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PaddleClas
提交 ae2cbc31 编写于 作者: G gaotingquan
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README.md
浏览文件 @ ae2cbc31
......@@ -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.
**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。
- 2020.10.10 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%.
- 2020.09.07 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%.
- 2020.06.17 Add English documents.
- 2020.06.12 Add support for training and evaluation on Windows or CPU.
- [more](./docs/en/update_history_en.md)
......
README_cn.md
浏览文件 @ ae2cbc31
......@@ -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。
**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。
- 2020.10.10 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%。
- 2020.09.07 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%。
- 2020.06.17 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)
......
configs/EfficientNet/EfficientNetB0.yaml
浏览文件 @ ae2cbc31
......@@ -2,7 +2,6 @@ mode: 'train'
ARCHITECTURE:
name: "EfficientNetB0"
params:
is_test: False
padding_type : "SAME"
override_params:
drop_connect_rate: 0.1
......
docs/en/models/SEResNext_and_Res2Net_en.md
浏览文件 @ ae2cbc31
......@@ -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_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 |
| 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_ssld | **0.851** | 0.974 | | | 31.490 | 76.210 |
| ResNeXt50_32x4d | 0.778 | 0.938 | 0.778 | | 8.020 | 23.640 |
......
docs/en/update_history_en.md
浏览文件 @ ae2cbc31
# 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
* Add Paddle-Lite demo.
......
docs/zh_CN/models/SEResNext_and_Res2Net.md
浏览文件 @ ae2cbc31
......@@ -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_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 |
| 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_ssld | **0.851** | 0.974 | | | 31.490 | 76.210 |
| ResNeXt50_32x4d | 0.778 | 0.938 | 0.778 | | 8.020 | 23.640 |
......
docs/zh_CN/update_history.md
浏览文件 @ ae2cbc31
# 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
* 添加Paddle-Lite demo。
......
ppcls/modeling/architectures/efficientnet.py
浏览文件 @ ae2cbc31
......@@ -518,7 +518,6 @@ class MbConvBlock(nn.Layer):
use_se,
name=None,
drop_connect_rate=None,
is_test=False,
model_name=None,
cur_stage=None):
super(MbConvBlock, self).__init__()
......@@ -530,7 +529,6 @@ class MbConvBlock(nn.Layer):
self.id_skip = block_args.id_skip
self.expand_ratio = block_args.expand_ratio
self.drop_connect_rate = drop_connect_rate
self.is_test = is_test
if self.expand_ratio != 1:
self._ecn = ExpandConvNorm(
......@@ -583,7 +581,7 @@ class MbConvBlock(nn.Layer):
self.block_args.stride == 1 and \
self.block_args.input_filters == self.block_args.output_filters:
if self.drop_connect_rate:
x = _drop_connect(x, self.drop_connect_rate, self.is_test)
x = _drop_connect(x, self.drop_connect_rate, not self.training)
x = paddle.elementwise_add(x, inputs)
return x
......@@ -623,7 +621,6 @@ class ExtractFeatures(nn.Layer):
_global_params,
padding_type,
use_se,
is_test,
model_name=None):
super(ExtractFeatures, self).__init__()
......@@ -661,7 +658,7 @@ class ExtractFeatures(nn.Layer):
num_repeat=round_repeats(block_args.num_repeat,
_global_params))
drop_connect_rate = self._global_params.drop_connect_rate if not is_test else 0
drop_connect_rate = self._global_params.drop_connect_rate
if drop_connect_rate:
drop_connect_rate *= float(idx) / block_size
......@@ -682,7 +679,7 @@ class ExtractFeatures(nn.Layer):
block_args = block_args._replace(
input_filters=block_args.output_filters, stride=1)
for _ in range(block_args.num_repeat - 1):
drop_connect_rate = self._global_params.drop_connect_rate if not is_test else 0
drop_connect_rate = self._global_params.drop_connect_rate
if drop_connect_rate:
drop_connect_rate *= float(idx) / block_size
_mc_block = self.add_sublayer(
......@@ -711,7 +708,6 @@ class ExtractFeatures(nn.Layer):
class EfficientNet(nn.Layer):
def __init__(self,
name="b0",
is_test=True,
padding_type="SAME",
override_params=None,
use_se=True,
......@@ -724,7 +720,6 @@ class EfficientNet(nn.Layer):
model_name, override_params)
self.padding_type = padding_type
self.use_se = use_se
self.is_test = is_test
self._ef = ExtractFeatures(
3,
......@@ -732,7 +727,6 @@ class EfficientNet(nn.Layer):
self._global_params,
self.padding_type,
self.use_se,
self.is_test,
model_name=self.name)
output_channels = round_filters(1280, self._global_params)
......@@ -785,14 +779,12 @@ class EfficientNet(nn.Layer):
return x
def EfficientNetB0_small(is_test=True,
padding_type='DYNAMIC',
def EfficientNetB0_small(padding_type='DYNAMIC',
override_params=None,
use_se=False,
**args):
model = EfficientNet(
name='b0',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
......@@ -800,14 +792,12 @@ def EfficientNetB0_small(is_test=True,
return model
def EfficientNetB0(is_test=False,
padding_type='SAME',
def EfficientNetB0(padding_type='SAME',
override_params=None,
use_se=True,
**args):
model = EfficientNet(
name='b0',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
......@@ -815,14 +805,12 @@ def EfficientNetB0(is_test=False,
return model
def EfficientNetB1(is_test=False,
padding_type='SAME',
def EfficientNetB1(padding_type='SAME',
override_params=None,
use_se=True,
**args):
model = EfficientNet(
name='b1',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
......@@ -830,14 +818,12 @@ def EfficientNetB1(is_test=False,
return model
def EfficientNetB2(is_test=False,
padding_type='SAME',
def EfficientNetB2(padding_type='SAME',
override_params=None,
use_se=True,
**args):
model = EfficientNet(
name='b2',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
......@@ -845,14 +831,12 @@ def EfficientNetB2(is_test=False,
return model
def EfficientNetB3(is_test=False,
padding_type='SAME',
def EfficientNetB3(padding_type='SAME',
override_params=None,
use_se=True,
**args):
model = EfficientNet(
name='b3',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
......@@ -860,14 +844,12 @@ def EfficientNetB3(is_test=False,
return model
def EfficientNetB4(is_test=False,
padding_type='SAME',
def EfficientNetB4(padding_type='SAME',
override_params=None,
use_se=True,
**args):
model = EfficientNet(
name='b4',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
......@@ -875,14 +857,12 @@ def EfficientNetB4(is_test=False,
return model
def EfficientNetB5(is_test=False,
padding_type='SAME',
def EfficientNetB5(padding_type='SAME',
override_params=None,
use_se=True,
**args):
model = EfficientNet(
name='b5',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
......@@ -890,14 +870,12 @@ def EfficientNetB5(is_test=False,
return model
def EfficientNetB6(is_test=False,
padding_type='SAME',
def EfficientNetB6(padding_type='SAME',
override_params=None,
use_se=True,
**args):
model = EfficientNet(
name='b6',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
......@@ -905,14 +883,12 @@ def EfficientNetB6(is_test=False,
return model
def EfficientNetB7(is_test=False,
padding_type='SAME',
def EfficientNetB7(padding_type='SAME',
override_params=None,
use_se=True,
**args):
model = EfficientNet(
name='b7',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
......
ppcls/modeling/architectures/res2net_vd.py
浏览文件 @ ae2cbc31
......@@ -57,7 +57,6 @@ class ConvBNLayer(nn.Layer):
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
if name == "conv1":
......@@ -111,8 +110,7 @@ class BottleneckBlock(nn.Layer):
act='relu',
name=name + '_branch2b_' + str(s + 1)))
self.conv1_list.append(conv1)
self.pool2d_avg = AvgPool2d(
kernel_size=3, stride=stride, padding=1, ceil_mode=True)
self.pool2d_avg = AvgPool2d(kernel_size=3, stride=stride, padding=1)
self.conv2 = ConvBNLayer(
num_channels=num_filters,
......
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