flowvision.models¶

Pretrain Models for Visual Tasks

Classification¶

The models subpackage contains definitions for the following model architectures for image classification:

Alexnet¶

flowvision.models.alexnet(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.alexnet.AlexNet[source]¶

Constructs the AlexNet model.

Note

AlexNet model architecture from the One weird trick… paper. The required minimum input size of this model is 63x63.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> alexnet = flowvision.models.alexnet(pretrained=False, progress=True)

SqueezeNet¶

flowvision.models.squeezenet1_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.squeezenet.SqueezeNet[source]¶

Constructs the SqueezeNet model.

Note

SqueezeNet model from the SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> squeezenet1_0 = flowvision.models.squeezenet1_0(pretrained=False, progress=True)

flowvision.models.squeezenet1_1(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.squeezenet.SqueezeNet[source]¶

Constructs the SqueezeNet 1.1 model.

Note

SqueezeNet 1.1 model from the SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size paper. Note that SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> squeezenet1_1 = flowvision.models.squeezenet1_1(pretrained=False, progress=True)

VGG¶

flowvision.models.vgg11(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.vgg.VGG[source]¶

Constructs the VGG-11 model (configuration “A”).

Note

VGG 11-layer model (configuration “A”) from “Very Deep Convolutional Networks For Large-Scale Image Recognition”. The required minimum input size of the model is 32x32.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vgg11 = flowvision.models.vgg11(pretrained=False, progress=True)

flowvision.models.vgg11_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.vgg.VGG[source]¶

Constructs the VGG-11 model with batch normalization (configuration “A”).

Note

VGG 11-layer model (configuration “A”) with batch normalization “Very Deep Convolutional Networks For Large-Scale Image Recognition”. The required minimum input size of the model is 32x32.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vgg11_bn = flowvision.models.vgg11_bn(pretrained=False, progress=True)

flowvision.models.vgg13(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.vgg.VGG[source]¶

Constructs the VGG-13 model (configuration “B”).

Note

VGG 13-layer model (configuration “B”) from “Very Deep Convolutional Networks For Large-Scale Image Recognition”. The required minimum input size of the model is 32x32.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vgg13 = flowvision.models.vgg13(pretrained=False, progress=True)

flowvision.models.vgg13_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.vgg.VGG[source]¶

Constructs the VGG-13 model with batch normalization (configuration “B”).

Note

VGG 13-layer model (configuration “B”) with batch normalization from “Very Deep Convolutional Networks For Large-Scale Image Recognition”. The required minimum input size of the model is 32x32.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vgg13_bn = flowvision.models.vgg13_bn(pretrained=False, progress=True)

flowvision.models.vgg16(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.vgg.VGG[source]¶

Constructs the VGG-16 model (configuration “D”).

Note

VGG 16-layer model (configuration “D”) from “Very Deep Convolutional Networks For Large-Scale Image Recognition”. The required minimum input size of the model is 32x32.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vgg16 = flowvision.models.vgg16(pretrained=False, progress=True)

flowvision.models.vgg16_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.vgg.VGG[source]¶

Constructs the VGG-16 model (configuration “D”) with batch normalization.

Note

VGG 16-layer model (configuration “D”) with batch normalization from “Very Deep Convolutional Networks For Large-Scale Image Recognition”. The required minimum input size of the model is 32x32.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vgg16_bn = flowvision.models.vgg16_bn(pretrained=False, progress=True)

flowvision.models.vgg19(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.vgg.VGG[source]¶

Constructs the VGG-19 model (configuration “E”).

Note

VGG 19-layer model (configuration “E”) from “Very Deep Convolutional Networks For Large-Scale Image Recognition”. The required minimum input size of the model is 32x32.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vgg19 = flowvision.models.vgg19(pretrained=False, progress=True)

flowvision.models.vgg19_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.vgg.VGG[source]¶

Constructs the VGG-19 model (configuration “E”) with batch normalization.

Note

VGG 19-layer model (configuration “E”) with batch normalization from “Very Deep Convolutional Networks For Large-Scale Image Recognition”. The required minimum input size of the model is 32x32.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vgg19_bn = flowvision.models.vgg19_bn(pretrained=False, progress=True)

GoogLeNet¶

flowvision.models.googlenet(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.googlenet.GoogLeNet[source]¶

Constructs the GoogLeNet (Inception v1) model.

Note

GoogLeNet (Inception v1) model from the Going Deeper with Convolutions paper. The required minimum input size of the model is 15x15.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True
aux_logits (bool) – If True, adds two auxiliary branches that can improve training. Default: False when pretrained is True otherwise True
transform_input (bool) – If True, preprocesses the input according to the method with which it was trained on ImageNet. Default: False

For example:

>>> import flowvision
>>> googlenet = flowvision.models.googlenet(pretrained=False, progress=True)

InceptionV3¶

flowvision.models.inception_v3(pretrained: bool = False, progress: bool = True, **kwargs: Any)[source]¶

Constructs Inception v3 model.

Note

Inception v3 model from the Rethinking the Inception Architecture for Computer Vision paper. In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True
aux_logits (bool) – If True, add an auxiliary branch that can improve training. Default: True
transform_input (bool) – If True, preprocesses the input according to the method with which it was trained on ImageNet. Default: False

For example:

>>> import flowvision
>>> inception_v3 = flowvision.models.inception_v3(pretrained=False, progress=True)

ResNet¶

flowvision.models.resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.resnet.ResNet[source]¶

Constructs the ResNet-101 model.

Note

Deep Residual Learning for Image Recognition.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resnet101 = flowvision.models.resnet101(pretrained=False, progress=True)

flowvision.models.resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.resnet.ResNet[source]¶

Constructs the ResNet-152 model.

Note

Deep Residual Learning for Image Recognition.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resnet152 = flowvision.models.resnet152(pretrained=False, progress=True)

flowvision.models.resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.resnet.ResNet[source]¶

Constructs the ResNet-18 model.

Note

Deep Residual Learning for Image Recognition.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resnet18 = flowvision.models.resnet18(pretrained=False, progress=True)

flowvision.models.resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.resnet.ResNet[source]¶

Constructs the ResNet-34 model.

Note

Deep Residual Learning for Image Recognition.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resnet34 = flowvision.models.resnet34(pretrained=False, progress=True)

flowvision.models.resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.resnet.ResNet[source]¶

Constructs the ResNet-50 model.

Note

Deep Residual Learning for Image Recognition.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resnet50 = flowvision.models.resnet50(pretrained=False, progress=True)

flowvision.models.resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.resnet.ResNet[source]¶

Constructs the ResNeXt-101 32x8d model.

Note

Aggregated Residual Transformation for Deep Neural Networks.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resnext101_32x8d = flowvision.models.resnext101_32x8d(pretrained=False, progress=True)

flowvision.models.resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.resnet.ResNet[source]¶

Constructs the ResNeXt-50 32x4d model.

Note

Aggregated Residual Transformation for Deep Neural Networks.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resnext50_32x4d = flowvision.models.resnext50_32x4d(pretrained=False, progress=True)

flowvision.models.wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.resnet.ResNet[source]¶

Constructs the Wide ResNet-101-2 model.

Note

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> wide_resnet101_2 = flowvision.models.wide_resnet101_2(pretrained=False, progress=True)

flowvision.models.wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.resnet.ResNet[source]¶

Constructs the Wide ResNet-50-2 model.

Note

Wide Residual Networks. The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> wide_resnet50_2 = flowvision.models.wide_resnet50_2(pretrained=False, progress=True)

DenseNet¶

flowvision.models.densenet121(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.densenet.DenseNet[source]¶

Constructs the DenseNet-121 model.

Note

DenseNet-121 model architecture from the Densely Connected Convolutional Networks paper. The required minimum input size of the model is 29x29.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> densenet121 = flowvision.models.densenet121(pretrained=False, progress=True)

flowvision.models.densenet161(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.densenet.DenseNet[source]¶

Constructs the DenseNet-161 model.

Note

DenseNet-161 model architecture from the Densely Connected Convolutional Networks paper. The required minimum input size of the model is 29x29.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> densenet161 = flowvision.models.densenet161(pretrained=False, progress=True)

flowvision.models.densenet169(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.densenet.DenseNet[source]¶

Constructs the DenseNet-169 model.

Note

DenseNet-169 model architecture from the Densely Connected Convolutional Networks paper. The required minimum input size of the model is 29x29.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> densenet169 = flowvision.models.densenet169(pretrained=False, progress=True)

flowvision.models.densenet201(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.densenet.DenseNet[source]¶

Constructs the DenseNet-201 model.

Note

DenseNet-201 model architecture from the Densely Connected Convolutional Networks paper. The required minimum input size of the model is 29x29.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> densenet201 = flowvision.models.densenet201(pretrained=False, progress=True)

ShuffleNetV2¶

flowvision.models.shufflenet_v2_x0_5(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.shufflenet_v2.ShuffleNetV2[source]¶

Constructs the ShuffleNetV2(0.5x) model.

Note

ShuffleNetV2 with 0.5x output channels model architecture from the ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> shufflenet_v2_x0_5 = flowvision.models.shufflenet_v2_x0_5(pretrained=False, progress=True)

flowvision.models.shufflenet_v2_x1_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.shufflenet_v2.ShuffleNetV2[source]¶

Constructs the ShuffleNetV2(1.0x) model.

Note

ShuffleNetV2 with 1.0x output channels model architecture from the ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> shufflenet_v2_x1_0 = flowvision.models.shufflenet_v2_x1_0(pretrained=False, progress=True)

flowvision.models.shufflenet_v2_x1_5(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.shufflenet_v2.ShuffleNetV2[source]¶

Constructs the ShuffleNetV2(1.5x) model.

Note

ShuffleNetV2 with 1.5x output channels model architecture from the ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> shufflenet_v2_x1_5 = flowvision.models.shufflenet_v2_x1_5(pretrained=False, progress=True)

flowvision.models.shufflenet_v2_x2_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.shufflenet_v2.ShuffleNetV2[source]¶

Constructs the ShuffleNetV2(2.0x) model.

Note

ShuffleNetV2 with 2.0x output channels model architecture from the ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> shufflenet_v2_x2_0 = flowvision.models.shufflenet_v2_x2_0(pretrained=False, progress=True)

MobileNetV2¶

flowvision.models.mobilenet_v2(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.mobilenet_v2.MobileNetV2[source]¶

Constructs the MobileNetV2 model.

Note

MobileNetV2 model architecture from the MobileNetV2: Inverted Residuals and Linear Bottlenecks paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> mobilenet_v2 = flowvision.models.mobilenet_v2(pretrained=False, progress=True)

MobileNetV3¶

flowvision.models.mobilenet_v3_large(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.mobilenet_v3.MobileNetV3[source]¶

Constructs the MobileNetV3-Large model.

Note

MobileNetV3-Large model architecture from the Searching for MobileNetV3 paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> mobilenet_v3_large = flowvision.models.mobilenet_v3_large(pretrained=False, progress=True)

flowvision.models.mobilenet_v3_small(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.mobilenet_v3.MobileNetV3[source]¶

Constructs the MobileNetV3-Small model.

Note

MobileNetV3-Small model architecture from the Searching for MobileNetV3 paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> mobilenet_v3_small = flowvision.models.mobilenet_v3_small(pretrained=False, progress=True)

MNASNet¶

flowvision.models.mnasnet0_5(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the MNASNet model with depth multiplier of 0.5.

Note

MNASNet model with depth multiplier of 0.5 from the MnasNet: Platform-Aware Neural Architecture Search for Mobile paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> mnasnet0_5 = flowvision.models.mnasnet0_5(pretrained=False, progress=True)

flowvision.models.mnasnet0_75(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the MNASNet model with depth multiplier of 0.75.

Note

MNASNet model with depth multiplier of 0.75 from the MnasNet: Platform-Aware Neural Architecture Search for Mobile paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> mnasnet0_75 = flowvision.models.mnasnet0_75(pretrained=False, progress=True)

flowvision.models.mnasnet1_0(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the MNASNet model with depth multiplier of 1.0.

Note

MNASNet model with depth multiplier of 1.0 from the MnasNet: Platform-Aware Neural Architecture Search for Mobile paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> mnasnet1_0 = flowvision.models.mnasnet1_0(pretrained=False, progress=True)

flowvision.models.mnasnet1_3(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the MNASNet model with depth multiplier of 1.3.

Note

MNASNet model with depth multiplier of 1.3 from the MnasNet: Platform-Aware Neural Architecture Search for Mobile paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> mnasnet1_3 = flowvision.models.mnasnet1_3(pretrained=False, progress=True)

GhostNet¶

flowvision.models.ghostnet(pretrained: bool = False, progress: bool = True, **kwargs: Any)[source]¶

Constructs the GhostNet model.

Note

GhostNet model from GhostNet: More Features from Cheap Operations.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> ghostnet = flowvision.models.ghostnet(pretrained=True, progress=True)

Res2Net¶

flowvision.models.res2net101_26w_4s(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the Res2Net-101_26w_4s model.

Note

Res2Net-101_26w_4s model from the Res2Net: A New Multi-scale Backbone Architecture paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> res2net101_26w_4s = flowvision.models.res2net101_26w_4s(pretrained=False, progress=True)

flowvision.models.res2net50_14w_8s(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the Res2Net-50_14w_8s model.

Note

Res2Net-50_14w_8s model from the Res2Net: A New Multi-scale Backbone Architecture paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> res2net50_14w_8s = flowvision.models.res2net50_14w_8s(pretrained=False, progress=True)

flowvision.models.res2net50_26w_4s(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the Res2Net-50_26w_4s model.

Note

Res2Net-50_26w_4s model from the Res2Net: A New Multi-scale Backbone Architecture paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> res2net50_26w_4s = flowvision.models.res2net50_26w_4s(pretrained=False, progress=True)

flowvision.models.res2net50_26w_6s(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the Res2Net-50_26w_6s model.

Note

Res2Net-50_26w_6s model from the Res2Net: A New Multi-scale Backbone Architecture paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> res2net50_26w_6s = flowvision.models.res2net50_26w_6s(pretrained=False, progress=True)

flowvision.models.res2net50_26w_8s(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the Res2Net-50_26w_8s model.

Note

Res2Net-50_26w_8s model from the Res2Net: A New Multi-scale Backbone Architecture paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> res2net50_26w_8s = flowvision.models.res2net50_26w_8s(pretrained=False, progress=True)

flowvision.models.res2net50_48w_2s(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the Res2Net-50_48w_2s model.

Note

Res2Net-50_48w_2s model from the Res2Net: A New Multi-scale Backbone Architecture paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> res2net50_48w_2s = flowvision.models.res2net50_48w_2s(pretrained=False, progress=True)

EfficientNet¶

flowvision.models.efficientnet_b0(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.efficientnet.EfficientNet[source]¶

Constructs the EfficientNet B0 model.

Note

EfficientNet B0 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper. Note that the (resize-size, crop-size) should be (256, 224) for efficientnet-b0 model when training and testing.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> efficientnet_b0 = flowvision.models.efficientnet_b0(pretrained=False, progress=True)

flowvision.models.efficientnet_b1(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.efficientnet.EfficientNet[source]¶

Constructs the EfficientNet B1 model.

Note

EfficientNet B1 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper. Note that the (resize-size, crop-size) should be (256, 240) for efficientnet-b1 model when training and testing.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> efficientnet_b1 = flowvision.models.efficientnet_b1(pretrained=False, progress=True)

flowvision.models.efficientnet_b2(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.efficientnet.EfficientNet[source]¶

Constructs the EfficientNet B2 model.

Note

EfficientNet B2 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper. Note that the (resize-size, crop-size) should be (288, 288) for efficientnet-b2 model when training and testing.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> efficientnet_b2 = flowvision.models.efficientnet_b2(pretrained=False, progress=True)

flowvision.models.efficientnet_b3(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.efficientnet.EfficientNet[source]¶

Constructs the EfficientNet B3 model.

Note

EfficientNet B3 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper. Note that the (resize-size, crop-size) should be (320, 300) for efficientnet-b3 model when training and testing.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> efficientnet_b3 = flowvision.models.efficientnet_b3(pretrained=False, progress=True)

flowvision.models.efficientnet_b4(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.efficientnet.EfficientNet[source]¶

Constructs the EfficientNet B4 model.

Note

EfficientNet B4 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper. Note that the (resize-size, crop-size) should be (384, 380) for efficientnet-b4 model when training and testing.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> efficientnet_b4 = flowvision.models.efficientnet_b4(pretrained=False, progress=True)

flowvision.models.efficientnet_b5(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.efficientnet.EfficientNet[source]¶

Constructs the EfficientNet B5 model.

Note

EfficientNet B5 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper. Note that the (resize-size, crop-size) should be (489, 456) for efficientnet-b5 model when training and testing.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> efficientnet_b5 = flowvision.models.efficientnet_b5(pretrained=False, progress=True)

flowvision.models.efficientnet_b6(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.efficientnet.EfficientNet[source]¶

Constructs the EfficientNet B6 model.

Note

EfficientNet B6 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper. Note that the (resize-size, crop-size) should be (561, 528) for efficientnet-b6 model when training and testing.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> efficientnet_b6 = flowvision.models.efficientnet_b6(pretrained=False, progress=True)

flowvision.models.efficientnet_b7(pretrained: bool = False, progress: bool = True, **kwargs: Any) → flowvision.models.efficientnet.EfficientNet[source]¶

Constructs the EfficientNet B7 model.

Note

EfficientNet B7 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper. Note that the (resize-size, crop-size) should be (633, 600) for efficientnet-b7 model when training and testing.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> efficientnet_b7 = flowvision.models.efficientnet_b7(pretrained=False, progress=True)

ReXNet¶

flowvision.models.rexnet_lite_1_0(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ReXNet-lite model with width multiplier of 1.0.

Note

ReXNet-lite model with width multiplier of 1.0 from the Rethinking Channel Dimensions for Efficient Model Design paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> rexnet_lite_1_0 = flowvision.models.rexnet_lite_1_0(pretrained=False, progress=True)

flowvision.models.rexnet_lite_1_3(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ReXNet-lite model with width multiplier of 1.3.

Note

ReXNet-lite model with width multiplier of 1.3 from the Rethinking Channel Dimensions for Efficient Model Design paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> rexnet_lite_1_3 = flowvision.models.rexnet_lite_1_3(pretrained=False, progress=True)

flowvision.models.rexnet_lite_1_5(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ReXNet-lite model with width multiplier of 1.5.

Note

ReXNet-lite model with width multiplier of 1.5 from the Rethinking Channel Dimensions for Efficient Model Design paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> rexnet_lite_1_5 = flowvision.models.rexnet_lite_1_5(pretrained=False, progress=True)

flowvision.models.rexnet_lite_2_0(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ReXNet-lite model with width multiplier of 2.0.

Note

ReXNet-lite model with width multiplier of 2.0 from the Rethinking Channel Dimensions for Efficient Model Design paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> rexnet_lite_2_0 = flowvision.models.rexnet_lite_2_0(pretrained=False, progress=True)

flowvision.models.rexnetv1_1_0(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ReXNet model with width multiplier of 1.0.

Note

ReXNet model with width multiplier of 1.0 from the Rethinking Channel Dimensions for Efficient Model Design paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> rexnetv1_1_0 = flowvision.models.rexnetv1_1_0(pretrained=False, progress=True)

flowvision.models.rexnetv1_1_3(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ReXNet model with width multiplier of 1.3.

Note

ReXNet model with width multiplier of 1.3 from the Rethinking Channel Dimensions for Efficient Model Design paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> rexnetv1_1_3 = flowvision.models.rexnetv1_1_3(pretrained=False, progress=True)

flowvision.models.rexnetv1_1_5(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ReXNet model with width multiplier of 1.5.

Note

ReXNet model with width multiplier of 1.5 from the Rethinking Channel Dimensions for Efficient Model Design paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> rexnetv1_1_5 = flowvision.models.rexnetv1_1_5(pretrained=False, progress=True)

flowvision.models.rexnetv1_2_0(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ReXNet model with width multiplier of 2.0.

Note

ReXNet model with width multiplier of 2.0 from the Rethinking Channel Dimensions for Efficient Model Design paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> rexnetv1_2_0 = flowvision.models.rexnetv1_2_0(pretrained=False, progress=True)

flowvision.models.rexnetv1_3_0(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ReXNet model with width multiplier of 3.0.

Note

ReXNet model with width multiplier of 3.0 from the Rethinking Channel Dimensions for Efficient Model Design paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> rexnetv1_3_0 = flowvision.models.rexnetv1_3_0(pretrained=False, progress=True)

ViT¶

flowvision.models.vit_base_patch16_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Base-patch16-224 model.

Note

ViT-Base-patch16-224 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_base_patch16_224 = flowvision.models.vit_base_patch16_224(pretrained=False, progress=True)

flowvision.models.vit_base_patch16_224_in21k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Base-patch16-224 ImageNet21k pretrained model.

Note

ViT-Base-patch16-224 ImageNet21k pretrained model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_base_patch16_224_in21k = flowvision.models.vit_base_patch16_224_in21k(pretrained=False, progress=True)

flowvision.models.vit_base_patch16_224_miil(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Base-patch16-224-miil model.

Note

ViT-Base-patch16-224-miil model from “Training data-efficient image transformers & distillation through attention”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_base_patch16_224_miil = flowvision.models.vit_base_patch16_224_miil(pretrained=False, progress=True)

flowvision.models.vit_base_patch16_224_miil_in21k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Base-patch16-224-miil ImageNet21k pretrained model.

Note

ViT-Base-patch16-224-miil ImageNet21k pretrained model from “Training data-efficient image transformers & distillation through attention”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_base_patch16_224_miil_in21k = flowvision.models.vit_base_patch16_224_miil_in21k(pretrained=False, progress=True)

flowvision.models.vit_base_patch16_224_sam(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Base-patch16-224-sam model.

Note

ViT-Base-patch16-224-sam model from “When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_base_patch16_224_sam = flowvision.models.vit_base_patch16_224_sam(pretrained=False, progress=True)

flowvision.models.vit_base_patch16_384(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Base-patch16-384 model.

Note

ViT-Base-patch16-384 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 384x384.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_base_patch16_384 = flowvision.models.vit_base_patch16_384(pretrained=False, progress=True)

flowvision.models.vit_base_patch32_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Base-patch32-224 model.

Note

ViT-Base-patch32-224 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_base_patch32_224 = flowvision.models.vit_base_patch32_224(pretrained=False, progress=True)

flowvision.models.vit_base_patch32_224_in21k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Base-patch32-224 ImageNet21k pretrained model.

Note

ViT-Base-patch32-224 ImageNet21k pretrained model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_base_patch32_224_in21k = flowvision.models.vit_base_patch32_224_in21k(pretrained=False, progress=True)

flowvision.models.vit_base_patch32_224_sam(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Base-patch32-224-sam model.

Note

ViT-Base-patch32-224-sam model from “When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_base_patch32_224_sam = flowvision.models.vit_base_patch32_224_sam(pretrained=False, progress=True)

flowvision.models.vit_base_patch32_384(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Base-patch32-384 model.

Note

ViT-Base-patch32-384 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 384x384.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_base_patch32_384 = flowvision.models.vit_base_patch32_384(pretrained=False, progress=True)

flowvision.models.vit_base_patch8_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Base-patch8-224 model.

Note

ViT-Base-patch8-224 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_base_patch8_224 = flowvision.models.vit_base_patch8_224(pretrained=False, progress=True)

flowvision.models.vit_base_patch8_224_in21k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Base-patch8-224 ImageNet21k pretrained model.

Note

ViT-Base-patch8-224 ImageNet21k pretrained model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_base_patch8_224_in21k = flowvision.models.vit_base_patch8_224_in21k(pretrained=False, progress=True)

flowvision.models.vit_giant_patch14_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Giant-patch14-224 model.

Note

ViT-Giant-patch14-224 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_giant_patch14_224 = flowvision.models.vit_giant_patch14_224(pretrained=False, progress=True)

flowvision.models.vit_gigantic_patch14_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Gigantic-patch14-224 model.

Note

ViT-Giant-patch14-224 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_gigantic_patch14_224 = flowvision.models.vit_gigantic_patch14_224(pretrained=False, progress=True)

flowvision.models.vit_huge_patch14_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Huge-patch14-224 model.

Note

ViT-Huge-patch14-224 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_huge_patch14_224 = flowvision.models.vit_huge_patch14_224(pretrained=False, progress=True)

flowvision.models.vit_huge_patch14_224_in21k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Huge-patch14-224 ImageNet21k pretrained model.

Note

ViT-Huge-patch14-224 ImageNet21k pretrained model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_huge_patch14_224_in21k = flowvision.models.vit_huge_patch14_224_in21k(pretrained=False, progress=True)

flowvision.models.vit_large_patch16_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Large-patch16-224 model.

Note

ViT-Large-patch16-224 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_large_patch16_224 = flowvision.models.vit_large_patch16_224(pretrained=False, progress=True)

flowvision.models.vit_large_patch16_224_in21k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Large-patch16-224 ImageNet21k pretrained model.

Note

ViT-Large-patch16-224 ImageNet21k pretrained model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_large_patch16_224_in21k = flowvision.models.vit_large_patch16_224_in21k(pretrained=False, progress=True)

flowvision.models.vit_large_patch16_384(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Large-patch16-384 model.

Note

ViT-Large-patch16-384 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 384x384.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_large_patch16_384 = flowvision.models.vit_large_patch16_384(pretrained=False, progress=True)

flowvision.models.vit_large_patch32_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Large-patch32-224 model.

Note

ViT-Large-patch32-224 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_large_patch32_224 = flowvision.models.vit_large_patch32_224(pretrained=False, progress=True)

flowvision.models.vit_large_patch32_224_in21k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Large-patch32-224 ImageNet21k pretrained model.

Note

ViT-Large-patch32-224 ImageNet21k pretrained model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_large_patch32_224_in21k = flowvision.models.vit_large_patch32_224_in21k(pretrained=False, progress=True)

flowvision.models.vit_large_patch32_384(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Large-patch32-384 model.

Note

ViT-Large-patch32-384 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 384x384.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_large_patch32_384 = flowvision.models.vit_large_patch32_384(pretrained=False, progress=True)

flowvision.models.vit_small_patch16_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Small-patch16-224 model.

Note

ViT-Small-patch16-224 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_small_patch16_224 = flowvision.models.vit_small_patch16_224(pretrained=False, progress=True)

flowvision.models.vit_small_patch16_224_in21k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Small-patch16-224 ImageNet21k pretrained model.

Note

ViT-Small-patch16-224 ImageNet21k pretrained model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_small_patch16_224_in21k = flowvision.models.vit_small_patch16_224_in21k(pretrained=False, progress=True)

flowvision.models.vit_small_patch16_384(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Small-patch16-384 model.

Note

ViT-Small-patch16-384 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 384x384.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_small_patch16_384 = flowvision.models.vit_small_patch16_384(pretrained=False, progress=True)

flowvision.models.vit_small_patch32_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Small-patch32-224 model.

Note

ViT-Small-patch32-224 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_small_patch32_224 = flowvision.models.vit_small_patch32_224(pretrained=False, progress=True)

flowvision.models.vit_small_patch32_224_in21k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Small-patch32-224 ImageNet21k pretrained model.

Note

ViT-Small-patch32-224 ImageNet21k pretrained model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_small_patch32_224_in21k = flowvision.models.vit_small_patch32_224_in21k(pretrained=False, progress=True)

flowvision.models.vit_small_patch32_384(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Small-patch32-384 model.

Note

ViT-Small-patch32-384 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 384x384.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_small_patch32_384 = flowvision.models.vit_small_patch32_384(pretrained=False, progress=True)

flowvision.models.vit_tiny_patch16_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Tiny-patch16-224 model.

Note

ViT-Tiny-patch16-224 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_tiny_patch16_224 = flowvision.models.vit_tiny_patch16_224(pretrained=False, progress=True)

flowvision.models.vit_tiny_patch16_224_in21k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Tiny-patch16-224 ImageNet21k pretrained model.

Note

ViT-Tiny-patch16-224 ImageNet21k pretrained model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_tiny_patch16_224_in21k = flowvision.models.vit_tiny_patch16_224_in21k(pretrained=False, progress=True)

flowvision.models.vit_tiny_patch16_384(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ViT-Tiny-patch16-384 model.

Note

ViT-Tiny-patch16-384 model from “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. The required input size of the model is 384x384.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> vit_tiny_patch16_384 = flowvision.models.vit_tiny_patch16_384(pretrained=False, progress=True)

DeiT¶

flowvision.models.deit_base_distilled_patch16_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the DeiT-Base-patch16-224 distilled model.

Note

DeiT-Base-patch16-224 distilled model from “Training data-efficient image transformers & distillation through attention”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> deit_base_distilled_patch16_224 = flowvision.models.deit_base_distilled_patch16_224(pretrained=False, progress=True)

flowvision.models.deit_base_distilled_patch16_384(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the DeiT-Base-patch16-384 distilled model.

Note

DeiT-Base-patch16-384 distilled model from “Training data-efficient image transformers & distillation through attention”. The required input size of the model is 384x384.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> deit_base_distilled_patch16_384 = flowvision.models.deit_base_distilled_patch16_384(pretrained=False, progress=True)

flowvision.models.deit_base_patch16_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the DeiT-Base-patch16-224 model.

Note

DeiT-Base-patch16-224 model from “Training data-efficient image transformers & distillation through attention”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> deit_base_patch16_224 = flowvision.models.deit_base_patch16_224(pretrained=False, progress=True)

flowvision.models.deit_base_patch16_384(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the DeiT-Base-patch16-384 model.

Note

DeiT-Base-patch16-384 model from “Training data-efficient image transformers & distillation through attention”. The required input size of the model is 384x384.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> deit_base_patch16_384 = flowvision.models.deit_base_patch16_384(pretrained=False, progress=True)

flowvision.models.deit_small_distilled_patch16_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the DeiT-Small-patch16-224 distilled model.

Note

DeiT-Small-patch16-224 distilled model from “Training data-efficient image transformers & distillation through attention”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> deit_small_distilled_patch16_224 = flowvision.models.deit_small_distilled_patch16_224(pretrained=False, progress=True)

flowvision.models.deit_small_patch16_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the DeiT-Small-patch16-224 model.

Note

DeiT-Small-patch16-224 model from “Training data-efficient image transformers & distillation through attention”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> deit_small_patch16_224 = flowvision.models.deit_small_patch16_224(pretrained=False, progress=True)

flowvision.models.deit_tiny_distilled_patch16_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the DeiT-Tiny-patch16-224 distilled model.

Note

DeiT-Tiny-patch16-224 distilled model from “Training data-efficient image transformers & distillation through attention”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> deit_tiny_distilled_patch16_224 = flowvision.models.deit_tiny_distilled_patch16_224(pretrained=False, progress=True)

flowvision.models.deit_tiny_patch16_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the DeiT-Tiny-patch16-224 model.

Note

DeiT-Tiny-patch16-224 model from “Training data-efficient image transformers & distillation through attention”. The required input size of the model is 224x224.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> deit_tiny_patch16_224 = flowvision.models.deit_tiny_patch16_224(pretrained=False, progress=True)

PVT¶

flowvision.models.pvt_large(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the PVT-large model.

Note

PVT-large model from “Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> pvt_large = flowvision.models.pvt_large(pretrained=False, progress=True)

flowvision.models.pvt_medium(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the PVT-medium model.

Note

PVT-medium model from “Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> pvt_medium = flowvision.models.pvt_medium(pretrained=False, progress=True)

flowvision.models.pvt_small(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the PVT-small model.

Note

PVT-small model from “Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> pvt_small = flowvision.models.pvt_small(pretrained=False, progress=True)

flowvision.models.pvt_tiny(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the PVT-tiny model.

Note

PVT-tiny model from “Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> pvt_tiny = flowvision.models.pvt_tiny(pretrained=False, progress=True)

Swin-Transformer¶

flowvision.models.swin_base_patch4_window12_384(pretrained=False, progress=True, **kwargs)[source]¶

Constructs Swin-B 384x384 model trained on ImageNet-1k.

Note

Swin-B 384x384 model from “Swin Transformer: Hierarchical Vision Transformer using Shifted Windows”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> swin_base_patch4_window12_384 = flowvision.models.swin_base_patch4_window12_384(pretrained=False, progress=True)

flowvision.models.swin_base_patch4_window12_384_in22k_to_1k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs Swin-B 384x384 model pretrained on ImageNet-22k and fine tuned on ImageNet-1k.

Note

Swin-B 384x384 model from “Swin Transformer: Hierarchical Vision Transformer using Shifted Windows”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> swin_base_patch4_window12_384_in22k_to_1k = flowvision.models.swin_base_patch4_window12_384_in22k_to_1k(pretrained=False, progress=True)

flowvision.models.swin_base_patch4_window7_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs Swin-B 224x224 model trained on ImageNet-1k.

Note

Swin-B 224x224 model from “Swin Transformer: Hierarchical Vision Transformer using Shifted Windows”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> swin_base_patch4_window7_224 = flowvision.models.swin_base_patch4_window7_224(pretrained=False, progress=True)

flowvision.models.swin_base_patch4_window7_224_in22k_to_1k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs Swin-B 224x224 model pretrained on ImageNet-22k and fine tuned on ImageNet-1k.

Note

Swin-B 224x224 model from “Swin Transformer: Hierarchical Vision Transformer using Shifted Windows”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> swin_base_patch4_window7_224_in22k_to_1k = flowvision.models.swin_base_patch4_window7_224_in22k_to_1k(pretrained=False, progress=True)

flowvision.models.swin_large_patch4_window12_384_in22k_to_1k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs Swin-L 384x384 model pretrained on ImageNet-22k and fine tuned on ImageNet-1k.

Note

Swin-L 384x384 model from “Swin Transformer: Hierarchical Vision Transformer using Shifted Windows”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> swin_large_patch4_window12_384_in22k_to_1k = flowvision.models.swin_large_patch4_window12_384_in22k_to_1k(pretrained=False, progress=True)

flowvision.models.swin_large_patch4_window7_224_in22k_to_1k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs Swin-L 224x224 model pretrained on ImageNet-22k and fine tuned on ImageNet-1k.

Note

Swin-L 224x224 model from “Swin Transformer: Hierarchical Vision Transformer using Shifted Windows”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> swin_large_patch4_window7_224_in22k_to_1k = flowvision.models.swin_large_patch4_window7_224_in22k_to_1k(pretrained=False, progress=True)

flowvision.models.swin_small_patch4_window7_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs Swin-S 224x224 model trained on ImageNet-1k.

Note

Swin-S 224x224 model from “Swin Transformer: Hierarchical Vision Transformer using Shifted Windows”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> swin_small_patch4_window7_224 = flowvision.models.swin_small_patch4_window7_224(pretrained=False, progress=True)

flowvision.models.swin_tiny_patch4_window7_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs Swin-T 224x224 model trained on ImageNet-1k.

Note

Swin-T 224x224 model from “Swin Transformer: Hierarchical Vision Transformer using Shifted Windows”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> swin_tiny_patch4_window7_224 = flowvision.models.swin_tiny_patch4_window7_224(pretrained=False, progress=True)

CSwin-Transformer¶

flowvision.models.cswin_base_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs CSwin-B 224x224 model.

Note

CSwin-B 224x224 model from “CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> cswin_base_224 = flowvision.models.cswin_base_224(pretrained=False, progress=True)

flowvision.models.cswin_base_384(pretrained=False, progress=True, **kwargs)[source]¶

Constructs CSwin-B 384x384 model.

Note

CSwin-B 384x384 model from “CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> cswin_base_384 = flowvision.models.cswin_base_384(pretrained=False, progress=True)

flowvision.models.cswin_large_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs CSwin-L 224x224 model.

Note

CSwin-L 224x224 model from “CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> cswin_large_224 = flowvision.models.cswin_large_224(pretrained=False, progress=True)

flowvision.models.cswin_large_384(pretrained=False, progress=True, **kwargs)[source]¶

Constructs CSwin-L 384x384 model.

Note

CSwin-L 384x384 model from “CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> cswin_large_384 = flowvision.models.cswin_large_384(pretrained=False, progress=True)

flowvision.models.cswin_small_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs CSwin-S 224x224 model.

Note

CSwin-S 224x224 model from “CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> cswin_small_224 = flowvision.models.cswin_small_224(pretrained=False, progress=True)

flowvision.models.cswin_tiny_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs CSwin-T 224x224 model.

Note

CSwin-T 224x224 model from “CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> cswin_tiny_224 = flowvision.models.cswin_tiny_224(pretrained=False, progress=True)

CrossFormer¶

flowvision.models.crossformer_base_patch4_group7_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs CrossFormer-B 224x224 model.

Note

CrossFormer-B 224x224 model from “CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> crossformer_base_patch4_group7_224 = flowvision.models.crossformer_base_patch4_group7_224(pretrained=False, progress=True)

flowvision.models.crossformer_large_patch4_group7_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs CrossFormer-L 224x224 model.

Note

CrossFormer-L 224x224 model from “CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> crossformer_large_patch4_group7_224 = flowvision.models.crossformer_large_patch4_group7_224(pretrained=False, progress=True)

flowvision.models.crossformer_small_patch4_group7_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs CrossFormer-S 224x224 model.

Note

CrossFormer-S 224x224 model from “CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> crossformer_small_patch4_group7_224 = flowvision.models.crossformer_small_patch4_group7_224(pretrained=False, progress=True)

flowvision.models.crossformer_tiny_patch4_group7_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs CrossFormer-T 224x224 model.

Note

CrossFormer-T 224x224 model from “CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> crossformer_tiny_patch4_group7_224 = flowvision.models.crossformer_tiny_patch4_group7_224(pretrained=False, progress=True)

Mlp-Mixer¶

flowvision.models.mlp_mixer_b16_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the Mixer-B/16 224x224 model.

Note

Mixer-B/16 224x224 model from “MLP-Mixer: An all-MLP Architecture for Vision”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> mlp_mixer_b16_224 = flowvision.models.mlp_mixer_b16_224(pretrained=False, progress=True)

flowvision.models.mlp_mixer_b16_224_in21k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the Mixer-B/16 224x224 ImageNet21k pretrained model.

Note

Mixer-B/16 224x224 ImageNet21k pretrained model from “MLP-Mixer: An all-MLP Architecture for Vision”. Note that this model is the pretrained model for fine-tune on different datasets.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> mlp_mixer_b16_224_in21k = flowvision.models.mlp_mixer_b16_224_in21k(pretrained=False, progress=True)

flowvision.models.mlp_mixer_b16_224_miil(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the Mixer-B/16 224x224 model with different weights.

Note

Mixer-B/16 224x224 model from “MLP-Mixer: An all-MLP Architecture for Vision”. Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> mlp_mixer_b16_224_miil = flowvision.models.mlp_mixer_b16_224_miil(pretrained=False, progress=True)

flowvision.models.mlp_mixer_b16_224_miil_in21k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the Mixer-B/16 224x224 ImageNet21k pretrained model.

Note

Mixer-B/16 224x224 ImageNet21k pretrained model from “MLP-Mixer: An all-MLP Architecture for Vision”. Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> mlp_mixer_b16_224_miil_in21k = flowvision.models.mlp_mixer_b16_224_miil_in21k(pretrained=False, progress=True)

flowvision.models.mlp_mixer_b32_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the Mixer-B/32 224x224 model.

Note

Mixer-B/32 224x224 model from “MLP-Mixer: An all-MLP Architecture for Vision”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> mlp_mixer_b32_224 = flowvision.models.mlp_mixer_b32_224(pretrained=False, progress=True)

flowvision.models.mlp_mixer_l16_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the Mixer-L/16 224x224 model.

Note

Mixer-L/16 224x224 model from “MLP-Mixer: An all-MLP Architecture for Vision”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> mlp_mixer_l16_224 = flowvision.models.mlp_mixer_l16_224(pretrained=False, progress=True)

flowvision.models.mlp_mixer_l16_224_in21k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the Mixer-L/16 224x224 ImageNet21k pretrained model.

Note

Mixer-L/16 224x224 ImageNet21k pretrained model from “MLP-Mixer: An all-MLP Architecture for Vision”. Note that this model is the pretrained model for fine-tune on different datasets.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> mlp_mixer_l16_224_in21k = flowvision.models.mlp_mixer_l16_224_in21k(pretrained=False, progress=True)

flowvision.models.mlp_mixer_l32_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the Mixer-L/32 224x224 model.

Note

Mixer-L/32 224x224 model from “MLP-Mixer: An all-MLP Architecture for Vision”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> mlp_mixer_l32_224 = flowvision.models.mlp_mixer_l32_224(pretrained=False, progress=True)

flowvision.models.mlp_mixer_s16_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the Mixer-S/16 224x224 model.

Note

Mixer-S/16 224x224 model from “MLP-Mixer: An all-MLP Architecture for Vision”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> mlp_mixer_s16_224 = flowvision.models.mlp_mixer_s16_224(pretrained=False, progress=True)

flowvision.models.mlp_mixer_s32_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the Mixer-S/32 224x224 model.

Note

Mixer-S/32 224x224 model from “MLP-Mixer: An all-MLP Architecture for Vision”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> mlp_mixer_s32_224 = flowvision.models.mlp_mixer_s32_224(pretrained=False, progress=True)

ResMLP¶

flowvision.models.resmlp_12_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ResMLP-12 model.

Note

ResMLP-12 model from “ResMLP: Feedforward networks for image classification with data-efficient training”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resmlp_12_224 = flowvision.models.resmlp_12_224(pretrained=False, progress=True)

flowvision.models.resmlp_12_224_dino(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ResMLP-12 model trained under DINO proposed in “Emerging Properties in Self-Supervised Vision Transformers”.

Note

ResMLP-12 model with distillation from “ResMLP: Feedforward networks for image classification with data-efficient training”. Note that this model is the same as resmlp_12 but the pretrained weight is different.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resmlp_12_224_dino = flowvision.models.resmlp_12_224_dino(pretrained=False, progress=True)

flowvision.models.resmlp_12_distilled_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ResMLP-12 model with distillation.

Note

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resmlp_12_distilled_224 = flowvision.models.resmlp_12_distilled_224(pretrained=False, progress=True)

flowvision.models.resmlp_24_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ResMLP-24 model.

Note

ResMLP-24 model from “ResMLP: Feedforward networks for image classification with data-efficient training”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resmlp_24_224 = flowvision.models.resmlp_24_224(pretrained=False, progress=True)

flowvision.models.resmlp_24_224_dino(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ResMLP-24 model trained under DINO proposed in “Emerging Properties in Self-Supervised Vision Transformers”.

Note

ResMLP-24 model with distillation from “ResMLP: Feedforward networks for image classification with data-efficient training”. Note that this model is the same as resmlp_24 but the pretrained weight is different.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resmlp_24_224_dino = flowvision.models.resmlp_24_224_dino(pretrained=False, progress=True)

flowvision.models.resmlp_24_distilled_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ResMLP-24 model with distillation.

Note

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resmlp_24_distilled_224 = flowvision.models.resmlp_24_distilled_224(pretrained=False, progress=True)

flowvision.models.resmlp_36_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ResMLP-36 model.

Note

ResMLP-36 model from “ResMLP: Feedforward networks for image classification with data-efficient training”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resmlp_36_224 = flowvision.models.resmlp_36_224(pretrained=False, progress=True)

flowvision.models.resmlp_36_distilled_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ResMLP-36 model with distillation.

Note

ResMLP-36 model with distillation from “ResMLP: Feedforward networks for image classification with data-efficient training”. Note that this model is the same as resmlp_36 but the pretrained weight is different.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resmlp_36_distilled_224 = flowvision.models.resmlp_36_distilled_224(pretrained=False, progress=True)

flowvision.models.resmlp_big_24_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ResMLP-Big-24 model.

Note

ResMLP-Big-24 model from “ResMLP: Feedforward networks for image classification with data-efficient training”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resmlp_big_24_224 = flowvision.models.resmlp_big_24_224(pretrained=False, progress=True)

flowvision.models.resmlp_big_24_224_in22k_to_1k(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ImageNet22k pretrained ResMLP-B-24 model.

Note

ImageNet22k pretrained ResMLP-B-24 model from “ResMLP: Feedforward networks for image classification with data-efficient training”. Note that this model is the same as resmlpB_24 but the pretrained weight is different.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resmlp_big_24_224_in22k_to_1k = flowvision.models.resmlp_big_24_224_in22k_to_1k(pretrained=False, progress=True)

flowvision.models.resmlp_big_24_distilled_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the ResMLP-B-24 model with distillation.

Note

ResMLP-B-24 model with distillation from “ResMLP: Feedforward networks for image classification with data-efficient training”. Note that this model is the same as resmlpB_24 but the pretrained weight is different.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> resmlp_big_24_distilled_224 = flowvision.models.resmlp_big_24_distilled_224(pretrained=False, progress=True)

gMLP¶

flowvision.models.gmlp_b16_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the gMLP-base-16 224x224 model.

Note

gMLP-base-16 224x224 model from “Pay Attention to MLPs”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> gmlp_b16_224 = flowvision.models.gmlp_b16_224(pretrained=False, progress=True)

flowvision.models.gmlp_s16_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the gMLP-small-16 224x224 model.

Note

gMLP-small-16 224x224 model from “Pay Attention to MLPs”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> gmlp_s16_224 = flowvision.models.gmlp_s16_224(pretrained=False, progress=True)

flowvision.models.gmlp_ti16_224(pretrained=False, progress=True, **kwargs)[source]¶

Constructs the gMLP-tiny-16 224x224 model.

Note

gMLP-tiny-16 224x224 model from “Pay Attention to MLPs”.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> gmlp_ti16_224 = flowvision.models.gmlp_ti16_224(pretrained=False, progress=True)

ConvMixer¶

flowvision.models.convmixer_1024_20(pretrained: bool = False, progress: bool = True, **kwargs)[source]¶

Constructs the ConvMixer model with 20 depth and 1024 hidden size.

Note

ConvMixer model with 20 depth and 1024 hidden size from the Patched Are All You Need? paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> convmixer_1024_20 = flowvision.models.convmixer_1024_20(pretrained=False, progress=True)

flowvision.models.convmixer_1536_20(pretrained: bool = False, progress: bool = True, **kwargs)[source]¶

Constructs the ConvMixer model with 20 depth and 1536 hidden size.

Note

ConvMixer model with 20 depth and 1536 hidden size from the Patched Are All You Need? paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> convmixer_1536_20 = flowvision.models.convmixer_1536_20(pretrained=False, progress=True)

flowvision.models.convmixer_768_32_relu(pretrained: bool = False, progress: bool = True, **kwargs)[source]¶

Constructs the ConvMixer model with 32 depth and 768 hidden size and ReLU activation layer.

Note

ConvMixer model with 32 depth and 768 hidden size and ReLU activation layer from the Patched Are All You Need? paper.

Parameters

pretrained (bool) – Whether to download the pre-trained model on ImageNet. Default: False
progress (bool) – If True, displays a progress bar of the download to stderr. Default: True

For example:

>>> import flowvision
>>> convmixer_768_32_relu = flowvision.models.convmixer_768_32_relu(pretrained=False, progress=True)

flowvision.models¶

Classification¶

Alexnet¶

SqueezeNet¶

VGG¶

GoogLeNet¶

InceptionV3¶

ResNet¶

DenseNet¶

ShuffleNetV2¶

MobileNetV2¶

MobileNetV3¶

MNASNet¶

GhostNet¶

Res2Net¶

EfficientNet¶

ReXNet¶

ViT¶

DeiT¶

PVT¶

Swin-Transformer¶

CSwin-Transformer¶

CrossFormer¶

Mlp-Mixer¶

ResMLP¶

gMLP¶

ConvMixer¶

Neural Style¶