🔍 Available Encoders

🔍 Available Encoders#

Segmentation Models PyTorch provides support for a wide range of encoders. This flexibility allows you to use these encoders with any model in the library by specifying the encoder name in the encoder_name parameter during model initialization.

Here’s a quick example of using a ResNet34 encoder with the Unet model:

from segmentation_models_pytorch import Unet

# Initialize Unet with ResNet34 encoder pre-trained on ImageNet
model = Unet(encoder_name="resnet34", encoder_weights="imagenet")

The following encoder families are supported by the library, enabling you to choose the one that best fits your use case:

  • Mix Vision Transformer (mit)

  • MobileOne

  • MobileNet

  • EfficientNet

  • ResNet

  • ResNeXt

  • SENet

  • DPN

  • VGG

  • DenseNet

  • Xception

  • Inception

Choosing the Right Encoder#

  1. Small Models for Edge Devices Consider encoders like MobileNet or MobileOne, which have a smaller parameter count and are optimized for lightweight deployment.

  2. High Performance If you require state-of-the-art accuracy Mix Vision Transformer (mit), EfficientNet families offer excellent balance between performance and computational efficiency.

For each encoder, the table below provides detailed information:

  1. Pretrained Weights Specifies the available pretrained weights (e.g., imagenet, imagenet21k).

  2. Params, M: The total number of parameters in the encoder, measured in millions. This metric helps you assess the model’s size and computational requirements.

  3. Script: Indicates whether the encoder can be scripted with torch.jit.script.

  4. Compile: Indicates whether the encoder is compatible with torch.compile(model, fullgraph=True, dynamic=True, backend="eager"). You may still get some issues with another backends, such as inductor, depending on the torch/cuda/… dependencies version, but most of the time it will work.

  5. Export: Indicates whether the encoder can be exported using torch.export.export, making it suitable for deployment in different environments (e.g., ONNX).

Encoder

Pretrained weights

Params, M

Script

Compile

Export

resnet18

imagenet / ssl / swsl

11M

✅

✅

✅

resnet34

imagenet

21M

✅

✅

✅

resnet50

imagenet / ssl / swsl

23M

✅

✅

✅

resnet101

imagenet

42M

✅

✅

✅

resnet152

imagenet

58M

✅

✅

✅

resnext50_32x4d

imagenet / ssl / swsl

22M

✅

✅

✅

resnext101_32x4d

ssl / swsl

42M

✅

✅

✅

resnext101_32x8d

imagenet / instagram / ssl / swsl

86M

✅

✅

✅

resnext101_32x16d

instagram / ssl / swsl

191M

✅

✅

✅

resnext101_32x32d

instagram

466M

✅

✅

✅

resnext101_32x48d

instagram

826M

✅

✅

✅

dpn68

imagenet

11M

❌

✅

✅

dpn68b

imagenet+5k

11M

❌

✅

✅

dpn92

imagenet+5k

34M

❌

✅

✅

dpn98

imagenet

58M

❌

✅

✅

dpn107

imagenet+5k

84M

❌

✅

✅

dpn131

imagenet

76M

❌

✅

✅

vgg11

imagenet

9M

✅

✅

✅

vgg11_bn

imagenet

9M

✅

✅

✅

vgg13

imagenet

9M

✅

✅

✅

vgg13_bn

imagenet

9M

✅

✅

✅

vgg16

imagenet

14M

✅

✅

✅

vgg16_bn

imagenet

14M

✅

✅

✅

vgg19

imagenet

20M

✅

✅

✅

vgg19_bn

imagenet

20M

✅

✅

✅

senet154

imagenet

113M

✅

✅

✅

se_resnet50

imagenet

26M

✅

✅

✅

se_resnet101

imagenet

47M

✅

✅

✅

se_resnet152

imagenet

64M

✅

✅

✅

se_resnext50_32x4d

imagenet

25M

✅

✅

✅

se_resnext101_32x4d

imagenet

46M

✅

✅

✅

densenet121

imagenet

6M

✅

✅

✅

densenet169

imagenet

12M

✅

✅

✅

densenet201

imagenet

18M

✅

✅

✅

densenet161

imagenet

26M

✅

✅

✅

inceptionresnetv2

imagenet / imagenet+background

54M

✅

✅

✅

inceptionv4

imagenet / imagenet+background

41M

✅

✅

✅

efficientnet-b0

imagenet / advprop

4M

✅

✅

✅

efficientnet-b1

imagenet / advprop

6M

✅

✅

✅

efficientnet-b2

imagenet / advprop

7M

✅

✅

✅

efficientnet-b3

imagenet / advprop

10M

✅

✅

✅

efficientnet-b4

imagenet / advprop

17M

✅

✅

✅

efficientnet-b5

imagenet / advprop

28M

✅

✅

✅

efficientnet-b6

imagenet / advprop

40M

✅

✅

✅

efficientnet-b7

imagenet / advprop

63M

✅

✅

✅

mobilenet_v2

imagenet

2M

✅

✅

✅

xception

imagenet

20M

✅

✅

✅

timm-efficientnet-b0

imagenet / advprop / noisy-student

4M

✅

✅

✅

timm-efficientnet-b1

imagenet / advprop / noisy-student

6M

✅

✅

✅

timm-efficientnet-b2

imagenet / advprop / noisy-student

7M

✅

✅

✅

timm-efficientnet-b3

imagenet / advprop / noisy-student

10M

✅

✅

✅

timm-efficientnet-b4

imagenet / advprop / noisy-student

17M

✅

✅

✅

timm-efficientnet-b5

imagenet / advprop / noisy-student

28M

✅

✅

✅

timm-efficientnet-b6

imagenet / advprop / noisy-student

40M

✅

✅

✅

timm-efficientnet-b7

imagenet / advprop / noisy-student

63M

✅

✅

✅

timm-efficientnet-b8

imagenet / advprop

84M

✅

✅

✅

timm-efficientnet-l2

noisy-student / noisy-student-475

474M

✅

✅

✅

timm-tf_efficientnet_lite0

imagenet

3M

✅

✅

✅

timm-tf_efficientnet_lite1

imagenet

4M

✅

✅

✅

timm-tf_efficientnet_lite2

imagenet

4M

✅

✅

✅

timm-tf_efficientnet_lite3

imagenet

6M

✅

✅

✅

timm-tf_efficientnet_lite4

imagenet

11M

✅

✅

✅

timm-skresnet18

imagenet

11M

✅

✅

✅

timm-skresnet34

imagenet

21M

✅

✅

✅

timm-skresnext50_32x4d

imagenet

23M

✅

✅

✅

mit_b0

imagenet

3M

✅

✅

✅

mit_b1

imagenet

13M

✅

✅

✅

mit_b2

imagenet

24M

✅

✅

✅

mit_b3

imagenet

44M

✅

✅

✅

mit_b4

imagenet

60M

✅

✅

✅

mit_b5

imagenet

81M

✅

✅

✅

mobileone_s0

imagenet

4M

✅

✅

✅

mobileone_s1

imagenet

3M

✅

✅

✅

mobileone_s2

imagenet

5M

✅

✅

✅

mobileone_s3

imagenet

8M

✅

✅

✅

mobileone_s4

imagenet

12M

✅

✅

✅
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