torchvision.models¶

The models subpackage contains definitions of models for addressing different tasks, including: image classification, pixelwise semantic segmentation, object detection, instance segmentation, person keypoint detection and video classification.

Classification¶

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

You can construct a model with random weights by calling its constructor:

import torchvision.models as models
resnet18 = models.resnet18()
alexnet = models.alexnet()
vgg16 = models.vgg16()
squeezenet = models.squeezenet1_0()
densenet = models.densenet161()
inception = models.inception_v3()
googlenet = models.googlenet()
shufflenet = models.shufflenet_v2_x1_0()
mobilenet_v2 = models.mobilenet_v2()
mobilenet_v3_large = models.mobilenet_v3_large()
mobilenet_v3_small = models.mobilenet_v3_small()
resnext50_32x4d = models.resnext50_32x4d()
wide_resnet50_2 = models.wide_resnet50_2()
mnasnet = models.mnasnet1_0()

We provide pre-trained models, using the PyTorch torch.utils.model_zoo. These can be constructed by passing pretrained=True:

import torchvision.models as models
resnet18 = models.resnet18(pretrained=True)
alexnet = models.alexnet(pretrained=True)
squeezenet = models.squeezenet1_0(pretrained=True)
vgg16 = models.vgg16(pretrained=True)
densenet = models.densenet161(pretrained=True)
inception = models.inception_v3(pretrained=True)
googlenet = models.googlenet(pretrained=True)
shufflenet = models.shufflenet_v2_x1_0(pretrained=True)
mobilenet_v2 = models.mobilenet_v2(pretrained=True)
mobilenet_v3_large = models.mobilenet_v3_large(pretrained=True)
mobilenet_v3_small = models.mobilenet_v3_small(pretrained=True)
resnext50_32x4d = models.resnext50_32x4d(pretrained=True)
wide_resnet50_2 = models.wide_resnet50_2(pretrained=True)
mnasnet = models.mnasnet1_0(pretrained=True)

Instancing a pre-trained model will download its weights to a cache directory. This directory can be set using the TORCH_MODEL_ZOO environment variable. See torch.utils.model_zoo.load_url() for details.

Some models use modules which have different training and evaluation behavior, such as batch normalization. To switch between these modes, use model.train() or model.eval() as appropriate. See train() or eval() for details.

All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. You can use the following transform to normalize:

normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])

An example of such normalization can be found in the imagenet example here

The process for obtaining the values of mean and std is roughly equivalent to:

import torch
from torchvision import datasets, transforms as T

transform = T.Compose([T.Resize(256), T.CenterCrop(224), T.ToTensor()])
dataset = datasets.ImageNet(".", split="train", transform=transform)

means = []
stds = []
for img in subset(dataset):
    means.append(torch.mean(img))
    stds.append(torch.std(img))

mean = torch.mean(torch.tensor(means))
std = torch.mean(torch.tensor(stds))

Unfortunately, the concrete subset that was used is lost. For more information see this discussion or these experiments.

ImageNet 1-crop error rates (224x224)

Model	Acc@1	Acc@5
AlexNet	56.522	79.066
VGG-11	69.020	88.628
VGG-13	69.928	89.246
VGG-16	71.592	90.382
VGG-19	72.376	90.876
VGG-11 with batch normalization	70.370	89.810
VGG-13 with batch normalization	71.586	90.374
VGG-16 with batch normalization	73.360	91.516
VGG-19 with batch normalization	74.218	91.842
ResNet-18	69.758	89.078
ResNet-34	73.314	91.420
ResNet-50	76.130	92.862
ResNet-101	77.374	93.546
ResNet-152	78.312	94.046
SqueezeNet 1.0	58.092	80.420
SqueezeNet 1.1	58.178	80.624
Densenet-121	74.434	91.972
Densenet-169	75.600	92.806
Densenet-201	76.896	93.370
Densenet-161	77.138	93.560
Inception v3	77.294	93.450
GoogleNet	69.778	89.530
ShuffleNet V2 x1.0	69.362	88.316
ShuffleNet V2 x0.5	60.552	81.746
MobileNet V2	71.878	90.286
MobileNet V3 Large	74.042	91.340
MobileNet V3 Small	67.668	87.402
ResNeXt-50-32x4d	77.618	93.698
ResNeXt-101-32x8d	79.312	94.526
Wide ResNet-50-2	78.468	94.086
Wide ResNet-101-2	78.848	94.284
MNASNet 1.0	73.456	91.510
MNASNet 0.5	67.734	87.490

Alexnet¶

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

AlexNet model architecture from the “One weird trick…” paper.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

VGG¶

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

VGG 11-layer model (configuration “A”) from “Very Deep Convolutional Networks For Large-Scale Image Recognition”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

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

VGG 11-layer model (configuration “A”) with batch normalization “Very Deep Convolutional Networks For Large-Scale Image Recognition”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

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

VGG 13-layer model (configuration “B”) “Very Deep Convolutional Networks For Large-Scale Image Recognition”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

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

VGG 13-layer model (configuration “B”) with batch normalization “Very Deep Convolutional Networks For Large-Scale Image Recognition”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

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

VGG 16-layer model (configuration “D”) “Very Deep Convolutional Networks For Large-Scale Image Recognition”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

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

VGG 16-layer model (configuration “D”) with batch normalization “Very Deep Convolutional Networks For Large-Scale Image Recognition”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

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

VGG 19-layer model (configuration “E”) “Very Deep Convolutional Networks For Large-Scale Image Recognition”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

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

VGG 19-layer model (configuration ‘E’) with batch normalization “Very Deep Convolutional Networks For Large-Scale Image Recognition”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

ResNet¶

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

ResNet-18 model from “Deep Residual Learning for Image Recognition”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

Examples using resnet18:

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

ResNet-34 model from “Deep Residual Learning for Image Recognition”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

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

ResNet-50 model from “Deep Residual Learning for Image Recognition”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

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

ResNet-101 model from “Deep Residual Learning for Image Recognition”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

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

ResNet-152 model from “Deep Residual Learning for Image Recognition”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

SqueezeNet¶

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

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

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

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

SqueezeNet 1.1 model from the official SqueezeNet repo. SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

DenseNet¶

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

Densenet-121 model from “Densely Connected Convolutional Networks”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr
memory_efficient (bool) – but slower. Default: False. See “paper”.

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

Densenet-169 model from “Densely Connected Convolutional Networks”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr
memory_efficient (bool) –
but slower. Default: False. See “paper”.

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

Densenet-161 model from “Densely Connected Convolutional Networks”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr
memory_efficient (bool) –
but slower. Default: False. See “paper”.

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

Densenet-201 model from “Densely Connected Convolutional Networks”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr
memory_efficient (bool) –
but slower. Default: False. See “paper”.

Inception v3¶

torchvision.models.inception_v3(pretrained: bool = False, progress: bool = True, **kwargs: Any) → torchvision.models.inception.Inception3[source]¶

Inception v3 model architecture from “Rethinking the Inception Architecture for Computer Vision”.

Note

Important: 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) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr
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

Note

This requires scipy to be installed

GoogLeNet¶

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

GoogLeNet (Inception v1) model architecture from “Going Deeper with Convolutions”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr
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

Note

This requires scipy to be installed

ShuffleNet v2¶

torchvision.models.shufflenet_v2_x0_5(pretrained: bool = False, progress: bool = True, **kwargs: Any) → torchvision.models.shufflenetv2.ShuffleNetV2[source]¶

Constructs a ShuffleNetV2 with 0.5x output channels, as described in “ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

torchvision.models.shufflenet_v2_x1_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) → torchvision.models.shufflenetv2.ShuffleNetV2[source]¶

Constructs a ShuffleNetV2 with 1.0x output channels, as described in “ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

torchvision.models.shufflenet_v2_x1_5(pretrained: bool = False, progress: bool = True, **kwargs: Any) → torchvision.models.shufflenetv2.ShuffleNetV2[source]¶

Constructs a ShuffleNetV2 with 1.5x output channels, as described in “ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

torchvision.models.shufflenet_v2_x2_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) → torchvision.models.shufflenetv2.ShuffleNetV2[source]¶

Constructs a ShuffleNetV2 with 2.0x output channels, as described in “ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

MobileNet v2¶

torchvision.models.mobilenet_v2(pretrained: bool = False, progress: bool = True, **kwargs: Any) → torchvision.models.mobilenetv2.MobileNetV2[source]¶

Constructs a MobileNetV2 architecture from “MobileNetV2: Inverted Residuals and Linear Bottlenecks”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

MobileNet v3¶

torchvision.models.mobilenet_v3_large(pretrained: bool = False, progress: bool = True, **kwargs: Any) → torchvision.models.mobilenetv3.MobileNetV3[source]¶

Constructs a large MobileNetV3 architecture from “Searching for MobileNetV3”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

torchvision.models.mobilenet_v3_small(pretrained: bool = False, progress: bool = True, **kwargs: Any) → torchvision.models.mobilenetv3.MobileNetV3[source]¶

Constructs a small MobileNetV3 architecture from “Searching for MobileNetV3”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

ResNext¶

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

ResNeXt-50 32x4d model from “Aggregated Residual Transformation for Deep Neural Networks”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

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

ResNeXt-101 32x8d model from “Aggregated Residual Transformation for Deep Neural Networks”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

Wide ResNet¶

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

Wide ResNet-50-2 model from “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) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

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

Wide ResNet-101-2 model from “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) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

MNASNet¶

torchvision.models.mnasnet0_5(pretrained: bool = False, progress: bool = True, **kwargs: Any) → torchvision.models.mnasnet.MNASNet[source]¶

MNASNet with depth multiplier of 0.5 from “MnasNet: Platform-Aware Neural Architecture Search for Mobile”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

torchvision.models.mnasnet0_75(pretrained: bool = False, progress: bool = True, **kwargs: Any) → torchvision.models.mnasnet.MNASNet[source]¶

MNASNet with depth multiplier of 0.75 from “MnasNet: Platform-Aware Neural Architecture Search for Mobile”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

torchvision.models.mnasnet1_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) → torchvision.models.mnasnet.MNASNet[source]¶

MNASNet with depth multiplier of 1.0 from “MnasNet: Platform-Aware Neural Architecture Search for Mobile”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

torchvision.models.mnasnet1_3(pretrained: bool = False, progress: bool = True, **kwargs: Any) → torchvision.models.mnasnet.MNASNet[source]¶

MNASNet with depth multiplier of 1.3 from “MnasNet: Platform-Aware Neural Architecture Search for Mobile”.

Parameters

pretrained (bool) – If True, returns a model pre-trained on ImageNet
progress (bool) – If True, displays a progress bar of the download to stderr

Quantized Models¶

The following architectures provide support for INT8 quantized models. You can get a model with random weights by calling its constructor:

import torchvision.models as models
googlenet = models.quantization.googlenet()
inception_v3 = models.quantization.inception_v3()
mobilenet_v2 = models.quantization.mobilenet_v2()
mobilenet_v3_large = models.quantization.mobilenet_v3_large()
resnet18 = models.quantization.resnet18()
resnet50 = models.quantization.resnet50()
resnext101_32x8d = models.quantization.resnext101_32x8d()
shufflenet_v2_x0_5 = models.quantization.shufflenet_v2_x0_5()
shufflenet_v2_x1_0 = models.quantization.shufflenet_v2_x1_0()
shufflenet_v2_x1_5 = models.quantization.shufflenet_v2_x1_5()
shufflenet_v2_x2_0 = models.quantization.shufflenet_v2_x2_0()

Obtaining a pre-trained quantized model can be done with a few lines of code:

import torchvision.models as models
model = models.quantization.mobilenet_v2(pretrained=True, quantize=True)
model.eval()
# run the model with quantized inputs and weights
out = model(torch.rand(1, 3, 224, 224))

We provide pre-trained quantized weights for the following models:

Model	Acc@1	Acc@5
MobileNet V2	71.658	90.150
MobileNet V3 Large	73.004	90.858
ShuffleNet V2	68.360	87.582
ResNet 18	69.494	88.882
ResNet 50	75.920	92.814
ResNext 101 32x8d	78.986	94.480
Inception V3	77.176	93.354
GoogleNet	69.826	89.404

Semantic Segmentation¶

The models subpackage contains definitions for the following model architectures for semantic segmentation:

As with image classification models, all pre-trained models expect input images normalized in the same way. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. They have been trained on images resized such that their minimum size is 520.

For details on how to plot the masks of such models, you may refer to Semantic segmentation models.

The pre-trained models have been trained on a subset of COCO train2017, on the 20 categories that are present in the Pascal VOC dataset. You can see more information on how the subset has been selected in references/segmentation/coco_utils.py. The classes that the pre-trained model outputs are the following, in order:

['__background__', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']

The accuracies of the pre-trained models evaluated on COCO val2017 are as follows

Network	mean IoU	global pixelwise acc
FCN ResNet50	60.5	91.4
FCN ResNet101	63.7	91.9
DeepLabV3 ResNet50	66.4	92.4
DeepLabV3 ResNet101	67.4	92.4
DeepLabV3 MobileNetV3-Large	60.3	91.2
LR-ASPP MobileNetV3-Large	57.9	91.2

Fully Convolutional Networks¶

torchvision.models.segmentation.fcn_resnet50(pretrained=False, progress=True, num_classes=21, aux_loss=None, **kwargs)[source]¶

Constructs a Fully-Convolutional Network model with a ResNet-50 backbone.

Parameters

pretrained (bool) – If True, returns a model pre-trained on COCO train2017 which contains the same classes as Pascal VOC
progress (bool) – If True, displays a progress bar of the download to stderr
num_classes (int) – number of output classes of the model (including the background)
aux_loss (bool) – If True, it uses an auxiliary loss

Examples using fcn_resnet50:

torchvision.models.segmentation.fcn_resnet101(pretrained=False, progress=True, num_classes=21, aux_loss=None, **kwargs)[source]¶

Constructs a Fully-Convolutional Network model with a ResNet-101 backbone.

Parameters

pretrained (bool) – If True, returns a model pre-trained on COCO train2017 which contains the same classes as Pascal VOC
progress (bool) – If True, displays a progress bar of the download to stderr
num_classes (int) – number of output classes of the model (including the background)
aux_loss (bool) – If True, it uses an auxiliary loss

DeepLabV3¶

torchvision.models.segmentation.deeplabv3_resnet50(pretrained=False, progress=True, num_classes=21, aux_loss=None, **kwargs)[source]¶

Constructs a DeepLabV3 model with a ResNet-50 backbone.

Parameters

pretrained (bool) – If True, returns a model pre-trained on COCO train2017 which contains the same classes as Pascal VOC
progress (bool) – If True, displays a progress bar of the download to stderr
num_classes (int) – number of output classes of the model (including the background)
aux_loss (bool) – If True, it uses an auxiliary loss

Examples using deeplabv3_resnet50:

torchvision.models.segmentation.deeplabv3_resnet101(pretrained=False, progress=True, num_classes=21, aux_loss=None, **kwargs)[source]¶

Constructs a DeepLabV3 model with a ResNet-101 backbone.

Parameters

pretrained (bool) – If True, returns a model pre-trained on COCO train2017 which contains the same classes as Pascal VOC
progress (bool) – If True, displays a progress bar of the download to stderr
num_classes (int) – The number of classes
aux_loss (bool) – If True, include an auxiliary classifier

torchvision.models.segmentation.deeplabv3_mobilenet_v3_large(pretrained=False, progress=True, num_classes=21, aux_loss=None, **kwargs)[source]¶

Constructs a DeepLabV3 model with a MobileNetV3-Large backbone.

Parameters

pretrained (bool) – If True, returns a model pre-trained on COCO train2017 which contains the same classes as Pascal VOC
progress (bool) – If True, displays a progress bar of the download to stderr
num_classes (int) – number of output classes of the model (including the background)
aux_loss (bool) – If True, it uses an auxiliary loss

LR-ASPP¶

torchvision.models.segmentation.lraspp_mobilenet_v3_large(pretrained=False, progress=True, num_classes=21, **kwargs)[source]¶

Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone.

Parameters

pretrained (bool) – If True, returns a model pre-trained on COCO train2017 which contains the same classes as Pascal VOC
progress (bool) – If True, displays a progress bar of the download to stderr
num_classes (int) – number of output classes of the model (including the background)

Examples using lraspp_mobilenet_v3_large:

Object Detection, Instance Segmentation and Person Keypoint Detection¶

The models subpackage contains definitions for the following model architectures for detection:

The pre-trained models for detection, instance segmentation and keypoint detection are initialized with the classification models in torchvision.

The models expect a list of Tensor[C, H, W], in the range 0-1. The models internally resize the images but the behaviour varies depending on the model. Check the constructor of the models for more information. The output format of such models is illustrated in Instance segmentation models.

For object detection and instance segmentation, the pre-trained models return the predictions of the following classes:

COCO_INSTANCE_CATEGORY_NAMES = [
    '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign',
    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
    'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A',
    'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
    'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
    'bottle', 'N/A', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
    'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
    'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table',
    'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A', 'book',
    'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]

Here are the summary of the accuracies for the models trained on the instances set of COCO train2017 and evaluated on COCO val2017.

Network	box AP	mask AP
Faster R-CNN ResNet-50 FPN	37.0
Faster R-CNN MobileNetV3-Large FPN	32.8
Faster R-CNN MobileNetV3-Large 320 FPN	22.8
RetinaNet ResNet-50 FPN	36.4
SSD300 VGG16	25.1
SSDlite320 MobileNetV3-Large	21.3
Mask R-CNN ResNet-50 FPN	37.9	34.6

For person keypoint detection, the accuracies for the pre-trained models are as follows

Network	box AP	mask AP	keypoint AP
Keypoint R-CNN ResNet-50 FPN	54.6		65.0

For person keypoint detection, the pre-trained model return the keypoints in the following order:

COCO_PERSON_KEYPOINT_NAMES = [
    'nose',
    'left_eye',
    'right_eye',
    'left_ear',
    'right_ear',
    'left_shoulder',
    'right_shoulder',
    'left_elbow',
    'right_elbow',
    'left_wrist',
    'right_wrist',
    'left_hip',
    'right_hip',
    'left_knee',
    'right_knee',
    'left_ankle',
    'right_ankle'
]

Runtime characteristics¶

The implementations of the models for object detection, instance segmentation and keypoint detection are efficient.

In the following table, we use 8 GPUs to report the results. During training, we use a batch size of 2 per GPU for all models except SSD which uses 4 and SSDlite which uses 24. During testing a batch size of 1 is used.

For test time, we report the time for the model evaluation and postprocessing (including mask pasting in image), but not the time for computing the precision-recall.

Network	train time (s / it)	test time (s / it)	memory (GB)
Faster R-CNN ResNet-50 FPN	0.2288	0.0590	5.2
Faster R-CNN MobileNetV3-Large FPN	0.1020	0.0415	1.0
Faster R-CNN MobileNetV3-Large 320 FPN	0.0978	0.0376	0.6
RetinaNet ResNet-50 FPN	0.2514	0.0939	4.1
SSD300 VGG16	0.2093	0.0744	1.5
SSDlite320 MobileNetV3-Large	0.1773	0.0906	1.5
Mask R-CNN ResNet-50 FPN	0.2728	0.0903	5.4
Keypoint R-CNN ResNet-50 FPN	0.3789	0.1242	6.8

Faster R-CNN¶

torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True, trainable_backbone_layers=None, **kwargs)[source]¶

Constructs a Faster R-CNN model with a ResNet-50-FPN backbone.

The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each image, and should be in 0-1 range. Different images can have different sizes.

The behavior of the model changes depending if it is in training or evaluation mode.

During training, the model expects both the input tensors, as well as a targets (list of dictionary), containing:

boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.

labels (Int64Tensor[N]): the class label for each ground-truth box

The model returns a Dict[Tensor] during training, containing the classification and regression losses for both the RPN and the R-CNN.

During inference, the model requires only the input tensors, and returns the post-processed predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as follows, where N is the number of detections:

boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.

labels (Int64Tensor[N]): the predicted labels for each detection

scores (Tensor[N]): the scores of each detection

For more details on the output, you may refer to Instance segmentation models.

Faster R-CNN is exportable to ONNX for a fixed batch size with inputs images of fixed size.

Example:

>>> model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
>>> # For training
>>> images, boxes = torch.rand(4, 3, 600, 1200), torch.rand(4, 11, 4)
>>> labels = torch.randint(1, 91, (4, 11))
>>> images = list(image for image in images)
>>> targets = []
>>> for i in range(len(images)):
>>>     d = {}
>>>     d['boxes'] = boxes[i]
>>>     d['labels'] = labels[i]
>>>     targets.append(d)
>>> output = model(images, targets)
>>> # For inference
>>> model.eval()
>>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
>>> predictions = model(x)
>>>
>>> # optionally, if you want to export the model to ONNX:
>>> torch.onnx.export(model, x, "faster_rcnn.onnx", opset_version = 11)

Parameters

pretrained (bool) – If True, returns a model pre-trained on COCO train2017
progress (bool) – If True, displays a progress bar of the download to stderr
num_classes (int) – number of output classes of the model (including the background)
pretrained_backbone (bool) – If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int) – number of trainable (not frozen) resnet layers starting from final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.

Examples using fasterrcnn_resnet50_fpn:

torchvision.models.detection.fasterrcnn_mobilenet_v3_large_fpn(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True, trainable_backbone_layers=None, **kwargs)[source]¶

Constructs a high resolution Faster R-CNN model with a MobileNetV3-Large FPN backbone. It works similarly to Faster R-CNN with ResNet-50 FPN backbone. See fasterrcnn_resnet50_fpn() for more details.

Example:

>>> model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_fpn(pretrained=True)
>>> model.eval()
>>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
>>> predictions = model(x)

Parameters

pretrained (bool) – If True, returns a model pre-trained on COCO train2017
progress (bool) – If True, displays a progress bar of the download to stderr
num_classes (int) – number of output classes of the model (including the background)
pretrained_backbone (bool) – If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int) – number of trainable (not frozen) resnet layers starting from final block. Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable.

torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True, trainable_backbone_layers=None, **kwargs)[source]¶

Constructs a low resolution Faster R-CNN model with a MobileNetV3-Large FPN backbone tunned for mobile use-cases. It works similarly to Faster R-CNN with ResNet-50 FPN backbone. See fasterrcnn_resnet50_fpn() for more details.

Example:

>>> model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn(pretrained=True)
>>> model.eval()
>>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
>>> predictions = model(x)

Parameters

pretrained (bool) – If True, returns a model pre-trained on COCO train2017
progress (bool) – If True, displays a progress bar of the download to stderr
num_classes (int) – number of output classes of the model (including the background)
pretrained_backbone (bool) – If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int) – number of trainable (not frozen) resnet layers starting from final block. Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable.

RetinaNet¶

torchvision.models.detection.retinanet_resnet50_fpn(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True, trainable_backbone_layers=None, **kwargs)[source]¶

Constructs a RetinaNet model with a ResNet-50-FPN backbone.

The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each image, and should be in 0-1 range. Different images can have different sizes.

The behavior of the model changes depending if it is in training or evaluation mode.

During training, the model expects both the input tensors, as well as a targets (list of dictionary), containing:

boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.

labels (Int64Tensor[N]): the class label for each ground-truth box

The model returns a Dict[Tensor] during training, containing the classification and regression losses.

During inference, the model requires only the input tensors, and returns the post-processed predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as follows, where N is the number of detections:

boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.

labels (Int64Tensor[N]): the predicted labels for each detection

scores (Tensor[N]): the scores of each detection

For more details on the output, you may refer to Instance segmentation models.

Example:

>>> model = torchvision.models.detection.retinanet_resnet50_fpn(pretrained=True)
>>> model.eval()
>>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
>>> predictions = model(x)

Parameters

pretrained (bool) – If True, returns a model pre-trained on COCO train2017
progress (bool) – If True, displays a progress bar of the download to stderr
num_classes (int) – number of output classes of the model (including the background)
pretrained_backbone (bool) – If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int) – number of trainable (not frozen) resnet layers starting from final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.

Examples using retinanet_resnet50_fpn:

SSD¶

torchvision.models.detection.ssd300_vgg16(pretrained: bool = False, progress: bool = True, num_classes: int = 91, pretrained_backbone: bool = True, trainable_backbone_layers: Optional[int] = None, **kwargs: Any)[source]¶

Constructs an SSD model with input size 300x300 and a VGG16 backbone.

Reference: “SSD: Single Shot MultiBox Detector”.

The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each image, and should be in 0-1 range. Different images can have different sizes but they will be resized to a fixed size before passing it to the backbone.

The behavior of the model changes depending if it is in training or evaluation mode.

During training, the model expects both the input tensors, as well as a targets (list of dictionary), containing:

boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.

labels (Int64Tensor[N]): the class label for each ground-truth box

The model returns a Dict[Tensor] during training, containing the classification and regression losses.

During inference, the model requires only the input tensors, and returns the post-processed predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as follows, where N is the number of detections:

boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.

labels (Int64Tensor[N]): the predicted labels for each detection

scores (Tensor[N]): the scores for each detection

Example

>>> model = torchvision.models.detection.ssd300_vgg16(pretrained=True)
>>> model.eval()
>>> x = [torch.rand(3, 300, 300), torch.rand(3, 500, 400)]
>>> predictions = model(x)

Parameters

pretrained (bool) – If True, returns a model pre-trained on COCO train2017
progress (bool) – If True, displays a progress bar of the download to stderr
num_classes (int) – number of output classes of the model (including the background)
pretrained_backbone (bool) – If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int) – number of trainable (not frozen) resnet layers starting from final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.

Examples using ssd300_vgg16:

SSDlite¶

torchvision.models.detection.ssdlite320_mobilenet_v3_large(pretrained: bool = False, progress: bool = True, num_classes: int = 91, pretrained_backbone: bool = False, trainable_backbone_layers: Optional[int] = None, norm_layer: Optional[Callable[[...], torch.nn.modules.module.Module]] = None, **kwargs: Any)[source]¶

Constructs an SSDlite model with input size 320x320 and a MobileNetV3 Large backbone, as described at “Searching for MobileNetV3” and “MobileNetV2: Inverted Residuals and Linear Bottlenecks”.

See ssd300_vgg16() for more details.

Example

>>> model = torchvision.models.detection.ssdlite320_mobilenet_v3_large(pretrained=True)
>>> model.eval()
>>> x = [torch.rand(3, 320, 320), torch.rand(3, 500, 400)]
>>> predictions = model(x)

Parameters

pretrained (bool) – If True, returns a model pre-trained on COCO train2017
progress (bool) – If True, displays a progress bar of the download to stderr
num_classes (int) – number of output classes of the model (including the background)
pretrained_backbone (bool) – If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int) – number of trainable (not frozen) resnet layers starting from final block. Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable.
norm_layer (callable, optional) – Module specifying the normalization layer to use.

Examples using ssdlite320_mobilenet_v3_large:

Mask R-CNN¶

torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True, trainable_backbone_layers=None, **kwargs)[source]¶

Constructs a Mask R-CNN model with a ResNet-50-FPN backbone.

The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each image, and should be in 0-1 range. Different images can have different sizes.

The behavior of the model changes depending if it is in training or evaluation mode.

During training, the model expects both the input tensors, as well as a targets (list of dictionary), containing:

boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.

labels (Int64Tensor[N]): the class label for each ground-truth box

masks (UInt8Tensor[N, H, W]): the segmentation binary masks for each instance

The model returns a Dict[Tensor] during training, containing the classification and regression losses for both the RPN and the R-CNN, and the mask loss.

During inference, the model requires only the input tensors, and returns the post-processed predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as follows, where N is the number of detected instances:

boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.

labels (Int64Tensor[N]): the predicted labels for each instance

scores (Tensor[N]): the scores or each instance

masks (UInt8Tensor[N, 1, H, W]): the predicted masks for each instance, in 0-1 range. In order to obtain the final segmentation masks, the soft masks can be thresholded, generally with a value of 0.5 (mask >= 0.5)

For more details on the output and on how to plot the masks, you may refer to Instance segmentation models.

Mask R-CNN is exportable to ONNX for a fixed batch size with inputs images of fixed size.

Example:

>>> model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)
>>> model.eval()
>>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
>>> predictions = model(x)
>>>
>>> # optionally, if you want to export the model to ONNX:
>>> torch.onnx.export(model, x, "mask_rcnn.onnx", opset_version = 11)

Parameters

pretrained (bool) – If True, returns a model pre-trained on COCO train2017
progress (bool) – If True, displays a progress bar of the download to stderr
num_classes (int) – number of output classes of the model (including the background)
pretrained_backbone (bool) – If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int) – number of trainable (not frozen) resnet layers starting from final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.

Examples using maskrcnn_resnet50_fpn:

Keypoint R-CNN¶

torchvision.models.detection.keypointrcnn_resnet50_fpn(pretrained=False, progress=True, num_classes=2, num_keypoints=17, pretrained_backbone=True, trainable_backbone_layers=None, **kwargs)[source]¶

Constructs a Keypoint R-CNN model with a ResNet-50-FPN backbone.

The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each image, and should be in 0-1 range. Different images can have different sizes.

The behavior of the model changes depending if it is in training or evaluation mode.

During training, the model expects both the input tensors, as well as a targets (list of dictionary), containing:

boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.

labels (Int64Tensor[N]): the class label for each ground-truth box

keypoints (FloatTensor[N, K, 3]): the K keypoints location for each of the N instances, in the format [x, y, visibility], where visibility=0 means that the keypoint is not visible.

The model returns a Dict[Tensor] during training, containing the classification and regression losses for both the RPN and the R-CNN, and the keypoint loss.

During inference, the model requires only the input tensors, and returns the post-processed predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as follows, where N is the number of detected instances:

boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.

labels (Int64Tensor[N]): the predicted labels for each instance

scores (Tensor[N]): the scores or each instance

keypoints (FloatTensor[N, K, 3]): the locations of the predicted keypoints, in [x, y, v] format.

For more details on the output, you may refer to Instance segmentation models.

Keypoint R-CNN is exportable to ONNX for a fixed batch size with inputs images of fixed size.

Example:

>>> model = torchvision.models.detection.keypointrcnn_resnet50_fpn(pretrained=True)
>>> model.eval()
>>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
>>> predictions = model(x)
>>>
>>> # optionally, if you want to export the model to ONNX:
>>> torch.onnx.export(model, x, "keypoint_rcnn.onnx", opset_version = 11)

Parameters

pretrained (bool) – If True, returns a model pre-trained on COCO train2017
progress (bool) – If True, displays a progress bar of the download to stderr
num_classes (int) – number of output classes of the model (including the background)
num_keypoints (int) – number of keypoints, default 17
pretrained_backbone (bool) – If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int) – number of trainable (not frozen) resnet layers starting from final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.

Examples using keypointrcnn_resnet50_fpn:

Video classification¶

We provide models for action recognition pre-trained on Kinetics-400. They have all been trained with the scripts provided in references/video_classification.

All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB videos of shape (3 x T x H x W), where H and W are expected to be 112, and T is a number of video frames in a clip. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.43216, 0.394666, 0.37645] and std = [0.22803, 0.22145, 0.216989].

Note

The normalization parameters are different from the image classification ones, and correspond to the mean and std from Kinetics-400.

Note

For now, normalization code can be found in references/video_classification/transforms.py, see the Normalize function there. Note that it differs from standard normalization for images because it assumes the video is 4d.

Kinetics 1-crop accuracies for clip length 16 (16x112x112)

Network	Clip acc@1	Clip acc@5
ResNet 3D 18	52.75	75.45
ResNet MC 18	53.90	76.29
ResNet (2+1)D	57.50	78.81

ResNet 3D¶

torchvision.models.video.r3d_18(pretrained=False, progress=True, **kwargs)[source]¶

Construct 18 layer Resnet3D model as in https://arxiv.org/abs/1711.11248

Parameters

pretrained (bool) – If True, returns a model pre-trained on Kinetics-400
progress (bool) – If True, displays a progress bar of the download to stderr

Returns

R3D-18 network

Return type

nn.Module

ResNet Mixed Convolution¶

torchvision.models.video.mc3_18(pretrained=False, progress=True, **kwargs)[source]¶

Constructor for 18 layer Mixed Convolution network as in https://arxiv.org/abs/1711.11248

Parameters

pretrained (bool) – If True, returns a model pre-trained on Kinetics-400
progress (bool) – If True, displays a progress bar of the download to stderr

Returns

MC3 Network definition

Return type

nn.Module

ResNet (2+1)D¶

torchvision.models.video.r2plus1d_18(pretrained=False, progress=True, **kwargs)[source]¶

Constructor for the 18 layer deep R(2+1)D network as in https://arxiv.org/abs/1711.11248

Parameters

pretrained (bool) – If True, returns a model pre-trained on Kinetics-400
progress (bool) – If True, displays a progress bar of the download to stderr

Returns

R(2+1)D-18 network

Return type

nn.Module

torchvision.models¶

Classification¶

Alexnet¶

VGG¶

ResNet¶

SqueezeNet¶

DenseNet¶

Inception v3¶

GoogLeNet¶

ShuffleNet v2¶

MobileNet v2¶

MobileNet v3¶

ResNext¶

Wide ResNet¶

MNASNet¶

Quantized Models¶

Semantic Segmentation¶

Fully Convolutional Networks¶

DeepLabV3¶

LR-ASPP¶

Object Detection, Instance Segmentation and Person Keypoint Detection¶

Runtime characteristics¶

Faster R-CNN¶

RetinaNet¶

SSD¶

SSDlite¶

Mask R-CNN¶

Keypoint R-CNN¶

Video classification¶

ResNet 3D¶

ResNet Mixed Convolution¶

ResNet (2+1)D¶

Docs

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