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Getting Started
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Model Summaries
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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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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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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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(Gluon) SE-ResNeXt
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(Gluon) Xception
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HRNet
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Instagram ResNeXt WSL
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Inception ResNet v2
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Inception v3
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Inception v4
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(Legacy) SE-ResNet
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(Legacy) SE-ResNeXt
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(Legacy) SENet
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MixNet
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MnasNet
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MobileNet v2
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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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Res2NeXt
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ResNeSt
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ResNet-D
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ResNet-D
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How do I use this model on an image?
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How do I finetune this model?
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How do I train this model?
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Citation
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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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SelecSLS
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SE-ResNeXt
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SK-ResNet
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SK-ResNeXt
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SPNASNet
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SSL ResNet
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SSL ResNeXT
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SWSL ResNet
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SWSL ResNeXt
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(Tensorflow) EfficientNet CondConv
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(Tensorflow) EfficientNet Lite
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(Tensorflow) EfficientNet
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(Tensorflow) Inception v3
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(Tensorflow) MixNet
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(Tensorflow) MobileNet v3
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TResNet
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Vision Transformer (ViT)
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Wide ResNet
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Xception
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Scripts
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Training Examples
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Feature Extraction
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Recent Changes
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Archived Changes
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How do I use this model on an image?
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How do I finetune this model?
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< a href = "https://github.com/rwightman/pytorch-image-models/edit/master/docs/models/resnet-d.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 = "resnet-d" > ResNet-D< / h1 >
< p > < strong > ResNet-D< / strong > is a modification on the < a href = "https://paperswithcode.com/method/resnet" > ResNet< / a > architecture that utilises an < a href = "https://paperswithcode.com/method/average-pooling" > average pooling< / a > tweak for downsampling. The motivation is that in the unmodified ResNet, the < a href = "https://paperswithcode.com/method/1x1-convolution" > 1× < / a > for the downsampling block ignores ¾ of input feature maps, so this is modified so no information will be ignored< / p >
< h2 id = "how-do-i-use-this-model-on-an-image" > How do I use this model on an image?< / h2 >
< p > To load a pretrained model:< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "kn" > import< / span > < span class = "nn" > timm< / span >
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > timm< / span > < span class = "o" > .< / span > < span class = "n" > create_model< / span > < span class = "p" > (< / span > < span class = "s1" > ' resnet101d' < / span > < span class = "p" > ,< / span > < span class = "n" > pretrained< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )< / span >
< span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > eval< / span > < span class = "p" > ()< / span >
< / code > < / pre > < / div >
< p > To load and preprocess the image:
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "kn" > import< / span > < span class = "nn" > urllib< / span >
< span class = "kn" > from< / span > < span class = "nn" > PIL< / span > < span class = "kn" > import< / span > < span class = "n" > Image< / span >
< span class = "kn" > from< / span > < span class = "nn" > timm.data< / span > < span class = "kn" > import< / span > < span class = "n" > resolve_data_config< / span >
< span class = "kn" > from< / span > < span class = "nn" > timm.data.transforms_factory< / span > < span class = "kn" > import< / span > < span class = "n" > create_transform< / span >
< span class = "n" > config< / span > < span class = "o" > =< / span > < span class = "n" > resolve_data_config< / span > < span class = "p" > ({},< / span > < span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > model< / span > < span class = "p" > )< / span >
< span class = "n" > transform< / span > < span class = "o" > =< / span > < span class = "n" > create_transform< / span > < span class = "p" > (< / span > < span class = "o" > **< / span > < span class = "n" > config< / span > < span class = "p" > )< / span >
< span class = "n" > url< / span > < span class = "p" > ,< / span > < span class = "n" > filename< / span > < span class = "o" > =< / span > < span class = "p" > (< / span > < span class = "s2" > " https://github.com/pytorch/hub/raw/master/images/dog.jpg" < / span > < span class = "p" > ,< / span > < span class = "s2" > " dog.jpg" < / span > < span class = "p" > )< / span >
< span class = "n" > urllib< / span > < span class = "o" > .< / span > < span class = "n" > request< / span > < span class = "o" > .< / span > < span class = "n" > urlretrieve< / span > < span class = "p" > (< / span > < span class = "n" > url< / span > < span class = "p" > ,< / span > < span class = "n" > filename< / span > < span class = "p" > )< / span >
< span class = "n" > img< / span > < span class = "o" > =< / span > < span class = "n" > Image< / span > < span class = "o" > .< / span > < span class = "n" > open< / span > < span class = "p" > (< / span > < span class = "n" > filename< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "n" > convert< / span > < span class = "p" > (< / span > < span class = "s1" > ' RGB' < / span > < span class = "p" > )< / span >
< span class = "n" > tensor< / span > < span class = "o" > =< / span > < span class = "n" > transform< / span > < span class = "p" > (< / span > < span class = "n" > img< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "n" > unsqueeze< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > )< / span > < span class = "c1" > # transform and add batch dimension< / span >
< / code > < / pre > < / div > < / p >
< p > To get the model predictions:
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "kn" > import< / span > < span class = "nn" > torch< / span >
< span class = "k" > with< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > no_grad< / span > < span class = "p" > ():< / span >
< span class = "n" > out< / span > < span class = "o" > =< / span > < span class = "n" > model< / span > < span class = "p" > (< / span > < span class = "n" > tensor< / span > < span class = "p" > )< / span >
< span class = "n" > probabilities< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > functional< / span > < span class = "o" > .< / span > < span class = "n" > softmax< / span > < span class = "p" > (< / span > < span class = "n" > out< / span > < span class = "p" > [< / span > < span class = "mi" > 0< / span > < span class = "p" > ],< / span > < span class = "n" > dim< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span > < span class = "p" > )< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > probabilities< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span > < span class = "p" > )< / span >
< span class = "c1" > # prints: torch.Size([1000])< / span >
< / code > < / pre > < / div > < / p >
< p > To get the top-5 predictions class names:
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "c1" > # Get imagenet class mappings< / span >
< span class = "n" > url< / span > < span class = "p" > ,< / span > < span class = "n" > filename< / span > < span class = "o" > =< / span > < span class = "p" > (< / span > < span class = "s2" > " https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt" < / span > < span class = "p" > ,< / span > < span class = "s2" > " imagenet_classes.txt" < / span > < span class = "p" > )< / span >
< span class = "n" > urllib< / span > < span class = "o" > .< / span > < span class = "n" > request< / span > < span class = "o" > .< / span > < span class = "n" > urlretrieve< / span > < span class = "p" > (< / span > < span class = "n" > url< / span > < span class = "p" > ,< / span > < span class = "n" > filename< / span > < span class = "p" > )< / span >
< span class = "k" > with< / span > < span class = "nb" > open< / span > < span class = "p" > (< / span > < span class = "s2" > " imagenet_classes.txt" < / span > < span class = "p" > ,< / span > < span class = "s2" > " r" < / span > < span class = "p" > )< / span > < span class = "k" > as< / span > < span class = "n" > f< / span > < span class = "p" > :< / span >
< span class = "n" > categories< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "n" > s< / span > < span class = "o" > .< / span > < span class = "n" > strip< / span > < span class = "p" > ()< / span > < span class = "k" > for< / span > < span class = "n" > s< / span > < span class = "ow" > in< / span > < span class = "n" > f< / span > < span class = "o" > .< / span > < span class = "n" > readlines< / span > < span class = "p" > ()]< / span >
< span class = "c1" > # Print top categories per image< / span >
< span class = "n" > top5_prob< / span > < span class = "p" > ,< / span > < span class = "n" > top5_catid< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > topk< / span > < span class = "p" > (< / span > < span class = "n" > probabilities< / span > < span class = "p" > ,< / span > < span class = "mi" > 5< / span > < span class = "p" > )< / span >
< span class = "k" > for< / span > < span class = "n" > i< / span > < span class = "ow" > in< / span > < span class = "nb" > range< / span > < span class = "p" > (< / span > < span class = "n" > top5_prob< / span > < span class = "o" > .< / span > < span class = "n" > size< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > )):< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > categories< / span > < span class = "p" > [< / span > < span class = "n" > top5_catid< / span > < span class = "p" > [< / span > < span class = "n" > i< / span > < span class = "p" > ]],< / span > < span class = "n" > top5_prob< / span > < span class = "p" > [< / span > < span class = "n" > i< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > item< / span > < span class = "p" > ())< / span >
< span class = "c1" > # prints class names and probabilities like:< / span >
< span class = "c1" > # [(' Samoyed' , 0.6425196528434753), (' Pomeranian' , 0.04062102362513542), (' keeshond' , 0.03186424449086189), (' white wolf' , 0.01739676296710968), (' Eskimo dog' , 0.011717947199940681)]< / span >
< / code > < / pre > < / div > < / p >
< p > Replace the model name with the variant you want to use, e.g. < code > resnet101d< / code > . You can find the IDs in the model summaries at the top of this page.< / p >
< p > To extract image features with this model, follow the < a href = "https://rwightman.github.io/pytorch-image-models/feature_extraction/" > timm feature extraction examples< / a > , just change the name of the model you want to use.< / p >
< h2 id = "how-do-i-finetune-this-model" > How do I finetune this model?< / h2 >
< p > You can finetune any of the pre-trained models just by changing the classifier (the last layer).
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > timm< / span > < span class = "o" > .< / span > < span class = "n" > create_model< / span > < span class = "p" > (< / span > < span class = "s1" > ' resnet101d' < / span > < span class = "p" > ,< / span > < span class = "n" > pretrained< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > ,< / span > < span class = "n" > num_classes< / span > < span class = "o" > =< / span > < span class = "n" > NUM_FINETUNE_CLASSES< / span > < span class = "p" > )< / span >
< / code > < / pre > < / div >
To finetune on your own dataset, you have to write a training loop or adapt < a href = "https://github.com/rwightman/pytorch-image-models/blob/master/train.py" > timm's training
script< / a > to use your dataset.< / p >
< h2 id = "how-do-i-train-this-model" > How do I train this model?< / h2 >
< p > You can follow the < a href = "https://rwightman.github.io/pytorch-image-models/scripts/" > timm recipe scripts< / a > for training a new model afresh.< / p >
< h2 id = "citation" > Citation< / h2 >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "nc" > @misc< / span > < span class = "p" > {< / span > < span class = "nl" > he2018bag< / span > < span class = "p" > ,< / span > < span class = "w" > < / span >
< span class = "w" > < / span > < span class = "na" > title< / span > < span class = "p" > =< / span > < span class = "s" > {Bag of Tricks for Image Classification with Convolutional Neural Networks}< / span > < span class = "p" > ,< / span > < span class = "w" > < / span >
< span class = "w" > < / span > < span class = "na" > author< / span > < span class = "p" > =< / span > < span class = "s" > {Tong He and Zhi Zhang and Hang Zhang and Zhongyue Zhang and Junyuan Xie and Mu Li}< / span > < span class = "p" > ,< / span > < span class = "w" > < / span >
< span class = "w" > < / span > < span class = "na" > year< / span > < span class = "p" > =< / span > < span class = "s" > {2018}< / span > < span class = "p" > ,< / span > < span class = "w" > < / span >
< span class = "w" > < / span > < span class = "na" > eprint< / span > < span class = "p" > =< / span > < span class = "s" > {1812.01187}< / span > < span class = "p" > ,< / span > < span class = "w" > < / span >
< span class = "w" > < / span > < span class = "na" > archivePrefix< / span > < span class = "p" > =< / span > < span class = "s" > {arXiv}< / span > < span class = "p" > ,< / span > < span class = "w" > < / span >
< span class = "w" > < / span > < span class = "na" > primaryClass< / span > < span class = "p" > =< / span > < span class = "s" > {cs.CV}< / span > < span class = "w" > < / span >
< span class = "p" > }< / span > < span class = "w" > < / span >
< / code > < / pre > < / div >
<!--
Type: model-index
Collections:
- Name: ResNet-D
Paper:
Title: Bag of Tricks for Image Classification with Convolutional Neural Networks
URL: https://paperswithcode.com/paper/bag-of-tricks-for-image-classification-with
Models:
- Name: resnet101d
In Collection: ResNet-D
Metadata:
FLOPs: 13805639680
Parameters: 44570000
File Size: 178791263
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet101d
Crop Pct: '0.94'
Image Size: '256'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L716
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet101d_ra2-2803ffab.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 82.31%
Top 5 Accuracy: 96.06%
- Name: resnet152d
In Collection: ResNet-D
Metadata:
FLOPs: 20155275264
Parameters: 60210000
File Size: 241596837
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet152d
Crop Pct: '0.94'
Image Size: '256'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L724
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet152d_ra2-5cac0439.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.13%
Top 5 Accuracy: 96.35%
- Name: resnet18d
In Collection: ResNet-D
Metadata:
FLOPs: 2645205760
Parameters: 11710000
File Size: 46893231
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet18d
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L649
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet18d_ra2-48a79e06.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 72.27%
Top 5 Accuracy: 90.69%
- Name: resnet200d
In Collection: ResNet-D
Metadata:
FLOPs: 26034378752
Parameters: 64690000
File Size: 259662933
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet200d
Crop Pct: '0.94'
Image Size: '256'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L749
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet200d_ra2-bdba9bf9.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.24%
Top 5 Accuracy: 96.49%
- Name: resnet26d
In Collection: ResNet-D
Metadata:
FLOPs: 3335276032
Parameters: 16010000
File Size: 64209122
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet26d
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L683
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26d-69e92c46.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 76.69%
Top 5 Accuracy: 93.15%
- Name: resnet34d
In Collection: ResNet-D
Metadata:
FLOPs: 5026601728
Parameters: 21820000
File Size: 87369807
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet34d
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L666
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34d_ra2-f8dcfcaf.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.11%
Top 5 Accuracy: 93.38%
- Name: resnet50d
In Collection: ResNet-D
Metadata:
FLOPs: 5591002624
Parameters: 25580000
File Size: 102567109
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet50d
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L699
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50d_ra2-464e36ba.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.55%
Top 5 Accuracy: 95.16%
-->
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