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pytorch-image-models/modelindex/models/tf-efficientnet.md

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# Summary
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. 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 $2^N$ times more computational resources, then we can simply increase the network depth by $\alpha ^ N$, width by $\beta ^ N$, and image size by $\gamma ^ N$, where $\alpha, \beta, \gamma$ are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient $\phi$ to uniformly scales network width, depth, and resolution in a principled way.
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.
The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of [MobileNetV2](https://paperswithcode.com/method/mobilenetv2), in addition to [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block).
## How do I use this model on an image?
To load a pretrained model:
```python
import timm
model = timm.create_model('tf_efficientnet_b1', pretrained=True)
model.eval()
```
To load and preprocess the image:
```python
import urllib
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
config = resolve_data_config({}, model=model)
transform = create_transform(**config)
url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
urllib.request.urlretrieve(url, filename)
img = Image.open(filename).convert('RGB')
tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```python
import torch
with torch.no_grad():
out = model(tensor)
probabilities = torch.nn.functional.softmax(out[0], dim=0)
print(probabilities.shape)
# prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```python
# Get imagenet class mappings
url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
urllib.request.urlretrieve(url, filename)
with open("imagenet_classes.txt", "r") as f:
categories = [s.strip() for s in f.readlines()]
# Print top categories per image
top5_prob, top5_catid = torch.topk(probabilities, 5)
for i in range(top5_prob.size(0)):
print(categories[top5_catid[i]], top5_prob[i].item())
# prints class names and probabilities like:
# [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `tf_efficientnet_b1`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](https://rwightman.github.io/pytorch-image-models/feature_extraction/), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```python
model = timm.create_model('tf_efficientnet_b1', pretrained=True).reset_classifier(NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@misc{tan2020efficientnet,
title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
author={Mingxing Tan and Quoc V. Le},
year={2020},
eprint={1905.11946},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
<!--
Models:
- Name: tf_efficientnet_b1
Metadata:
FLOPs: 883633200
Epochs: 350
Batch Size: 2048
Training Data:
- ImageNet
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
File Size: 31512534
Tasks:
- Image Classification
ID: tf_efficientnet_b1
LR: 0.256
Crop Pct: '0.882'
Momentum: 0.9
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
In Collection: TF EfficientNet
- Name: tf_efficientnet_b4
Metadata:
FLOPs: 5749638672
Epochs: 350
Batch Size: 2048
Training Data:
- ImageNet
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Resources: TPUv3 Cloud TPU
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
File Size: 77989689
Tasks:
- Image Classification
Training Time: ''
ID: tf_efficientnet_b4
LR: 0.256
Crop Pct: '0.922'
Momentum: 0.9
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
Config: ''
In Collection: TF EfficientNet
- Name: tf_efficientnet_b2
Metadata:
FLOPs: 1234321170
Epochs: 350
Batch Size: 2048
Training Data:
- ImageNet
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
File Size: 36797929
Tasks:
- Image Classification
ID: tf_efficientnet_b2
LR: 0.256
Crop Pct: '0.89'
Momentum: 0.9
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
In Collection: TF EfficientNet
- Name: tf_efficientnet_b3
Metadata:
FLOPs: 2275247568
Epochs: 350
Batch Size: 2048
Training Data:
- ImageNet
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
File Size: 49381362
Tasks:
- Image Classification
ID: tf_efficientnet_b3
LR: 0.256
Crop Pct: '0.904'
Momentum: 0.9
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
In Collection: TF EfficientNet
- Name: tf_efficientnet_b0
Metadata:
FLOPs: 488688572
Epochs: 350
Batch Size: 2048
Training Data:
- ImageNet
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Resources: TPUv3 Cloud TPU
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
File Size: 21383997
Tasks:
- Image Classification
Training Time: ''
ID: tf_efficientnet_b0
LR: 0.256
Crop Pct: '0.875'
Momentum: 0.9
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
Config: ''
In Collection: TF EfficientNet
- Name: tf_efficientnet_b5
Metadata:
FLOPs: 13176501888
Epochs: 350
Batch Size: 2048
Training Data:
- ImageNet
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
File Size: 122403150
Tasks:
- Image Classification
ID: tf_efficientnet_b5
LR: 0.256
Crop Pct: '0.934'
Momentum: 0.9
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
In Collection: TF EfficientNet
- Name: tf_efficientnet_b6
Metadata:
FLOPs: 24180518488
Epochs: 350
Batch Size: 2048
Training Data:
- ImageNet
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
File Size: 173232007
Tasks:
- Image Classification
ID: tf_efficientnet_b6
LR: 0.256
Crop Pct: '0.942'
Momentum: 0.9
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
In Collection: TF EfficientNet
- Name: tf_efficientnet_b7
Metadata:
FLOPs: 48205304880
Epochs: 350
Batch Size: 2048
Training Data:
- ImageNet
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
File Size: 266850607
Tasks:
- Image Classification
ID: tf_efficientnet_b7
LR: 0.256
Crop Pct: '0.949'
Momentum: 0.9
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
In Collection: TF EfficientNet
- Name: tf_efficientnet_b8
Metadata:
FLOPs: 80962956270
Epochs: 350
Batch Size: 2048
Training Data:
- ImageNet
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
File Size: 351379853
Tasks:
- Image Classification
ID: tf_efficientnet_b8
LR: 0.256
Crop Pct: '0.954'
Momentum: 0.9
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
In Collection: TF EfficientNet
- Name: tf_efficientnet_el
Metadata:
FLOPs: 9356616096
Training Data:
- ImageNet
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
File Size: 42800271
Tasks:
- Image Classification
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
In Collection: TF EfficientNet
- Name: tf_efficientnet_em
Metadata:
FLOPs: 3636607040
Training Data:
- ImageNet
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
File Size: 27933644
Tasks:
- Image Classification
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
In Collection: TF EfficientNet
- Name: tf_efficientnet_es
Metadata:
FLOPs: 2057577472
Training Data:
- ImageNet
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
File Size: 22008479
Tasks:
- Image Classification
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
In Collection: TF EfficientNet
- Name: tf_efficientnet_l2_ns_475
Metadata:
FLOPs: 217795669644
Epochs: 350
Batch Size: 2048
Training Data:
- ImageNet
- JFT-300M
Training Techniques:
- AutoAugment
- FixRes
- Label Smoothing
- Noisy Student
- RMSProp
- RandAugment
- Weight Decay
Training Resources: TPUv3 Cloud TPU
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
File Size: 1925950424
Tasks:
- Image Classification
Training Time: ''
ID: tf_efficientnet_l2_ns_475
LR: 0.128
Dropout: 0.5
Crop Pct: '0.936'
Momentum: 0.9
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
Config: ''
In Collection: TF EfficientNet
Collections:
- Name: TF EfficientNet
Paper:
title: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks'
url: https://papperswithcode.com//paper/efficientnet-rethinking-model-scaling-for
type: model-index
Type: model-index
-->
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