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Model Architectures
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Self-trained Weights
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Ported and Other Weights
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Training Examples
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Feature Extraction
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Models
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Models
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< a href = "../models/adversarial-inception-v3/" class = "md-nav__link" >
Adversarial Inception v3
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< a href = "../models/advprop/" class = "md-nav__link" >
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 = "../models/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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< a href = "../models/ecaresnet/" class = "md-nav__link" >
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 = "../models/ese-vovnet/" class = "md-nav__link" >
ESE-VoVNet
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< a href = "../models/fbnet/" class = "md-nav__link" >
FBNet
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< a href = "../models/gloun-inception-v3/" class = "md-nav__link" >
(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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(Gluon) SE-ResNeXt
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(Gluon) Xception
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< a href = "../models/hrnet/" class = "md-nav__link" >
HRNet
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< a href = "../models/ig-resnext/" class = "md-nav__link" >
Instagram ResNeXt WSL
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< a href = "../models/inception-resnet-v2/" class = "md-nav__link" >
Inception ResNet v2
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Inception v3
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< a href = "../models/inception-v4/" class = "md-nav__link" >
Inception v4
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< a href = "../models/legacy-se-resnet/" class = "md-nav__link" >
(Legacy) SE-ResNet
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(Legacy) SE-ResNeXt
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(Legacy) SENet
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< a href = "../models/mixnet/" class = "md-nav__link" >
MixNet
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MnasNet
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MobileNet v2
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< a href = "../models/mobilenet-v3/" class = "md-nav__link" >
MobileNet v3
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< a href = "../models/nasnet/" class = "md-nav__link" >
NASNet
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< a href = "../models/noisy-student/" class = "md-nav__link" >
Noisy Student (EfficientNet)
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PNASNet
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< a href = "../models/regnetx/" class = "md-nav__link" >
RegNetX
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< a href = "../models/regnety/" class = "md-nav__link" >
RegNetY
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< a href = "../models/res2net/" class = "md-nav__link" >
Res2Net
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< a href = "../models/res2next/" class = "md-nav__link" >
Res2NeXt
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< a href = "../models/resnest/" class = "md-nav__link" >
ResNeSt
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< a href = "../models/resnet-d/" class = "md-nav__link" >
ResNet-D
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ResNet
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ResNeXt
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RexNet
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SE-ResNet
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< a href = "../models/selecsls/" class = "md-nav__link" >
SelecSLS
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SE-ResNeXt
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< a href = "../models/skresnet/" class = "md-nav__link" >
SK-ResNet
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< a href = "../models/skresnext/" class = "md-nav__link" >
SK-ResNeXt
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< a href = "../models/spnasnet/" class = "md-nav__link" >
SPNASNet
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< a href = "../models/ssl-resnet/" class = "md-nav__link" >
SSL ResNet
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< a href = "../models/ssl-resnext/" class = "md-nav__link" >
SSL ResNeXT
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< a href = "../models/swsl-resnet/" class = "md-nav__link" >
SWSL ResNet
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< a href = "../models/swsl-resnext/" class = "md-nav__link" >
SWSL ResNeXt
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< a href = "../models/tf-efficientnet-condconv/" class = "md-nav__link" >
(Tensorflow) EfficientNet CondConv
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< a href = "../models/tf-efficientnet-lite/" class = "md-nav__link" >
(Tensorflow) EfficientNet Lite
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< a href = "../models/tf-efficientnet/" class = "md-nav__link" >
(Tensorflow) EfficientNet
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< a href = "../models/tf-inception-v3/" class = "md-nav__link" >
(Tensorflow) Inception v3
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(Tensorflow) MixNet
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(Tensorflow) MobileNet v3
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< a href = "../models/tresnet/" class = "md-nav__link" >
TResNet
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Vision Transformer (ViT)
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Wide ResNet
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Xception
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Self-trained Weights
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Ported and Other Weights
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< h1 id = "results" > Results< / h1 >
< p > CSV files containing an ImageNet-1K and out-of-distribution (OOD) test set validation results for all models with pretrained weights is located in the repository < a href = "https://github.com/rwightman/pytorch-image-models/tree/master/results" > results folder< / a > .< / p >
< h2 id = "self-trained-weights" > Self-trained Weights< / h2 >
< p > The table below includes ImageNet-1k validation results of model weights that I've trained myself. It is not updated as frequently as the csv results outputs linked above.< / p >
< table >
< thead >
< tr >
< th > Model< / th >
< th > Acc@1 (Err)< / th >
< th > Acc@5 (Err)< / th >
< th > Param # (M)< / th >
< th > Interpolation< / th >
< th > Image Size< / th >
< / tr >
< / thead >
< tbody >
< tr >
< td > efficientnet_b3a< / td >
< td > 82.242 (17.758)< / td >
< td > 96.114 (3.886)< / td >
< td > 12.23< / td >
< td > bicubic< / td >
< td > 320 (1.0 crop)< / td >
< / tr >
< tr >
< td > efficientnet_b3< / td >
< td > 82.076 (17.924)< / td >
< td > 96.020 (3.980)< / td >
< td > 12.23< / td >
< td > bicubic< / td >
< td > 300< / td >
< / tr >
< tr >
< td > regnet_32< / td >
< td > 82.002 (17.998)< / td >
< td > 95.906 (4.094)< / td >
< td > 19.44< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > skresnext50d_32x4d< / td >
< td > 81.278 (18.722)< / td >
< td > 95.366 (4.634)< / td >
< td > 27.5< / td >
< td > bicubic< / td >
< td > 288 (1.0 crop)< / td >
< / tr >
< tr >
< td > seresnext50d_32x4d< / td >
< td > 81.266 (18.734)< / td >
< td > 95.620 (4.380)< / td >
< td > 27.6< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > efficientnet_b2a< / td >
< td > 80.608 (19.392)< / td >
< td > 95.310 (4.690)< / td >
< td > 9.11< / td >
< td > bicubic< / td >
< td > 288 (1.0 crop)< / td >
< / tr >
< tr >
< td > resnet50d< / td >
< td > 80.530 (19.470)< / td >
< td > 95.160 (4.840)< / td >
< td > 25.6< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > mixnet_xl< / td >
< td > 80.478 (19.522)< / td >
< td > 94.932 (5.068)< / td >
< td > 11.90< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > efficientnet_b2< / td >
< td > 80.402 (19.598)< / td >
< td > 95.076 (4.924)< / td >
< td > 9.11< / td >
< td > bicubic< / td >
< td > 260< / td >
< / tr >
< tr >
< td > seresnet50< / td >
< td > 80.274 (19.726)< / td >
< td > 95.070 (4.930)< / td >
< td > 28.1< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > skresnext50d_32x4d< / td >
< td > 80.156 (19.844)< / td >
< td > 94.642 (5.358)< / td >
< td > 27.5< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > cspdarknet53< / td >
< td > 80.058 (19.942)< / td >
< td > 95.084 (4.916)< / td >
< td > 27.6< / td >
< td > bicubic< / td >
< td > 256< / td >
< / tr >
< tr >
< td > cspresnext50< / td >
< td > 80.040 (19.960)< / td >
< td > 94.944 (5.056)< / td >
< td > 20.6< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > resnext50_32x4d< / td >
< td > 79.762 (20.238)< / td >
< td > 94.600 (5.400)< / td >
< td > 25< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > resnext50d_32x4d< / td >
< td > 79.674 (20.326)< / td >
< td > 94.868 (5.132)< / td >
< td > 25.1< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > cspresnet50< / td >
< td > 79.574 (20.426)< / td >
< td > 94.712 (5.288)< / td >
< td > 21.6< / td >
< td > bicubic< / td >
< td > 256< / td >
< / tr >
< tr >
< td > ese_vovnet39b< / td >
< td > 79.320 (20.680)< / td >
< td > 94.710 (5.290)< / td >
< td > 24.6< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > resnetblur50< / td >
< td > 79.290 (20.710)< / td >
< td > 94.632 (5.368)< / td >
< td > 25.6< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > dpn68b< / td >
< td > 79.216 (20.784)< / td >
< td > 94.414 (5.586)< / td >
< td > 12.6< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > resnet50< / td >
< td > 79.038 (20.962)< / td >
< td > 94.390 (5.610)< / td >
< td > 25.6< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > mixnet_l< / td >
< td > 78.976 (21.024< / td >
< td > 94.184 (5.816)< / td >
< td > 7.33< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > efficientnet_b1< / td >
< td > 78.692 (21.308)< / td >
< td > 94.086 (5.914)< / td >
< td > 7.79< / td >
< td > bicubic< / td >
< td > 240< / td >
< / tr >
< tr >
< td > efficientnet_es< / td >
< td > 78.066 (21.934)< / td >
< td > 93.926 (6.074)< / td >
< td > 5.44< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > seresnext26t_32x4d< / td >
< td > 77.998 (22.002)< / td >
< td > 93.708 (6.292)< / td >
< td > 16.8< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > seresnext26tn_32x4d< / td >
< td > 77.986 (22.014)< / td >
< td > 93.746 (6.254)< / td >
< td > 16.8< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > efficientnet_b0< / td >
< td > 77.698 (22.302)< / td >
< td > 93.532 (6.468)< / td >
< td > 5.29< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > seresnext26d_32x4d< / td >
< td > 77.602 (22.398)< / td >
< td > 93.608 (6.392)< / td >
< td > 16.8< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > mobilenetv2_120d< / td >
< td > 77.294 (22.706< / td >
< td > 93.502 (6.498)< / td >
< td > 5.8< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > mixnet_m< / td >
< td > 77.256 (22.744)< / td >
< td > 93.418 (6.582)< / td >
< td > 5.01< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > resnet34d< / td >
< td > 77.116 (22.884)< / td >
< td > 93.382 (6.618)< / td >
< td > 21.8< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > seresnext26_32x4d< / td >
< td > 77.104 (22.896)< / td >
< td > 93.316 (6.684)< / td >
< td > 16.8< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > skresnet34< / td >
< td > 76.912 (23.088)< / td >
< td > 93.322 (6.678)< / td >
< td > 22.2< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > ese_vovnet19b_dw< / td >
< td > 76.798 (23.202)< / td >
< td > 93.268 (6.732)< / td >
< td > 6.5< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > resnet26d< / td >
< td > 76.68 (23.32)< / td >
< td > 93.166 (6.834)< / td >
< td > 16< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > densenetblur121d< / td >
< td > 76.576 (23.424)< / td >
< td > 93.190 (6.810)< / td >
< td > 8.0< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > mobilenetv2_140< / td >
< td > 76.524 (23.476)< / td >
< td > 92.990 (7.010)< / td >
< td > 6.1< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > mixnet_s< / td >
< td > 75.988 (24.012)< / td >
< td > 92.794 (7.206)< / td >
< td > 4.13< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > mobilenetv3_large_100< / td >
< td > 75.766 (24.234)< / td >
< td > 92.542 (7.458)< / td >
< td > 5.5< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > mobilenetv3_rw< / td >
< td > 75.634 (24.366)< / td >
< td > 92.708 (7.292)< / td >
< td > 5.5< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > mnasnet_a1< / td >
< td > 75.448 (24.552)< / td >
< td > 92.604 (7.396)< / td >
< td > 3.89< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > resnet26< / td >
< td > 75.292 (24.708)< / td >
< td > 92.57 (7.43)< / td >
< td > 16< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > fbnetc_100< / td >
< td > 75.124 (24.876)< / td >
< td > 92.386 (7.614)< / td >
< td > 5.6< / td >
< td > bilinear< / td >
< td > 224< / td >
< / tr >
< tr >
< td > resnet34< / td >
< td > 75.110 (24.890)< / td >
< td > 92.284 (7.716)< / td >
< td > 22< / td >
< td > bilinear< / td >
< td > 224< / td >
< / tr >
< tr >
< td > mobilenetv2_110d< / td >
< td > 75.052 (24.948)< / td >
< td > 92.180 (7.820)< / td >
< td > 4.5< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > seresnet34< / td >
< td > 74.808 (25.192)< / td >
< td > 92.124 (7.876)< / td >
< td > 22< / td >
< td > bilinear< / td >
< td > 224< / td >
< / tr >
< tr >
< td > mnasnet_b1< / td >
< td > 74.658 (25.342)< / td >
< td > 92.114 (7.886)< / td >
< td > 4.38< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > spnasnet_100< / td >
< td > 74.084 (25.916)< / td >
< td > 91.818 (8.182)< / td >
< td > 4.42< / td >
< td > bilinear< / td >
< td > 224< / td >
< / tr >
< tr >
< td > skresnet18< / td >
< td > 73.038 (26.962)< / td >
< td > 91.168 (8.832)< / td >
< td > 11.9< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > mobilenetv2_100< / td >
< td > 72.978 (27.022)< / td >
< td > 91.016 (8.984)< / td >
< td > 3.5< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > resnet18d< / td >
< td > 72.260 (27.740)< / td >
< td > 90.696 (9.304)< / td >
< td > 11.7< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< tr >
< td > seresnet18< / td >
< td > 71.742 (28.258)< / td >
< td > 90.334 (9.666)< / td >
< td > 11.8< / td >
< td > bicubic< / td >
< td > 224< / td >
< / tr >
< / tbody >
< / table >
< h2 id = "ported-and-other-weights" > Ported and Other Weights< / h2 >
< p > For weights ported from other deep learning frameworks (Tensorflow, MXNet GluonCV) or copied from other PyTorch sources, please see the full results tables for ImageNet and various OOD test sets at in the < a href = "https://github.com/rwightman/pytorch-image-models/tree/master/results" > results tables< / a > .< / p >
< p > Model code .py files contain links to original sources of models and weights.< / p >
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