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Model Summaries
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< a href = "#big-transfer-resnetv2-bit-resnetv2py" class = "md-nav__link" >
Big Transfer ResNetV2 (BiT) [resnetv2.py]
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< a href = "#cross-stage-partial-networks-cspnetpy" class = "md-nav__link" >
Cross-Stage Partial Networks [cspnet.py]
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< a href = "#densenet-densenetpy" class = "md-nav__link" >
DenseNet [densenet.py]
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< a href = "#dla-dlapy" class = "md-nav__link" >
DLA [dla.py]
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< a href = "#dual-path-networks-dpnpy" class = "md-nav__link" >
Dual-Path Networks [dpn.py]
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< a href = "#gpu-efficient-networks-byobnetpy" class = "md-nav__link" >
GPU-Efficient Networks [byobnet.py]
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< a href = "#hrnet-hrnetpy" class = "md-nav__link" >
HRNet [hrnet.py]
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< a href = "#inception-v3-inception_v3py" class = "md-nav__link" >
Inception-V3 [inception_v3.py]
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< a href = "#inception-v4-inception_v4py" class = "md-nav__link" >
Inception-V4 [inception_v4.py]
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< a href = "#inception-resnet-v2-inception_resnet_v2py" class = "md-nav__link" >
Inception-ResNet-V2 [inception_resnet_v2.py]
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< a href = "#nasnet-a-nasnetpy" class = "md-nav__link" >
NASNet-A [nasnet.py]
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< a href = "#pnasnet-5-pnasnetpy" class = "md-nav__link" >
PNasNet-5 [pnasnet.py]
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< a href = "#efficientnet-efficientnetpy" class = "md-nav__link" >
EfficientNet [efficientnet.py]
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< a href = "#mobilenet-v3-mobilenetv3py" class = "md-nav__link" >
MobileNet-V3 [mobilenetv3.py]
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< a href = "#regnet-regnetpy" class = "md-nav__link" >
RegNet [regnet.py]
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< a href = "#repvgg-byobnetpy" class = "md-nav__link" >
RepVGG [byobnet.py]
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< a href = "#resnet-resnext-resnetpy" class = "md-nav__link" >
ResNet, ResNeXt [resnet.py]
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< a href = "#res2net-res2netpy" class = "md-nav__link" >
Res2Net [res2net.py]
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< a href = "#resnest-resnestpy" class = "md-nav__link" >
ResNeSt [resnest.py]
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< a href = "#rexnet-rexnetpy" class = "md-nav__link" >
ReXNet [rexnet.py]
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< a href = "#selective-kernel-networks-sknetpy" class = "md-nav__link" >
Selective-Kernel Networks [sknet.py]
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SelecSLS [selecsls.py]
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Squeeze-and-Excitation Networks [senet.py]
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TResNet [tresnet.py]
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VGG [vgg.py]
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Vision Transformer [vision_transformer.py]
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VovNet V2 and V1 [vovnet.py]
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Xception [xception.py]
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Xception (Modified Aligned, Gluon) [gluon_xception.py]
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Xception (Modified Aligned, TF) [aligned_xception.py]
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Adversarial Inception v3
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AdvProp (EfficientNet)
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Big Transfer (BiT)
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CSP-DarkNet
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CSP-ResNet
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CSP-ResNeXt
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< a href = "densenet/" class = "md-nav__link" >
DenseNet
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Deep Layer Aggregation
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Dual Path Network (DPN)
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ECA-ResNet
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EfficientNet (Knapsack Pruned)
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EfficientNet
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Ensemble Adversarial Inception ResNet v2
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< a href = "ese-vovnet/" class = "md-nav__link" >
ESE-VoVNet
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< a href = "fbnet/" class = "md-nav__link" >
FBNet
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(Gluon) Inception v3
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(Gluon) ResNet
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(Gluon) ResNeXt
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(Gluon) SENet
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< a href = "gloun-seresnext/" class = "md-nav__link" >
(Gluon) SE-ResNeXt
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< a href = "gloun-xception/" class = "md-nav__link" >
(Gluon) Xception
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< a href = "hrnet/" class = "md-nav__link" >
HRNet
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< a href = "ig-resnext/" class = "md-nav__link" >
Instagram ResNeXt WSL
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< a href = "inception-resnet-v2/" class = "md-nav__link" >
Inception ResNet v2
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Inception v3
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Inception v4
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< a href = "legacy-se-resnet/" class = "md-nav__link" >
(Legacy) SE-ResNet
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< a href = "legacy-se-resnext/" class = "md-nav__link" >
(Legacy) SE-ResNeXt
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< a href = "legacy-senet/" class = "md-nav__link" >
(Legacy) SENet
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< a href = "mixnet/" class = "md-nav__link" >
MixNet
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< a href = "mnasnet/" class = "md-nav__link" >
MnasNet
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< a href = "mobilenet-v2/" class = "md-nav__link" >
MobileNet v2
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< a href = "mobilenet-v3/" class = "md-nav__link" >
MobileNet v3
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NASNet
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Noisy Student (EfficientNet)
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PNASNet
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RegNetX
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RegNetY
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Res2Net
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< a href = "res2next/" class = "md-nav__link" >
Res2NeXt
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< li class = "md-nav__item" >
< a href = "resnest/" class = "md-nav__link" >
ResNeSt
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< li class = "md-nav__item" >
< a href = "resnet-d/" class = "md-nav__link" >
ResNet-D
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< li class = "md-nav__item" >
< a href = "resnet/" class = "md-nav__link" >
ResNet
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< a href = "resnext/" class = "md-nav__link" >
ResNeXt
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< li class = "md-nav__item" >
< a href = "rexnet/" class = "md-nav__link" >
RexNet
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< a href = "se-resnet/" class = "md-nav__link" >
SE-ResNet
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< a href = "selecsls/" class = "md-nav__link" >
SelecSLS
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< a href = "seresnext/" class = "md-nav__link" >
SE-ResNeXt
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< li class = "md-nav__item" >
< a href = "skresnet/" class = "md-nav__link" >
SK-ResNet
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< li class = "md-nav__item" >
< a href = "skresnext/" class = "md-nav__link" >
SK-ResNeXt
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "spnasnet/" class = "md-nav__link" >
SPNASNet
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< li class = "md-nav__item" >
< a href = "ssl-resnet/" class = "md-nav__link" >
SSL ResNet
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< li class = "md-nav__item" >
< a href = "ssl-resnext/" class = "md-nav__link" >
SSL ResNeXT
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< li class = "md-nav__item" >
< a href = "swsl-resnet/" class = "md-nav__link" >
SWSL ResNet
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< li class = "md-nav__item" >
< a href = "swsl-resnext/" class = "md-nav__link" >
SWSL ResNeXt
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< li class = "md-nav__item" >
< a href = "tf-efficientnet-condconv/" class = "md-nav__link" >
(Tensorflow) EfficientNet CondConv
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< li class = "md-nav__item" >
< a href = "tf-efficientnet-lite/" class = "md-nav__link" >
(Tensorflow) EfficientNet Lite
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< li class = "md-nav__item" >
< a href = "tf-efficientnet/" class = "md-nav__link" >
(Tensorflow) EfficientNet
< / a >
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< li class = "md-nav__item" >
< a href = "tf-inception-v3/" class = "md-nav__link" >
(Tensorflow) Inception v3
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< li class = "md-nav__item" >
< a href = "tf-mixnet/" class = "md-nav__link" >
(Tensorflow) MixNet
< / a >
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< li class = "md-nav__item" >
< a href = "tf-mobilenet-v3/" class = "md-nav__link" >
(Tensorflow) MobileNet v3
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< li class = "md-nav__item" >
< a href = "tresnet/" class = "md-nav__link" >
TResNet
< / a >
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< li class = "md-nav__item" >
< a href = "vision-transformer/" class = "md-nav__link" >
Vision Transformer (ViT)
< / a >
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< li class = "md-nav__item" >
< a href = "wide-resnet/" class = "md-nav__link" >
Wide ResNet
< / a >
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< li class = "md-nav__item" >
< a href = "xception/" class = "md-nav__link" >
Xception
< / a >
< / li >
< / ul >
< / nav >
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< li class = "md-nav__item" >
< a href = "../results/" class = "md-nav__link" >
Results
< / a >
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< li class = "md-nav__item" >
< a href = "../scripts/" class = "md-nav__link" >
Scripts
< / a >
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< li class = "md-nav__item" >
< a href = "../training_hparam_examples/" class = "md-nav__link" >
Training Examples
< / a >
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< li class = "md-nav__item" >
< a href = "../feature_extraction/" class = "md-nav__link" >
Feature Extraction
< / a >
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< li class = "md-nav__item" >
< a href = "../changes/" class = "md-nav__link" >
Recent Changes
< / a >
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< li class = "md-nav__item" >
< a href = "../archived_changes/" class = "md-nav__link" >
Archived Changes
< / a >
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< / ul >
< / nav >
< / div >
< / div >
< / div >
< div class = "md-sidebar md-sidebar--secondary" data-md-component = "sidebar" data-md-type = "toc" >
< div class = "md-sidebar__scrollwrap" >
< div class = "md-sidebar__inner" >
< nav class = "md-nav md-nav--secondary" aria-label = "Table of contents" >
< label class = "md-nav__title" for = "__toc" >
< span class = "md-nav__icon md-icon" > < / span >
Table of contents
< / label >
< ul class = "md-nav__list" data-md-component = "toc" data-md-scrollfix >
< li class = "md-nav__item" >
< a href = "#big-transfer-resnetv2-bit-resnetv2py" class = "md-nav__link" >
Big Transfer ResNetV2 (BiT) [resnetv2.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#cross-stage-partial-networks-cspnetpy" class = "md-nav__link" >
Cross-Stage Partial Networks [cspnet.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#densenet-densenetpy" class = "md-nav__link" >
DenseNet [densenet.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#dla-dlapy" class = "md-nav__link" >
DLA [dla.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#dual-path-networks-dpnpy" class = "md-nav__link" >
Dual-Path Networks [dpn.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#gpu-efficient-networks-byobnetpy" class = "md-nav__link" >
GPU-Efficient Networks [byobnet.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#hrnet-hrnetpy" class = "md-nav__link" >
HRNet [hrnet.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#inception-v3-inception_v3py" class = "md-nav__link" >
Inception-V3 [inception_v3.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#inception-v4-inception_v4py" class = "md-nav__link" >
Inception-V4 [inception_v4.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#inception-resnet-v2-inception_resnet_v2py" class = "md-nav__link" >
Inception-ResNet-V2 [inception_resnet_v2.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#nasnet-a-nasnetpy" class = "md-nav__link" >
NASNet-A [nasnet.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#pnasnet-5-pnasnetpy" class = "md-nav__link" >
PNasNet-5 [pnasnet.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#efficientnet-efficientnetpy" class = "md-nav__link" >
EfficientNet [efficientnet.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#mobilenet-v3-mobilenetv3py" class = "md-nav__link" >
MobileNet-V3 [mobilenetv3.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#regnet-regnetpy" class = "md-nav__link" >
RegNet [regnet.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#repvgg-byobnetpy" class = "md-nav__link" >
RepVGG [byobnet.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#resnet-resnext-resnetpy" class = "md-nav__link" >
ResNet, ResNeXt [resnet.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#res2net-res2netpy" class = "md-nav__link" >
Res2Net [res2net.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#resnest-resnestpy" class = "md-nav__link" >
ResNeSt [resnest.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#rexnet-rexnetpy" class = "md-nav__link" >
ReXNet [rexnet.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#selective-kernel-networks-sknetpy" class = "md-nav__link" >
Selective-Kernel Networks [sknet.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#selecsls-selecslspy" class = "md-nav__link" >
SelecSLS [selecsls.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#squeeze-and-excitation-networks-senetpy" class = "md-nav__link" >
Squeeze-and-Excitation Networks [senet.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#tresnet-tresnetpy" class = "md-nav__link" >
TResNet [tresnet.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#vgg-vggpy" class = "md-nav__link" >
VGG [vgg.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#vision-transformer-vision_transformerpy" class = "md-nav__link" >
Vision Transformer [vision_transformer.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#vovnet-v2-and-v1-vovnetpy" class = "md-nav__link" >
VovNet V2 and V1 [vovnet.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#xception-xceptionpy" class = "md-nav__link" >
Xception [xception.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#xception-modified-aligned-gluon-gluon_xceptionpy" class = "md-nav__link" >
Xception (Modified Aligned, Gluon) [gluon_xception.py]
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#xception-modified-aligned-tf-aligned_xceptionpy" class = "md-nav__link" >
Xception (Modified Aligned, TF) [aligned_xception.py]
< / a >
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< / ul >
< / nav >
< / div >
< / div >
< / div >
< div class = "md-content" data-md-component = "content" >
< article class = "md-content__inner md-typeset" >
< a href = "https://github.com/rwightman/pytorch-image-models/edit/master/docs/models.md" title = "Edit this page" class = "md-content__button md-icon" >
< svg xmlns = "http://www.w3.org/2000/svg" viewBox = "0 0 24 24" > < path d = "M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z" / > < / svg >
< / a >
< h1 id = "model-summaries" > Model Summaries< / h1 >
< p > The model architectures included come from a wide variety of sources. Sources, including papers, original impl ("reference code") that I rewrote / adapted, and PyTorch impl that I leveraged directly ("code") are listed below.< / p >
< p > Most included models have pretrained weights. The weights are either:< / p >
< ol >
< li > from their original sources< / li >
< li > ported by myself from their original impl in a different framework (e.g. Tensorflow models)< / li >
< li > trained from scratch using the included training script< / li >
< / ol >
< p > The validation results for the pretrained weights are < a href = "../results/" > here< / a > < / p >
< p > A more exciting view (with pretty pictures) of the models within < code > timm< / code > can be found at < a href = "https://paperswithcode.com/lib/timm" > paperswithcode< / a > .< / p >
< h2 id = "big-transfer-resnetv2-bit-resnetv2py" > Big Transfer ResNetV2 (BiT) [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnetv2.py" > resnetv2.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > Big Transfer (BiT): General Visual Representation Learning< / code > - < a href = "https://arxiv.org/abs/1912.11370" > https://arxiv.org/abs/1912.11370< / a > < / li >
< li > Reference code: < a href = "https://github.com/google-research/big_transfer" > https://github.com/google-research/big_transfer< / a > < / li >
< / ul >
< h2 id = "cross-stage-partial-networks-cspnetpy" > Cross-Stage Partial Networks [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/cspnet.py" > cspnet.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > CSPNet: A New Backbone that can Enhance Learning Capability of CNN< / code > - < a href = "https://arxiv.org/abs/1911.11929" > https://arxiv.org/abs/1911.11929< / a > < / li >
< li > Reference impl: < a href = "https://github.com/WongKinYiu/CrossStagePartialNetworks" > https://github.com/WongKinYiu/CrossStagePartialNetworks< / a > < / li >
< / ul >
< h2 id = "densenet-densenetpy" > DenseNet [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/densenet.py" > densenet.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > Densely Connected Convolutional Networks< / code > - < a href = "https://arxiv.org/abs/1608.06993" > https://arxiv.org/abs/1608.06993< / a > < / li >
< li > Code: < a href = "https://github.com/pytorch/vision/tree/master/torchvision/models" > https://github.com/pytorch/vision/tree/master/torchvision/models< / a > < / li >
< / ul >
< h2 id = "dla-dlapy" > DLA [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/dla.py" > dla.py< / a > ]< / h2 >
< ul >
< li > Paper: < a href = "https://arxiv.org/abs/1707.06484" > https://arxiv.org/abs/1707.06484< / a > < / li >
< li > Code: < a href = "https://github.com/ucbdrive/dla" > https://github.com/ucbdrive/dla< / a > < / li >
< / ul >
< h2 id = "dual-path-networks-dpnpy" > Dual-Path Networks [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/dpn.py" > dpn.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > Dual Path Networks< / code > - < a href = "https://arxiv.org/abs/1707.01629" > https://arxiv.org/abs/1707.01629< / a > < / li >
< li > My PyTorch code: < a href = "https://github.com/rwightman/pytorch-dpn-pretrained" > https://github.com/rwightman/pytorch-dpn-pretrained< / a > < / li >
< li > Reference code: < a href = "https://github.com/cypw/DPNs" > https://github.com/cypw/DPNs< / a > < / li >
< / ul >
< h2 id = "gpu-efficient-networks-byobnetpy" > GPU-Efficient Networks [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/byobnet.py" > byobnet.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > Neural Architecture Design for GPU-Efficient Networks< / code > - < a href = "https://arxiv.org/abs/2006.14090" > https://arxiv.org/abs/2006.14090< / a > < / li >
< li > Reference code: < a href = "https://github.com/idstcv/GPU-Efficient-Networks" > https://github.com/idstcv/GPU-Efficient-Networks< / a > < / li >
< / ul >
< h2 id = "hrnet-hrnetpy" > HRNet [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/hrnet.py" > hrnet.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > Deep High-Resolution Representation Learning for Visual Recognition< / code > - < a href = "https://arxiv.org/abs/1908.07919" > https://arxiv.org/abs/1908.07919< / a > < / li >
< li > Code: < a href = "https://github.com/HRNet/HRNet-Image-Classification" > https://github.com/HRNet/HRNet-Image-Classification< / a > < / li >
< / ul >
< h2 id = "inception-v3-inception_v3py" > Inception-V3 [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_v3.py" > inception_v3.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > Rethinking the Inception Architecture for Computer Vision< / code > - < a href = "https://arxiv.org/abs/1512.00567" > https://arxiv.org/abs/1512.00567< / a > < / li >
< li > Code: < a href = "https://github.com/pytorch/vision/tree/master/torchvision/models" > https://github.com/pytorch/vision/tree/master/torchvision/models< / a > < / li >
< / ul >
< h2 id = "inception-v4-inception_v4py" > Inception-V4 [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_v4.py" > inception_v4.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning< / code > - < a href = "https://arxiv.org/abs/1602.07261" > https://arxiv.org/abs/1602.07261< / a > < / li >
< li > Code: < a href = "https://github.com/Cadene/pretrained-models.pytorch" > https://github.com/Cadene/pretrained-models.pytorch< / a > < / li >
< li > Reference code: < a href = "https://github.com/tensorflow/models/tree/master/research/slim/nets" > https://github.com/tensorflow/models/tree/master/research/slim/nets< / a > < / li >
< / ul >
< h2 id = "inception-resnet-v2-inception_resnet_v2py" > Inception-ResNet-V2 [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_resnet_v2.py" > inception_resnet_v2.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning< / code > - < a href = "https://arxiv.org/abs/1602.07261" > https://arxiv.org/abs/1602.07261< / a > < / li >
< li > Code: < a href = "https://github.com/Cadene/pretrained-models.pytorch" > https://github.com/Cadene/pretrained-models.pytorch< / a > < / li >
< li > Reference code: < a href = "https://github.com/tensorflow/models/tree/master/research/slim/nets" > https://github.com/tensorflow/models/tree/master/research/slim/nets< / a > < / li >
< / ul >
< h2 id = "nasnet-a-nasnetpy" > NASNet-A [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/nasnet.py" > nasnet.py< / a > ]< / h2 >
< ul >
< li > Papers: < code > Learning Transferable Architectures for Scalable Image Recognition< / code > - < a href = "https://arxiv.org/abs/1707.07012" > https://arxiv.org/abs/1707.07012< / a > < / li >
< li > Code: < a href = "https://github.com/Cadene/pretrained-models.pytorch" > https://github.com/Cadene/pretrained-models.pytorch< / a > < / li >
< li > Reference code: < a href = "https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet" > https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet< / a > < / li >
< / ul >
< h2 id = "pnasnet-5-pnasnetpy" > PNasNet-5 [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/pnasnet.py" > pnasnet.py< / a > ]< / h2 >
< ul >
< li > Papers: < code > Progressive Neural Architecture Search< / code > - < a href = "https://arxiv.org/abs/1712.00559" > https://arxiv.org/abs/1712.00559< / a > < / li >
< li > Code: < a href = "https://github.com/Cadene/pretrained-models.pytorch" > https://github.com/Cadene/pretrained-models.pytorch< / a > < / li >
< li > Reference code: < a href = "https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet" > https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet< / a > < / li >
< / ul >
< h2 id = "efficientnet-efficientnetpy" > EfficientNet [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py" > efficientnet.py< / a > ]< / h2 >
< ul >
< li > Papers:< ul >
< li > EfficientNet NoisyStudent (B0-B7, L2) - < a href = "https://arxiv.org/abs/1911.04252" > https://arxiv.org/abs/1911.04252< / a > < / li >
< li > EfficientNet AdvProp (B0-B8) - < a href = "https://arxiv.org/abs/1911.09665" > https://arxiv.org/abs/1911.09665< / a > < / li >
< li > EfficientNet (B0-B7) - < a href = "https://arxiv.org/abs/1905.11946" > https://arxiv.org/abs/1905.11946< / a > < / li >
< li > EfficientNet-EdgeTPU (S, M, L) - < a href = "https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html" > https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html< / a > < / li >
< li > MixNet - < a href = "https://arxiv.org/abs/1907.09595" > https://arxiv.org/abs/1907.09595< / a > < / li >
< li > MNASNet B1, A1 (Squeeze-Excite), and Small - < a href = "https://arxiv.org/abs/1807.11626" > https://arxiv.org/abs/1807.11626< / a > < / li >
< li > MobileNet-V2 - < a href = "https://arxiv.org/abs/1801.04381" > https://arxiv.org/abs/1801.04381< / a > < / li >
< li > FBNet-C - < a href = "https://arxiv.org/abs/1812.03443" > https://arxiv.org/abs/1812.03443< / a > < / li >
< li > Single-Path NAS - < a href = "https://arxiv.org/abs/1904.02877" > https://arxiv.org/abs/1904.02877< / a > < / li >
< / ul >
< / li >
< li > My PyTorch code: < a href = "https://github.com/rwightman/gen-efficientnet-pytorch" > https://github.com/rwightman/gen-efficientnet-pytorch< / a > < / li >
< li > Reference code: < a href = "https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet" > https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet< / a > < / li >
< / ul >
< h2 id = "mobilenet-v3-mobilenetv3py" > MobileNet-V3 [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py" > mobilenetv3.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > Searching for MobileNetV3< / code > - < a href = "https://arxiv.org/abs/1905.02244" > https://arxiv.org/abs/1905.02244< / a > < / li >
< li > Reference code: < a href = "https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet" > https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet< / a > < / li >
< / ul >
< h2 id = "regnet-regnetpy" > RegNet [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/regnet.py" > regnet.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > Designing Network Design Spaces< / code > - < a href = "https://arxiv.org/abs/2003.13678" > https://arxiv.org/abs/2003.13678< / a > < / li >
< li > Reference code: < a href = "https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py" > https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py< / a > < / li >
< / ul >
< h2 id = "repvgg-byobnetpy" > RepVGG [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/byobnet.py" > byobnet.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > Making VGG-style ConvNets Great Again< / code > - < a href = "https://arxiv.org/abs/2101.03697" > https://arxiv.org/abs/2101.03697< / a > < / li >
< li > Reference code: < a href = "https://github.com/DingXiaoH/RepVGG" > https://github.com/DingXiaoH/RepVGG< / a > < / li >
< / ul >
< h2 id = "resnet-resnext-resnetpy" > ResNet, ResNeXt [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnet.py" > resnet.py< / a > ]< / h2 >
< ul >
< li > ResNet (V1B)< ul >
< li > Paper: < code > Deep Residual Learning for Image Recognition< / code > - < a href = "https://arxiv.org/abs/1512.03385" > https://arxiv.org/abs/1512.03385< / a > < / li >
< li > Code: < a href = "https://github.com/pytorch/vision/tree/master/torchvision/models" > https://github.com/pytorch/vision/tree/master/torchvision/models< / a > < / li >
< / ul >
< / li >
< li > ResNeXt< ul >
< li > Paper: < code > Aggregated Residual Transformations for Deep Neural Networks< / code > - < a href = "https://arxiv.org/abs/1611.05431" > https://arxiv.org/abs/1611.05431< / a > < / li >
< li > Code: < a href = "https://github.com/pytorch/vision/tree/master/torchvision/models" > https://github.com/pytorch/vision/tree/master/torchvision/models< / a > < / li >
< / ul >
< / li >
< li > 'Bag of Tricks' / Gluon C, D, E, S ResNet variants< ul >
< li > Paper: < code > Bag of Tricks for Image Classification with CNNs< / code > - < a href = "https://arxiv.org/abs/1812.01187" > https://arxiv.org/abs/1812.01187< / a > < / li >
< li > Code: < a href = "https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/resnetv1b.py" > https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/resnetv1b.py< / a > < / li >
< / ul >
< / li >
< li > Instagram pretrained / ImageNet tuned ResNeXt101< ul >
< li > Paper: < code > Exploring the Limits of Weakly Supervised Pretraining< / code > - < a href = "https://arxiv.org/abs/1805.00932" > https://arxiv.org/abs/1805.00932< / a > < / li >
< li > Weights: < a href = "https://pytorch.org/hub/facebookresearch_WSL-Images_resnext" > https://pytorch.org/hub/facebookresearch_WSL-Images_resnext< / a > (NOTE: CC BY-NC 4.0 License, NOT commercial friendly)< / li >
< / ul >
< / li >
< li > Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet and ResNeXts< ul >
< li > Paper: < code > Billion-scale semi-supervised learning for image classification< / code > - < a href = "https://arxiv.org/abs/1905.00546" > https://arxiv.org/abs/1905.00546< / a > < / li >
< li > Weights: < a href = "https://github.com/facebookresearch/semi-supervised-ImageNet1K-models" > https://github.com/facebookresearch/semi-supervised-ImageNet1K-models< / a > (NOTE: CC BY-NC 4.0 License, NOT commercial friendly)< / li >
< / ul >
< / li >
< li > Squeeze-and-Excitation Networks< ul >
< li > Paper: < code > Squeeze-and-Excitation Networks< / code > - < a href = "https://arxiv.org/abs/1709.01507" > https://arxiv.org/abs/1709.01507< / a > < / li >
< li > Code: Added to ResNet base, this is current version going forward, old < code > senet.py< / code > is being deprecated< / li >
< / ul >
< / li >
< li > ECAResNet (ECA-Net)< ul >
< li > Paper: < code > ECA-Net: Efficient Channel Attention for Deep CNN< / code > - < a href = "https://arxiv.org/abs/1910.03151v4" > https://arxiv.org/abs/1910.03151v4< / a > < / li >
< li > Code: Added to ResNet base, ECA module contributed by @VRandme, reference < a href = "https://github.com/BangguWu/ECANet" > https://github.com/BangguWu/ECANet< / a > < / li >
< / ul >
< / li >
< / ul >
< h2 id = "res2net-res2netpy" > Res2Net [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/res2net.py" > res2net.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > Res2Net: A New Multi-scale Backbone Architecture< / code > - < a href = "https://arxiv.org/abs/1904.01169" > https://arxiv.org/abs/1904.01169< / a > < / li >
< li > Code: < a href = "https://github.com/gasvn/Res2Net" > https://github.com/gasvn/Res2Net< / a > < / li >
< / ul >
< h2 id = "resnest-resnestpy" > ResNeSt [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnest.py" > resnest.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > ResNeSt: Split-Attention Networks< / code > - < a href = "https://arxiv.org/abs/2004.08955" > https://arxiv.org/abs/2004.08955< / a > < / li >
< li > Code: < a href = "https://github.com/zhanghang1989/ResNeSt" > https://github.com/zhanghang1989/ResNeSt< / a > < / li >
< / ul >
< h2 id = "rexnet-rexnetpy" > ReXNet [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/rexnet.py" > rexnet.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > ReXNet: Diminishing Representational Bottleneck on CNN< / code > - < a href = "https://arxiv.org/abs/2007.00992" > https://arxiv.org/abs/2007.00992< / a > < / li >
< li > Code: < a href = "https://github.com/clovaai/rexnet" > https://github.com/clovaai/rexnet< / a > < / li >
< / ul >
< h2 id = "selective-kernel-networks-sknetpy" > Selective-Kernel Networks [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/sknet.py" > sknet.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > Selective-Kernel Networks< / code > - < a href = "https://arxiv.org/abs/1903.06586" > https://arxiv.org/abs/1903.06586< / a > < / li >
< li > Code: < a href = "https://github.com/implus/SKNet" > https://github.com/implus/SKNet< / a > , < a href = "https://github.com/clovaai/assembled-cnn" > https://github.com/clovaai/assembled-cnn< / a > < / li >
< / ul >
< h2 id = "selecsls-selecslspy" > SelecSLS [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/selecsls.py" > selecsls.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera< / code > - < a href = "https://arxiv.org/abs/1907.00837" > https://arxiv.org/abs/1907.00837< / a > < / li >
< li > Code: < a href = "https://github.com/mehtadushy/SelecSLS-Pytorch" > https://github.com/mehtadushy/SelecSLS-Pytorch< / a > < / li >
< / ul >
< h2 id = "squeeze-and-excitation-networks-senetpy" > Squeeze-and-Excitation Networks [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/senet.py" > senet.py< / a > ]< / h2 >
< p > NOTE: I am deprecating this version of the networks, the new ones are part of < code > resnet.py< / code > < / p >
< ul >
< li > Paper: < code > Squeeze-and-Excitation Networks< / code > - < a href = "https://arxiv.org/abs/1709.01507" > https://arxiv.org/abs/1709.01507< / a > < / li >
< li > Code: < a href = "https://github.com/Cadene/pretrained-models.pytorch" > https://github.com/Cadene/pretrained-models.pytorch< / a > < / li >
< / ul >
< h2 id = "tresnet-tresnetpy" > TResNet [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/tresnet.py" > tresnet.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > TResNet: High Performance GPU-Dedicated Architecture< / code > - < a href = "https://arxiv.org/abs/2003.13630" > https://arxiv.org/abs/2003.13630< / a > < / li >
< li > Code: < a href = "https://github.com/mrT23/TResNet" > https://github.com/mrT23/TResNet< / a > < / li >
< / ul >
< h2 id = "vgg-vggpy" > VGG [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vgg.py" > vgg.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > Very Deep Convolutional Networks For Large-Scale Image Recognition< / code > - < a href = "https://arxiv.org/pdf/1409.1556.pdf" > https://arxiv.org/pdf/1409.1556.pdf< / a > < / li >
< li > Reference code: < a href = "https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py" > https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py< / a > < / li >
< / ul >
< h2 id = "vision-transformer-vision_transformerpy" > Vision Transformer [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py" > vision_transformer.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale< / code > - < a href = "https://arxiv.org/abs/2010.11929" > https://arxiv.org/abs/2010.11929< / a > < / li >
< li > Reference code and pretrained weights: < a href = "https://github.com/google-research/vision_transformer" > https://github.com/google-research/vision_transformer< / a > < / li >
< / ul >
< h2 id = "vovnet-v2-and-v1-vovnetpy" > VovNet V2 and V1 [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vovnet.py" > vovnet.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > CenterMask : Real-Time Anchor-Free Instance Segmentation< / code > - < a href = "https://arxiv.org/abs/1911.06667" > https://arxiv.org/abs/1911.06667< / a > < / li >
< li > Reference code: < a href = "https://github.com/youngwanLEE/vovnet-detectron2" > https://github.com/youngwanLEE/vovnet-detectron2< / a > < / li >
< / ul >
< h2 id = "xception-xceptionpy" > Xception [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/xception.py" > xception.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > Xception: Deep Learning with Depthwise Separable Convolutions< / code > - < a href = "https://arxiv.org/abs/1610.02357" > https://arxiv.org/abs/1610.02357< / a > < / li >
< li > Code: < a href = "https://github.com/Cadene/pretrained-models.pytorch" > https://github.com/Cadene/pretrained-models.pytorch< / a > < / li >
< / ul >
< h2 id = "xception-modified-aligned-gluon-gluon_xceptionpy" > Xception (Modified Aligned, Gluon) [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/gluon_xception.py" > gluon_xception.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation< / code > - < a href = "https://arxiv.org/abs/1802.02611" > https://arxiv.org/abs/1802.02611< / a > < / li >
< li > Reference code: < a href = "https://github.com/dmlc/gluon-cv/tree/master/gluoncv/model_zoo" > https://github.com/dmlc/gluon-cv/tree/master/gluoncv/model_zoo< / a > , < a href = "https://github.com/jfzhang95/pytorch-deeplab-xception/" > https://github.com/jfzhang95/pytorch-deeplab-xception/< / a > < / li >
< / ul >
< h2 id = "xception-modified-aligned-tf-aligned_xceptionpy" > Xception (Modified Aligned, TF) [< a href = "https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/aligned_xception.py" > aligned_xception.py< / a > ]< / h2 >
< ul >
< li > Paper: < code > Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation< / code > - < a href = "https://arxiv.org/abs/1802.02611" > https://arxiv.org/abs/1802.02611< / a > < / li >
< li > Reference code: < a href = "https://github.com/tensorflow/models/tree/master/research/deeplab" > https://github.com/tensorflow/models/tree/master/research/deeplab< / a > < / li >
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