def inception_v3(pretrained=False, model_root=None, **kwargs): if pretrained: if 'transform_input' not in kwargs: kwargs['transform_input'] = True model = Inception3(**kwargs) misc.load_state_dict(model, model_urls['inception_v3_google'], model_root) return model return Inception3(**kwargs)
def squeezenet1_0(pretrained=False, model_root=None, **kwargs): r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size" <https://arxiv.org/abs/1602.07360>`_ paper. """ model = SqueezeNet(version=1.0, **kwargs) if pretrained: misc.load_state_dict(model, model_urls['squeezenet1_0'], model_root) return model
def squeezenet1_1(pretrained=False, model_root=None, **kwargs): r"""SqueezeNet 1.1 model from the `official SqueezeNet repo <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_. SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy. """ model = SqueezeNet(version=1.1, **kwargs) if pretrained: misc.load_state_dict(model, model_urls['squeezenet1_1'], model_root) return model
def resnet152(pretrained=False, model_root=None, **kwargs): model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) if pretrained: misc.load_state_dict(model, model_urls['resnet152'], model_root) return model
def resnet34(pretrained=False, model_root=None, **kwargs): model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) if pretrained: misc.load_state_dict(model, model_urls['resnet34'], model_root) return model