2019-03-25 01:22:43 +08:00
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2019-03-25 01:22:43 +08:00
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2019-05-24 23:30:24 +08:00
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< a href = "../_sources/pkg/models.rst.txt" rel = "nofollow" > View page source< / a >
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< div class = "section" id = "torchreid-models" >
< span id = "id1" > < / span > < h1 > torchreid.models< a class = "headerlink" href = "#torchreid-models" title = "Permalink to this headline" > ¶< / a > < / h1 >
< div class = "section" id = "module-torchreid.models.__init__" >
< span id = "interface" > < / span > < h2 > Interface< a class = "headerlink" href = "#module-torchreid.models.__init__" title = "Permalink to this headline" > ¶< / a > < / h2 >
< dl class = "function" >
< dt id = "torchreid.models.__init__.build_model" >
< code class = "descclassname" > torchreid.models.__init__.< / code > < code class = "descname" > build_model< / code > < span class = "sig-paren" > (< / span > < em > name< / em > , < em > num_classes< / em > , < em > loss='softmax'< / em > , < em > pretrained=True< / em > , < em > use_gpu=True< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/__init__.html#build_model" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.__init__.build_model" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > A function wrapper for building a model.< / p >
< table class = "docutils field-list" frame = "void" rules = "none" >
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < ul class = "first simple" >
< li > < strong > name< / strong > (< em > str< / em > ) – < / li >
< li > < strong > num_classes< / strong > (< em > int< / em > ) – < / li >
< li > < strong > loss< / strong > (< em > str< / em > < em > , < / em > < em > optional< / em > ) –
supports “softmax” and “triplet”. Default is “softmax”.< / li >
< li > < strong > pretrained< / strong > (< em > bool< / em > < em > , < / em > < em > optional< / em > ) –
Default is True.< / li >
< li > < strong > use_gpu< / strong > (< em > bool< / em > < em > , < / em > < em > optional< / em > ) – < / li >
< / ul >
< / td >
< / tr >
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > < p class = "first last" > nn.Module< / p >
< / td >
< / tr >
< / tbody >
< / table >
< dl class = "docutils" >
< dt > Examples::< / dt >
< dd > < div class = "first last highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "gp" > > > > < / span > < span class = "kn" > from< / span > < span class = "nn" > torchreid< / span > < span class = "k" > import< / span > < span class = "n" > models< / span >
< span class = "gp" > > > > < / span > < span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > models< / span > < span class = "o" > .< / span > < span class = "n" > build_model< / span > < span class = "p" > (< / span > < span class = "s1" > ' resnet50' < / span > < span class = "p" > ,< / span > < span class = "mi" > 751< / span > < span class = "p" > ,< / span > < span class = "n" > loss< / span > < span class = "o" > =< / span > < span class = "s1" > ' softmax' < / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< / dd >
< / dl >
< / dd > < / dl >
< dl class = "function" >
< dt id = "torchreid.models.__init__.show_avai_models" >
< code class = "descclassname" > torchreid.models.__init__.< / code > < code class = "descname" > show_avai_models< / code > < span class = "sig-paren" > (< / span > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/__init__.html#show_avai_models" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.__init__.show_avai_models" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > Displays available models.< / p >
< dl class = "docutils" >
< dt > Examples::< / dt >
< dd > < div class = "first last highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "gp" > > > > < / span > < span class = "kn" > from< / span > < span class = "nn" > torchreid< / span > < span class = "k" > import< / span > < span class = "n" > models< / span >
< span class = "gp" > > > > < / span > < span class = "n" > models< / span > < span class = "o" > .< / span > < span class = "n" > show_avai_models< / span > < span class = "p" > ()< / span >
< / pre > < / div >
< / div >
< / dd >
< / dl >
< / dd > < / dl >
< / div >
< div class = "section" id = "imagenet-classification-models" >
< h2 > ImageNet Classification Models< a class = "headerlink" href = "#imagenet-classification-models" title = "Permalink to this headline" > ¶< / a > < / h2 >
< dl class = "class" >
< dt id = "torchreid.models.resnet.ResNet" >
2019-05-24 23:30:24 +08:00
< em class = "property" > class < / em > < code class = "descclassname" > torchreid.models.resnet.< / code > < code class = "descname" > ResNet< / code > < span class = "sig-paren" > (< / span > < em > num_classes< / em > , < em > loss< / em > , < em > block< / em > , < em > layers< / em > , < em > zero_init_residual=False< / em > , < em > groups=1< / em > , < em > width_per_group=64< / em > , < em > replace_stride_with_dilation=None< / em > , < em > norm_layer=None< / em > , < em > last_stride=2< / em > , < em > fc_dims=None< / em > , < em > dropout_p=None< / em > , < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/resnet.html#ResNet" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.resnet.ResNet" title = "Permalink to this definition" > ¶< / a > < / dt >
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< dd > < p > Residual network.< / p >
< dl class = "docutils" >
< dt > Reference:< / dt >
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< dd > < ul class = "first last simple" >
< li > He et al. Deep Residual Learning for Image Recognition. CVPR 2016.< / li >
< li > Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.< / li >
< / ul >
< / dd >
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< dt > Public keys:< / dt >
< dd > < ul class = "first last simple" >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > resnet18< / span > < / code > : ResNet18.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > resnet34< / span > < / code > : ResNet34.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > resnet50< / span > < / code > : ResNet50.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > resnet101< / span > < / code > : ResNet101.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > resnet152< / span > < / code > : ResNet152.< / li >
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< li > < code class = "docutils literal notranslate" > < span class = "pre" > resnext50_32x4d< / span > < / code > : ResNeXt50.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > resnext101_32x8d< / span > < / code > : ResNeXt101.< / li >
2019-03-25 01:22:43 +08:00
< li > < code class = "docutils literal notranslate" > < span class = "pre" > resnet50_fc512< / span > < / code > : ResNet50 + FC.< / li >
< / ul >
< / dd >
< / dl >
< / dd > < / dl >
< dl class = "class" >
< dt id = "torchreid.models.senet.SENet" >
< em class = "property" > class < / em > < code class = "descclassname" > torchreid.models.senet.< / code > < code class = "descname" > SENet< / code > < span class = "sig-paren" > (< / span > < em > num_classes< / em > , < em > loss< / em > , < em > block< / em > , < em > layers< / em > , < em > groups< / em > , < em > reduction< / em > , < em > dropout_p=0.2< / em > , < em > inplanes=128< / em > , < em > input_3x3=True< / em > , < em > downsample_kernel_size=3< / em > , < em > downsample_padding=1< / em > , < em > last_stride=2< / em > , < em > fc_dims=None< / em > , < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/senet.html#SENet" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.senet.SENet" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > Squeeze-and-excitation network.< / p >
< dl class = "docutils" >
< dt > Reference:< / dt >
< dd > Hu et al. Squeeze-and-Excitation Networks. CVPR 2018.< / dd >
< dt > Public keys:< / dt >
< dd > < ul class = "first last simple" >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > senet154< / span > < / code > : SENet154.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > se_resnet50< / span > < / code > : ResNet50 + SE.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > se_resnet101< / span > < / code > : ResNet101 + SE.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > se_resnet152< / span > < / code > : ResNet152 + SE.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > se_resnext50_32x4d< / span > < / code > : ResNeXt50 (groups=32, width=4) + SE.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > se_resnext101_32x4d< / span > < / code > : ResNeXt101 (groups=32, width=4) + SE.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > se_resnet50_fc512< / span > < / code > : (ResNet50 + SE) + FC.< / li >
< / ul >
< / dd >
< / dl >
< / dd > < / dl >
< dl class = "class" >
< dt id = "torchreid.models.densenet.DenseNet" >
< em class = "property" > class < / em > < code class = "descclassname" > torchreid.models.densenet.< / code > < code class = "descname" > DenseNet< / code > < span class = "sig-paren" > (< / span > < em > num_classes< / em > , < em > loss< / em > , < em > growth_rate=32< / em > , < em > block_config=(6< / em > , < em > 12< / em > , < em > 24< / em > , < em > 16)< / em > , < em > num_init_features=64< / em > , < em > bn_size=4< / em > , < em > drop_rate=0< / em > , < em > fc_dims=None< / em > , < em > dropout_p=None< / em > , < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/densenet.html#DenseNet" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.densenet.DenseNet" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > Densely connected network.< / p >
< dl class = "docutils" >
< dt > Reference:< / dt >
< dd > Huang et al. Densely Connected Convolutional Networks. CVPR 2017.< / dd >
< dt > Public keys:< / dt >
< dd > < ul class = "first last simple" >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > densenet121< / span > < / code > : DenseNet121.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > densenet169< / span > < / code > : DenseNet169.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > densenet201< / span > < / code > : DenseNet201.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > densenet161< / span > < / code > : DenseNet161.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > densenet121_fc512< / span > < / code > : DenseNet121 + FC.< / li >
< / ul >
< / dd >
< / dl >
< / dd > < / dl >
< dl class = "class" >
< dt id = "torchreid.models.inceptionresnetv2.InceptionResNetV2" >
< em class = "property" > class < / em > < code class = "descclassname" > torchreid.models.inceptionresnetv2.< / code > < code class = "descname" > InceptionResNetV2< / code > < span class = "sig-paren" > (< / span > < em > num_classes< / em > , < em > loss='softmax'< / em > , < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/inceptionresnetv2.html#InceptionResNetV2" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.inceptionresnetv2.InceptionResNetV2" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > Inception-ResNet-V2.< / p >
< dl class = "docutils" >
< dt > Reference:< / dt >
< dd > Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual
Connections on Learning. AAAI 2017.< / dd >
< dt > Public keys:< / dt >
< dd > < ul class = "first last simple" >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > inceptionresnetv2< / span > < / code > : Inception-ResNet-V2.< / li >
< / ul >
< / dd >
< / dl >
< / dd > < / dl >
< dl class = "class" >
< dt id = "torchreid.models.inceptionv4.InceptionV4" >
< em class = "property" > class < / em > < code class = "descclassname" > torchreid.models.inceptionv4.< / code > < code class = "descname" > InceptionV4< / code > < span class = "sig-paren" > (< / span > < em > num_classes< / em > , < em > loss< / em > , < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/inceptionv4.html#InceptionV4" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.inceptionv4.InceptionV4" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > Inception-v4.< / p >
< dl class = "docutils" >
< dt > Reference:< / dt >
< dd > Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual
Connections on Learning. AAAI 2017.< / dd >
< dt > Public keys:< / dt >
< dd > < ul class = "first last simple" >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > inceptionv4< / span > < / code > : InceptionV4.< / li >
< / ul >
< / dd >
< / dl >
< / dd > < / dl >
< dl class = "class" >
< dt id = "torchreid.models.xception.Xception" >
< em class = "property" > class < / em > < code class = "descclassname" > torchreid.models.xception.< / code > < code class = "descname" > Xception< / code > < span class = "sig-paren" > (< / span > < em > num_classes< / em > , < em > loss< / em > , < em > fc_dims=None< / em > , < em > dropout_p=None< / em > , < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/xception.html#Xception" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.xception.Xception" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > Xception.< / p >
< dl class = "docutils" >
< dt > Reference:< / dt >
< dd > Chollet. Xception: Deep Learning with Depthwise
Separable Convolutions. CVPR 2017.< / dd >
< dt > Public keys:< / dt >
< dd > < ul class = "first last simple" >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > xception< / span > < / code > : Xception.< / li >
< / ul >
< / dd >
< / dl >
< / dd > < / dl >
< / div >
< div class = "section" id = "lightweight-models" >
< h2 > Lightweight Models< a class = "headerlink" href = "#lightweight-models" title = "Permalink to this headline" > ¶< / a > < / h2 >
< dl class = "class" >
< dt id = "torchreid.models.nasnet.NASNetAMobile" >
< em class = "property" > class < / em > < code class = "descclassname" > torchreid.models.nasnet.< / code > < code class = "descname" > NASNetAMobile< / code > < span class = "sig-paren" > (< / span > < em > num_classes< / em > , < em > loss< / em > , < em > stem_filters=32< / em > , < em > penultimate_filters=1056< / em > , < em > filters_multiplier=2< / em > , < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/nasnet.html#NASNetAMobile" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.nasnet.NASNetAMobile" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > Neural Architecture Search (NAS).< / p >
< dl class = "docutils" >
< dt > Reference:< / dt >
< dd > Zoph et al. Learning Transferable Architectures
for Scalable Image Recognition. CVPR 2018.< / dd >
< dt > Public keys:< / dt >
< dd > < ul class = "first last simple" >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > nasnetamobile< / span > < / code > : NASNet-A Mobile.< / li >
< / ul >
< / dd >
< / dl >
< / dd > < / dl >
< dl class = "class" >
< dt id = "torchreid.models.mobilenetv2.MobileNetV2" >
< em class = "property" > class < / em > < code class = "descclassname" > torchreid.models.mobilenetv2.< / code > < code class = "descname" > MobileNetV2< / code > < span class = "sig-paren" > (< / span > < em > num_classes< / em > , < em > width_mult=1< / em > , < em > loss='softmax'< / em > , < em > fc_dims=None< / em > , < em > dropout_p=None< / em > , < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/mobilenetv2.html#MobileNetV2" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.mobilenetv2.MobileNetV2" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > MobileNetV2.< / p >
< dl class = "docutils" >
< dt > Reference:< / dt >
< dd > Sandler et al. MobileNetV2: Inverted Residuals and
Linear Bottlenecks. CVPR 2018.< / dd >
< dt > Public keys:< / dt >
< dd > < ul class = "first last simple" >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > mobilenetv2_1dot0< / span > < / code > : MobileNetV2 x1.0.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > mobilenetv2_1dot4< / span > < / code > : MobileNetV2 x1.4.< / li >
< / ul >
< / dd >
< / dl >
< / dd > < / dl >
< dl class = "class" >
< dt id = "torchreid.models.shufflenet.ShuffleNet" >
< em class = "property" > class < / em > < code class = "descclassname" > torchreid.models.shufflenet.< / code > < code class = "descname" > ShuffleNet< / code > < span class = "sig-paren" > (< / span > < em > num_classes< / em > , < em > loss='softmax'< / em > , < em > num_groups=3< / em > , < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/shufflenet.html#ShuffleNet" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.shufflenet.ShuffleNet" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > ShuffleNet.< / p >
< dl class = "docutils" >
< dt > Reference:< / dt >
< dd > Zhang et al. ShuffleNet: An Extremely Efficient Convolutional Neural
Network for Mobile Devices. CVPR 2018.< / dd >
< dt > Public keys:< / dt >
< dd > < ul class = "first last simple" >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > shufflenet< / span > < / code > : ShuffleNet (groups=3).< / li >
< / ul >
< / dd >
< / dl >
< / dd > < / dl >
< dl class = "class" >
< dt id = "torchreid.models.squeezenet.SqueezeNet" >
< em class = "property" > class < / em > < code class = "descclassname" > torchreid.models.squeezenet.< / code > < code class = "descname" > SqueezeNet< / code > < span class = "sig-paren" > (< / span > < em > num_classes< / em > , < em > loss< / em > , < em > version=1.0< / em > , < em > fc_dims=None< / em > , < em > dropout_p=None< / em > , < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/squeezenet.html#SqueezeNet" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.squeezenet.SqueezeNet" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > SqueezeNet.< / p >
< dl class = "docutils" >
< dt > Reference:< / dt >
< dd > Iandola et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
and< 0.5 MB model size. arXiv:1602.07360.< / dd >
< dt > Public keys:< / dt >
< dd > < ul class = "first last simple" >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > squeezenet1_0< / span > < / code > : SqueezeNet (version=1.0).< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > squeezenet1_1< / span > < / code > : SqueezeNet (version=1.1).< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > squeezenet1_0_fc512< / span > < / code > : SqueezeNet (version=1.0) + FC.< / li >
< / ul >
< / dd >
< / dl >
< / dd > < / dl >
2019-05-24 23:30:24 +08:00
< dl class = "class" >
< dt id = "torchreid.models.shufflenetv2.ShuffleNetV2" >
< em class = "property" > class < / em > < code class = "descclassname" > torchreid.models.shufflenetv2.< / code > < code class = "descname" > ShuffleNetV2< / code > < span class = "sig-paren" > (< / span > < em > num_classes< / em > , < em > loss< / em > , < em > stages_repeats< / em > , < em > stages_out_channels< / em > , < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/shufflenetv2.html#ShuffleNetV2" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.shufflenetv2.ShuffleNetV2" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > ShuffleNetV2.< / p >
< dl class = "docutils" >
< dt > Reference:< / dt >
< dd > Ma et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. ECCV 2018.< / dd >
< dt > Public keys:< / dt >
< dd > < ul class = "first last simple" >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > shufflenet_v2_x0_5< / span > < / code > : ShuffleNetV2 x0.5.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > shufflenet_v2_x1_0< / span > < / code > : ShuffleNetV2 x1.0.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > shufflenet_v2_x1_5< / span > < / code > : ShuffleNetV2 x1.5.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > shufflenet_v2_x2_0< / span > < / code > : ShuffleNetV2 x2.0.< / li >
< / ul >
< / dd >
< / dl >
< / dd > < / dl >
2019-03-25 01:22:43 +08:00
< / div >
< div class = "section" id = "reid-specific-models" >
< h2 > ReID-specific Models< a class = "headerlink" href = "#reid-specific-models" title = "Permalink to this headline" > ¶< / a > < / h2 >
< dl class = "class" >
< dt id = "torchreid.models.mudeep.MuDeep" >
< em class = "property" > class < / em > < code class = "descclassname" > torchreid.models.mudeep.< / code > < code class = "descname" > MuDeep< / code > < span class = "sig-paren" > (< / span > < em > num_classes< / em > , < em > loss='softmax'< / em > , < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/mudeep.html#MuDeep" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.mudeep.MuDeep" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > Multiscale deep neural network.< / p >
< dl class = "docutils" >
< dt > Reference:< / dt >
< dd > Qian et al. Multi-scale Deep Learning Architectures
for Person Re-identification. ICCV 2017.< / dd >
< dt > Public keys:< / dt >
< dd > < ul class = "first last simple" >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > mudeep< / span > < / code > : Multiscale deep neural network.< / li >
< / ul >
< / dd >
< / dl >
< / dd > < / dl >
< dl class = "class" >
< dt id = "torchreid.models.resnetmid.ResNetMid" >
< em class = "property" > class < / em > < code class = "descclassname" > torchreid.models.resnetmid.< / code > < code class = "descname" > ResNetMid< / code > < span class = "sig-paren" > (< / span > < em > num_classes< / em > , < em > loss< / em > , < em > block< / em > , < em > layers< / em > , < em > last_stride=2< / em > , < em > fc_dims=None< / em > , < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/resnetmid.html#ResNetMid" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.resnetmid.ResNetMid" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > Residual network + mid-level features.< / p >
< dl class = "docutils" >
< dt > Reference:< / dt >
< dd > Yu et al. The Devil is in the Middle: Exploiting Mid-level Representations for
Cross-Domain Instance Matching. arXiv:1711.08106.< / dd >
< dt > Public keys:< / dt >
< dd > < ul class = "first last simple" >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > resnet50mid< / span > < / code > : ResNet50 + mid-level feature fusion.< / li >
< / ul >
< / dd >
< / dl >
< / dd > < / dl >
< dl class = "class" >
< dt id = "torchreid.models.hacnn.HACNN" >
< em class = "property" > class < / em > < code class = "descclassname" > torchreid.models.hacnn.< / code > < code class = "descname" > HACNN< / code > < span class = "sig-paren" > (< / span > < em > num_classes, loss='softmax', nchannels=[128, 256, 384], feat_dim=512, learn_region=True, use_gpu=True, **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/hacnn.html#HACNN" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.hacnn.HACNN" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > Harmonious Attention Convolutional Neural Network.< / p >
< dl class = "docutils" >
< dt > Reference:< / dt >
< dd > Li et al. Harmonious Attention Network for Person Re-identification. CVPR 2018.< / dd >
< dt > Public keys:< / dt >
< dd > < ul class = "first last simple" >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > hacnn< / span > < / code > : HACNN.< / li >
< / ul >
< / dd >
< / dl >
< / dd > < / dl >
< dl class = "class" >
< dt id = "torchreid.models.pcb.PCB" >
< em class = "property" > class < / em > < code class = "descclassname" > torchreid.models.pcb.< / code > < code class = "descname" > PCB< / code > < span class = "sig-paren" > (< / span > < em > num_classes< / em > , < em > loss< / em > , < em > block< / em > , < em > layers< / em > , < em > parts=6< / em > , < em > reduced_dim=256< / em > , < em > nonlinear='relu'< / em > , < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/pcb.html#PCB" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.pcb.PCB" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > Part-based Convolutional Baseline.< / p >
< dl class = "docutils" >
< dt > Reference:< / dt >
< dd > Sun et al. Beyond Part Models: Person Retrieval with Refined
Part Pooling (and A Strong Convolutional Baseline). ECCV 2018.< / dd >
< dt > Public keys:< / dt >
< dd > < ul class = "first last simple" >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > pcb_p4< / span > < / code > : PCB with 4-part strips.< / li >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > pcb_p6< / span > < / code > : PCB with 6-part strips.< / li >
< / ul >
< / dd >
< / dl >
< / dd > < / dl >
< dl class = "class" >
< dt id = "torchreid.models.mlfn.MLFN" >
< em class = "property" > class < / em > < code class = "descclassname" > torchreid.models.mlfn.< / code > < code class = "descname" > MLFN< / code > < span class = "sig-paren" > (< / span > < em > num_classes, loss='softmax', groups=32, channels=[64, 256, 512, 1024, 2048], embed_dim=1024, **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/models/mlfn.html#MLFN" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.models.mlfn.MLFN" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > Multi-Level Factorisation Net.< / p >
< dl class = "docutils" >
< dt > Reference:< / dt >
< dd > Chang et al. Multi-Level Factorisation Net for
Person Re-Identification. CVPR 2018.< / dd >
< dt > Public keys:< / dt >
< dd > < ul class = "first last simple" >
< li > < code class = "docutils literal notranslate" > < span class = "pre" > mlfn< / span > < / code > : MLFN (Multi-Level Factorisation Net).< / li >
< / ul >
< / dd >
< / dl >
< / dd > < / dl >
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