Most other projects on the web are using adapters based on the CM108 chip. 108 radio interface Having the key ingredients and the rest of the parts in my junk box I decided to put together a "radio-less" node interface that will also make a great test box. shufflenet_v2_x1_0 ( pretrained=False, progress=True, **kwargs ) ¶Ĭonstructs a ShuffleNetV2 with 1.0x output channels, as described in “ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design”. shufflenet_v2_x0_5 ( pretrained=False, progress=True, **kwargs ) ¶Ĭonstructs a ShuffleNetV2 with 0.5x output channels, as described in densenet201 ( pretrained=False, progress=True, **kwargs ) ¶ densenet161 ( pretrained=False, progress=True, **kwargs ) ¶ densenet169 ( pretrained=False, progress=True, **kwargs ) ¶
resnet101 ( pretrained=False, progress=True, **kwargs ) ¶ resnet50 ( pretrained=False, progress=True, **kwargs ) ¶ resnet34 ( pretrained=False, progress=True, **kwargs ) ¶ “Deep Residual Learning for Image Recognition” Parameters: resnet18 ( pretrained=False, progress=True, **kwargs ) ¶ VGG 19-layer model (configuration ‘E’) with batch normalization vgg19_bn ( pretrained=False, progress=True, **kwargs ) ¶ vgg19 ( pretrained=False, progress=True, **kwargs ) ¶ VGG 16-layer model (configuration “D”) with batch normalization vgg16_bn ( pretrained=False, progress=True, **kwargs ) ¶ vgg16 ( pretrained=False, progress=True, **kwargs ) ¶ VGG 13-layer model (configuration “B”) with batch normalization vgg13_bn ( pretrained=False, progress=True, **kwargs ) ¶
vgg13 ( pretrained=False, progress=True, **kwargs ) ¶ VGG 11-layer model (configuration “A”) with batch normalization vgg11_bn ( pretrained=False, progress=True, **kwargs ) ¶
ELEMENT 3D V2 AUXILARY CHANNEL DOWNLOAD
This directory can be set using the TORCH_MODEL_ZOO environment variable. Instancing a pre-trained model will download its weights to a cache directory. wide_resnet50_2 ( pretrained = True ) mnasnet = models. resnext50_32x4d ( pretrained = True ) wide_resnet50_2 = models. mobilenet_v2 ( pretrained = True ) resnext50_32x4d = models. shufflenet_v2_x1_0 ( pretrained = True ) mobilenet = models. googlenet ( pretrained = True ) shufflenet = models. inception_v3 ( pretrained = True ) googlenet = models. densenet161 ( pretrained = True ) inception = models. vgg16 ( pretrained = True ) densenet = models. squeezenet1_0 ( pretrained = True ) vgg16 = models. alexnet ( pretrained = True ) squeezenet = models. resnet18 ( pretrained = True ) alexnet = models. Import torchvision.models as models resnet18 = models.