shufflenet_v2_x1_0¶

paddle.vision.models. shufflenet_v2_x1_0 ( pretrained=False, **kwargs ) [source]

ShuffleNetV2 with 1.0x output channels, as described in “ShuffleNet V2: Practical Guidelines for Ecient CNN Architecture Design” 。

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet. Default: False.

Examples

import paddle
from paddle.vision.models import shufflenet_v2_x1_0

# build model
model = shufflenet_v2_x1_0()

# build model and load imagenet pretrained weight
# model = shufflenet_v2_x1_0(pretrained=True)

x = paddle.rand([1, 3, 224, 224])
out = model(x)

print(out.shape)