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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) → 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) → torchvision.models.vgg.VGG[source]

VGG 11-layer model (configuration “A”) from “Very Deep Convolutional Networks For Large-Scale Image Recognition” <https://arxiv.org/pdf/1409.1556.pdf>._

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) → torchvision.models.vgg.VGG[source]

VGG 11-layer model (configuration “A”) with batch normalization “Very Deep Convolutional Networks For Large-Scale Image Recognition” <https://arxiv.org/pdf/1409.1556.pdf>._

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) → torchvision.models.vgg.VGG[source]

VGG 13-layer model (configuration “B”) “Very Deep Convolutional Networks For Large-Scale Image Recognition” <https://arxiv.org/pdf/1409.1556.pdf>._

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) → torchvision.models.vgg.VGG[source]

VGG 13-layer model (configuration “B”) with batch normalization “Very Deep Convolutional Networks For Large-Scale Image Recognition” <https://arxiv.org/pdf/1409.1556.pdf>._

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) → torchvision.models.vgg.VGG[source]

VGG 16-layer model (configuration “D”) “Very Deep Convolutional Networks For Large-Scale Image Recognition” <https://arxiv.org/pdf/1409.1556.pdf>._

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) → torchvision.models.vgg.VGG[source]

VGG 16-layer model (configuration “D”) with batch normalization “Very Deep Convolutional Networks For Large-Scale Image Recognition” <https://arxiv.org/pdf/1409.1556.pdf>._

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) → torchvision.models.vgg.VGG[source]

VGG 19-layer model (configuration “E”) “Very Deep Convolutional Networks For Large-Scale Image Recognition” <https://arxiv.org/pdf/1409.1556.pdf>._

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) → torchvision.models.vgg.VGG[source]

VGG 19-layer model (configuration ‘E’) with batch normalization “Very Deep Convolutional Networks For Large-Scale Image Recognition” <https://arxiv.org/pdf/1409.1556.pdf>._

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) → 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
torchvision.models.resnet34(pretrained: bool = False, progress: bool = True, **kwargs) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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) → 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.

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
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
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)

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 so that they have a minimum size of 800. This option can be changed by passing the option min_size to the constructor of the 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 keypoint 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
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 V100 GPUs, with CUDA 10.0 and CUDNN 7.4 to report the results. During training, we use a batch size of 2 per GPU, and 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
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:

  • 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 image
  • scores (Tensor[N]): the scores or each prediction

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.
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:

  • 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 image
  • scores (Tensor[N]): the scores or each prediction

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.

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:

  • 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 image
  • scores (Tensor[N]): the scores or each prediction
  • 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)

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.

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:

  • 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 image
  • scores (Tensor[N]): the scores or each prediction
  • keypoints (FloatTensor[N, K, 3]): the locations of the predicted keypoints, in [x, y, v] format.

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.

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

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