | | Res2Net50_14w_8s | 0.795 | 0.947 | 0.781 | 0.939 | 9.010 | 25.720 |
| Res2Net101_vd_26w_4s | 0.806 | 0.952 | | | 16.670 | 45.220 | | Res2Net101_vd_26w_4s | 0.806 | 0.952 | | | 16.670 | 45.220 |
| Res2Net101_vd_26w_4s_ssld | 0.839 | 0.971 | | | 16.670 | 45.220 |
| Res2Net200_vd_26w_4s | 0.812 | 0.957 | | | 31.490 | 76.210 | | Res2Net200_vd_26w_4s | 0.812 | 0.957 | | | 31.490 | 76.210 |
| Res2Net200_vd_26w_4s_ssld | **0.851** | 0.974 | | | 31.490 | 76.210 | | Res2Net200_vd_26w_4s_ssld | **0.851** | 0.974 | | | 31.490 | 76.210 |
| ResNeXt50_32x4d | 0.778 | 0.938 | 0.778 | | 8.020 | 23.640 | | ResNeXt50_32x4d | 0.778 | 0.938 | 0.778 | | 8.020 | 23.640 |
......
# Release Notes # Release Notes
* 2020.10.20
* Add `Res2Net50_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 83.1%.
* Add `Res2Net101_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 83.9%.
- 2020.10.12 - 2020.10.12
* Add Paddle-Lite demo. * Add Paddle-Lite demo.
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...@@ -29,8 +29,10 @@ Res2Net是2019年提出的一种全新的对ResNet的改进方案,该方案可 ...@@ -29,8 +29,10 @@ Res2Net是2019年提出的一种全新的对ResNet的改进方案,该方案可
|:--:|:--:|:--:|:--:|:--:|:--:|:--:| |:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| Res2Net50_26w_4s | 0.793 | 0.946 | 0.780 | 0.936 | 8.520 | 25.700 | | Res2Net50_26w_4s | 0.793 | 0.946 | 0.780 | 0.936 | 8.520 | 25.700 |
| Res2Net50_vd_26w_4s | 0.798 | 0.949 | | | 8.370 | 25.060 | | Res2Net50_vd_26w_4s | 0.798 | 0.949 | | | 8.370 | 25.060 |
| Res2Net50_vd_26w_4s_ssld | 0.831 | 0.966 | | | 8.370 | 25.060 |
| Res2Net50_14w_8s | 0.795 | 0.947 | 0.781 | 0.939 | 9.010 | 25.720 | | Res2Net50_14w_8s | 0.795 | 0.947 | 0.781 | 0.939 | 9.010 | 25.720 |
| Res2Net101_vd_26w_4s | 0.806 | 0.952 | | | 16.670 | 45.220 | | Res2Net101_vd_26w_4s | 0.806 | 0.952 | | | 16.670 | 45.220 |
| Res2Net101_vd_26w_4s_ssld | 0.839 | 0.971 | | | 16.670 | 45.220 |
| Res2Net200_vd_26w_4s | 0.812 | 0.957 | | | 31.490 | 76.210 | | Res2Net200_vd_26w_4s | 0.812 | 0.957 | | | 31.490 | 76.210 |
| Res2Net200_vd_26w_4s_ssld | **0.851** | 0.974 | | | 31.490 | 76.210 | | Res2Net200_vd_26w_4s_ssld | **0.851** | 0.974 | | | 31.490 | 76.210 |
| ResNeXt50_32x4d | 0.778 | 0.938 | 0.778 | | 8.020 | 23.640 | | ResNeXt50_32x4d | 0.778 | 0.938 | 0.778 | | 8.020 | 23.640 |
......
# 更新日志 # 更新日志
- 2020.10.20
* 添加Res2Net50_vd_26w_4s_ssld模型,在ImageNet上Top-1 Acc可达0.831;添加Res2Net101_vd_26w_4s_ssld模型,在ImageNet上Top-1 Acc可达0.839。
- 2020.10.12 - 2020.10.12
* 添加Paddle-Lite demo。 * 添加Paddle-Lite demo。
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...@@ -57,7 +57,6 @@ class ConvBNLayer(nn.Layer): ...@@ -57,7 +57,6 @@ class ConvBNLayer(nn.Layer):
stride=stride, stride=stride,
padding=(filter_size - 1) // 2, padding=(filter_size - 1) // 2,
groups=groups, groups=groups,
act=None,
weight_attr=ParamAttr(name=name + "_weights"), weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False) bias_attr=False)
if name == "conv1": if name == "conv1":
...@@ -111,8 +110,7 @@ class BottleneckBlock(nn.Layer): ...@@ -111,8 +110,7 @@ class BottleneckBlock(nn.Layer):
act='relu', act='relu',
name=name + '_branch2b_' + str(s + 1))) name=name + '_branch2b_' + str(s + 1)))
self.conv1_list.append(conv1) self.conv1_list.append(conv1)
self.pool2d_avg = AvgPool2d( self.pool2d_avg = AvgPool2d(kernel_size=3, stride=stride, padding=1)
kernel_size=3, stride=stride, padding=1, ceil_mode=True)
self.conv2 = ConvBNLayer( self.conv2 = ConvBNLayer(
num_channels=num_filters, num_channels=num_filters,
......
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