gluonfr.model_zoo

gluonfr.model_zoo.get_model	Returns a model by name.
gluonfr.model_zoo.get_model_list	Get the entire list of model names in model_zoo.

Hint

This is the recommended method for getting a pre-defined model.

API Reference

Models for face recognition

class gluonfr.model_zoo.AttentionNet

AttentionNet Model from “Residual Attention Network for Image Classification” paper.

Parameters:

• classes (int.) – Number of classification classes.
• modules (list.) – The number of Attention Module in each stage.
• p (int.) – Number of pre-processing Residual Units before split into trunk branch and mask branch.
• t (int.) – Number of Residual Units in trunk branch.
• r (int.) – Number of Residual Units between adjacent pooling layer in the mask branch.
• kwargs –

class gluonfr.model_zoo.AttentionNetFace

AttentionNet Model for input 112x112.

Parameters:

• classes (int.) – Number of classification classes.
• modules (list.) – The number of Attention Module in each stage.
• p (int.) – Number of pre-processing Residual Units before split into trunk branch and mask branch.
• t (int.) – Number of Residual Units in trunk branch.
• r (int.) – Number of Residual Units between adjacent pooling layer in the mask branch.
• embedding_size (int) – Units of embedding layer.
• weight_norm (bool, default False) – Whether use weight norm in NormDense layer.
• feature_norm (bool, default False) – Whether use features norm in NormDense layer.
• need_cls_layer (bool, default True) – Whether use NormDense layer.Normally it depends on your loss function. When you use Softmax, ArcLoss or based on Softmax loss, you need to set it to True. When you only need embedding output, like you are predicting or training with triplet loss, you need to set it to False.

class gluonfr.model_zoo.MobileFaceNet

Mobile FaceNet

gluonfr.model_zoo.attention_net128(classes=-1, need_cls_layer=True, **kwargs)

AttentionNet 128 Model for face recognition.

Parameters:

• classes (int, -1) – Number of classification classes.
• need_cls_layer (bool, default True) – Whether to use NormDense output layer.

gluonfr.model_zoo.attention_net164(classes=-1, need_cls_layer=True, **kwargs)

AttentionNet 164 Model for face recognition.

Parameters:

• classes (int, -1) – Number of classification classes.
• need_cls_layer (bool, default True) – Whether to use NormDense output layer.

gluonfr.model_zoo.attention_net236(classes=-1, need_cls_layer=True, **kwargs)

AttentionNet 236 Model for face recognition.

Parameters:

• classes (int, -1) – Number of classification classes.
• need_cls_layer (bool, default True) – Whether to use NormDense output layer.

gluonfr.model_zoo.attention_net452(classes=-1, need_cls_layer=True, **kwargs)

AttentionNet 452 Model for face recognition.

Parameters:

• classes (int, -1) – Number of classification classes.
• need_cls_layer (bool, default True) – Whether to use NormDense output layer.

gluonfr.model_zoo.attention_net56(classes=-1, need_cls_layer=True, **kwargs)

AttentionNet 56 Model for face recognition.

Parameters:

• classes (int, -1) – Number of classification classes.
• need_cls_layer (bool, default True) – Whether to use NormDense output layer.

gluonfr.model_zoo.attention_net92(classes=-1, need_cls_layer=True, **kwargs)

AttentionNet 92 Model for face recognition.

Parameters:

• classes (int, -1) – Number of classification classes.
• need_cls_layer (bool, default True) – Whether to use NormDense output layer.

gluonfr.model_zoo.get_attention_face(classes=-1, num_layers=128, embedding_size=512, need_cls_layer=True, **kwargs)

AttentionNet Model for 112x112 face images from “Residual Attention Network for Image Classification” paper.

Parameters:

• classes (int, -1) – Number of classification classes.
• num_layers (int, 128) – Numbers of layers. Options are 56, 92, 128, 164, 236, 452.
• embedding_size (int, 256) – Feature dimensions of the embedding layers.
• need_cls_layer (bool, default True) – Whether to use NormDense output layer.

gluonfr.model_zoo.get_attention_net(classes, num_layers, **kwargs)

AttentionNet Model from “Residual Attention Network for Image Classification” paper.

Parameters:

• classes (int,) – Number of classification classes.
• num_layers (int) – Numbers of layers. Options are 56, 92, 128, 164, 236, 452.

gluonfr.model_zoo.get_mobile_facenet(classes=-1, need_cls_layer=True, **kwargs)

Parameters:

• classes (int, -1) – Number of classification classes.
• need_cls_layer (bool, default True) – Whether to use NormDense output layer.

gluonfr.model_zoo.get_mobile_facenet_re(classes=-1, need_cls_layer=True, **kwargs)

Parameters:

• classes (int, -1) – Number of classification classes.
• need_cls_layer (bool, default True) – Whether to use NormDense output layer.

gluonfr.model_zoo.get_model(name, **kwargs)

Returns a model by name.

Parameters:

• name (str) – Name of the model.
• classes (int) – Number of classes for the output layer.
• ctx (Context, default CPU) – The context in which to load the pretrained weights.

Returns:

The model.

Return type:

HybridBlock

gluonfr.model_zoo.get_model_list()

Get the entire list of model names in model_zoo.

Returns:Entire list of model names in model_zoo. Return type:list of str

gluonfr.model_zoo.get_se_resnet(num_layers, **kwargs)

SE_ResNet V1 model from “Deep Residual Learning for Image Recognition” paper. SE_ResNet V2 model from “Identity Mappings in Deep Residual Networks” paper.

Parameters:

• version (int) – Version of ResNet. Options are 1, 2.
• num_layers (int) – Numbers of layers. Options are 18, 34, 50, 101, 152.
• pretrained (bool, default False) – Whether to load the pretrained weights for model.
• ctx (Context, default CPU) – The context in which to load the pretrained weights.
• root (str, default '~/.mxnet/models') – Location for keeping the model parameters.

gluonfr.model_zoo.se_resnet101_v2(**kwargs)

SE_ResNet-101 V2 model from “Identity Mappings in Deep Residual Networks” paper.

Parameters:

• pretrained (bool, default False) – Whether to load the pretrained weights for model.
• ctx (Context, default CPU) – The context in which to load the pretrained weights.
• root (str, default '~/.mxnet/models') – Location for keeping the model parameters.

gluonfr.model_zoo.se_resnet152_v2(**kwargs)

SE_ResNet-152 V2 model from “Identity Mappings in Deep Residual Networks” paper.

Parameters:

• pretrained (bool, default False) – Whether to load the pretrained weights for model.
• ctx (Context, default CPU) – The context in which to load the pretrained weights.
• root (str, default '~/.mxnet/models') – Location for keeping the model parameters.

gluonfr.model_zoo.se_resnet18_v2(**kwargs)

SE_ResNet-18 V2 model from “Identity Mappings in Deep Residual Networks” paper.

Parameters:

• pretrained (bool, default False) – Whether to load the pretrained weights for model.
• ctx (Context, default CPU) – The context in which to load the pretrained weights.
• root (str, default '~/.mxnet/models') – Location for keeping the model parameters.

gluonfr.model_zoo.se_resnet34_v2(**kwargs)

SE_ResNet-34 V2 model from “Identity Mappings in Deep Residual Networks” paper.

Parameters:

• pretrained (bool, default False) – Whether to load the pretrained weights for model.
• ctx (Context, default CPU) – The context in which to load the pretrained weights.
• root (str, default '~/.mxnet/models') – Location for keeping the model parameters.

gluonfr.model_zoo.se_resnet50_v2(**kwargs)

SE_ResNet-50 V2 model from “Identity Mappings in Deep Residual Networks” paper.

Parameters:

• pretrained (bool, default False) – Whether to load the pretrained weights for model.
• ctx (Context, default CPU) – The context in which to load the pretrained weights.
• root (str, default '~/.mxnet/models') – Location for keeping the model parameters.