- paddle.vision.models. resnext152_32x4d ( pretrained=False, **kwargs ) [source]
ResNeXt-152 32x4d model from “Aggregated Residual Transformations for Deep Neural Networks”.
pretrained (bool, optional) – Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False.
**kwargs (optional) – Additional keyword arguments. For details, please refer to ResNet.
Layer. An instance of ResNeXt-152 32x4d model.
import paddle from paddle.vision.models import resnext152_32x4d # build model model = resnext152_32x4d() # build model and load imagenet pretrained weight # model = resnext152_32x4d(pretrained=True) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000]