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Adversarial Inception v3
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AdvProp (EfficientNet)
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CSP-DarkNet
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CSP-ResNet
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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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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
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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) EfficientNet
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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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(Tensorflow) Inception v3
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(Tensorflow) MixNet
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(Tensorflow) MobileNet v3
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< a href = "../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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How do I use this model on an image?
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< h1 id = "tensorflow-efficientnet" > (Tensorflow) EfficientNet< / h1 >
< p > < strong > EfficientNet< / strong > is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a < em > compound coefficient< / em > . Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use < span class = "arithmatex" > < span class = "MathJax_Preview" > 2^N< / span > < script type = "math/tex" > 2 ^ N < / script > < / span > times more computational resources, then we can simply increase the network depth by < span class = "arithmatex" > < span class = "MathJax_Preview" > \alpha ^ N< / span > < script type = "math/tex" > \ alpha ^ N < / script > < / span > , width by < span class = "arithmatex" > < span class = "MathJax_Preview" > \beta ^ N< / span > < script type = "math/tex" > \ beta ^ N < / script > < / span > , and image size by < span class = "arithmatex" > < span class = "MathJax_Preview" > \gamma ^ N< / span > < script type = "math/tex" > \ gamma ^ N < / script > < / span > , where < span class = "arithmatex" > < span class = "MathJax_Preview" > \alpha, \beta, \gamma< / span > < script type = "math/tex" > \ alpha , \ beta , \ gamma < / script > < / span > are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient < span class = "arithmatex" > < span class = "MathJax_Preview" > \phi< / span > < script type = "math/tex" > \ phi < / script > < / span > to uniformly scales network width, depth, and resolution in a principled way.< / p >
< p > The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image.< / p >
< p > The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of < a href = "https://paperswithcode.com/method/mobilenetv2" > MobileNetV2< / a > , in addition to < a href = "https://paperswithcode.com/method/squeeze-and-excitation-block" > squeeze-and-excitation blocks< / a > .< / p >
< p > The weights from this model were ported from < a href = "https://github.com/tensorflow/tpu" > Tensorflow/TPU< / a > .< / 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" > ' tf_efficientnet_b0' < / 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 > tf_efficientnet_b0< / 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" > ' tf_efficientnet_b0' < / 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" > tan2020efficientnet< / span > < span class = "p" > ,< / span >
< span class = "w" > < / span > < span class = "na" > title< / span > < span class = "p" > =< / span > < span class = "s" > {EfficientNet: Rethinking Model Scaling for 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" > {Mingxing Tan and Quoc V. Le}< / span > < span class = "p" > ,< / span >
< span class = "w" > < / span > < span class = "na" > year< / span > < span class = "p" > =< / span > < span class = "s" > {2020}< / span > < span class = "p" > ,< / span >
< span class = "w" > < / span > < span class = "na" > eprint< / span > < span class = "p" > =< / span > < span class = "s" > {1905.11946}< / span > < span class = "p" > ,< / 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 = "na" > primaryClass< / span > < span class = "p" > =< / span > < span class = "s" > {cs.LG}< / span >
< span class = "p" > }< / span >
< / code > < / pre > < / div >
<!--
Type: model-index
Collections:
- Name: TF EfficientNet
Paper:
Title: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks'
URL: https://paperswithcode.com/paper/efficientnet-rethinking-model-scaling-for
Models:
- Name: tf_efficientnet_b0
In Collection: TF EfficientNet
Metadata:
FLOPs: 488688572
Parameters: 5290000
File Size: 21383997
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
Training Resources: TPUv3 Cloud TPU
ID: tf_efficientnet_b0
LR: 0.256
Epochs: 350
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 2048
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1241
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 76.85%
Top 5 Accuracy: 93.23%
- Name: tf_efficientnet_b1
In Collection: TF EfficientNet
Metadata:
FLOPs: 883633200
Parameters: 7790000
File Size: 31512534
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_b1
LR: 0.256
Epochs: 350
Crop Pct: '0.882'
Momentum: 0.9
Batch Size: 2048
Image Size: '240'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1251
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_aa-ea7a6ee0.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.84%
Top 5 Accuracy: 94.2%
- Name: tf_efficientnet_b2
In Collection: TF EfficientNet
Metadata:
FLOPs: 1234321170
Parameters: 9110000
File Size: 36797929
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_b2
LR: 0.256
Epochs: 350
Crop Pct: '0.89'
Momentum: 0.9
Batch Size: 2048
Image Size: '260'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1261
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_aa-60c94f97.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.07%
Top 5 Accuracy: 94.9%
- Name: tf_efficientnet_b3
In Collection: TF EfficientNet
Metadata:
FLOPs: 2275247568
Parameters: 12230000
File Size: 49381362
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_b3
LR: 0.256
Epochs: 350
Crop Pct: '0.904'
Momentum: 0.9
Batch Size: 2048
Image Size: '300'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1271
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_aa-84b4657e.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.65%
Top 5 Accuracy: 95.72%
- Name: tf_efficientnet_b4
In Collection: TF EfficientNet
Metadata:
FLOPs: 5749638672
Parameters: 19340000
File Size: 77989689
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
Training Resources: TPUv3 Cloud TPU
ID: tf_efficientnet_b4
LR: 0.256
Epochs: 350
Crop Pct: '0.922'
Momentum: 0.9
Batch Size: 2048
Image Size: '380'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1281
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_aa-818f208c.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.03%
Top 5 Accuracy: 96.3%
- Name: tf_efficientnet_b5
In Collection: TF EfficientNet
Metadata:
FLOPs: 13176501888
Parameters: 30390000
File Size: 122403150
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_b5
LR: 0.256
Epochs: 350
Crop Pct: '0.934'
Momentum: 0.9
Batch Size: 2048
Image Size: '456'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1291
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ra-9a3e5369.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.81%
Top 5 Accuracy: 96.75%
- Name: tf_efficientnet_b6
In Collection: TF EfficientNet
Metadata:
FLOPs: 24180518488
Parameters: 43040000
File Size: 173232007
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_b6
LR: 0.256
Epochs: 350
Crop Pct: '0.942'
Momentum: 0.9
Batch Size: 2048
Image Size: '528'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1301
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_aa-80ba17e4.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 84.11%
Top 5 Accuracy: 96.89%
- Name: tf_efficientnet_b7
In Collection: TF EfficientNet
Metadata:
FLOPs: 48205304880
Parameters: 66349999
File Size: 266850607
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_b7
LR: 0.256
Epochs: 350
Crop Pct: '0.949'
Momentum: 0.9
Batch Size: 2048
Image Size: '600'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1312
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ra-6c08e654.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 84.93%
Top 5 Accuracy: 97.2%
- Name: tf_efficientnet_b8
In Collection: TF EfficientNet
Metadata:
FLOPs: 80962956270
Parameters: 87410000
File Size: 351379853
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_b8
LR: 0.256
Epochs: 350
Crop Pct: '0.954'
Momentum: 0.9
Batch Size: 2048
Image Size: '672'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1323
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ra-572d5dd9.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 85.35%
Top 5 Accuracy: 97.39%
- Name: tf_efficientnet_el
In Collection: TF EfficientNet
Metadata:
FLOPs: 9356616096
Parameters: 10590000
File Size: 42800271
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: tf_efficientnet_el
Crop Pct: '0.904'
Image Size: '300'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1551
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_el-5143854e.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.45%
Top 5 Accuracy: 95.17%
- Name: tf_efficientnet_em
In Collection: TF EfficientNet
Metadata:
FLOPs: 3636607040
Parameters: 6900000
File Size: 27933644
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: tf_efficientnet_em
Crop Pct: '0.882'
Image Size: '240'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1541
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_em-e78cfe58.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.71%
Top 5 Accuracy: 94.33%
- Name: tf_efficientnet_es
In Collection: TF EfficientNet
Metadata:
FLOPs: 2057577472
Parameters: 5440000
File Size: 22008479
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: tf_efficientnet_es
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1531
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_es-ca1afbfe.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.28%
Top 5 Accuracy: 93.6%
- Name: tf_efficientnet_l2_ns_475
In Collection: TF EfficientNet
Metadata:
FLOPs: 217795669644
Parameters: 480310000
File Size: 1925950424
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- FixRes
- Label Smoothing
- Noisy Student
- RMSProp
- RandAugment
- Weight Decay
Training Data:
- ImageNet
- JFT-300M
Training Resources: TPUv3 Cloud TPU
ID: tf_efficientnet_l2_ns_475
LR: 0.128
Epochs: 350
Dropout: 0.5
Crop Pct: '0.936'
Momentum: 0.9
Batch Size: 2048
Image Size: '475'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Stochastic Depth Survival: 0.8
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1509
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns_475-bebbd00a.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 88.24%
Top 5 Accuracy: 98.55%
-->
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