Need help with EfficientNet-PyTorch?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

lukemelas
5.1K Stars 1.0K Forks Apache License 2.0 141 Commits 100 Opened issues

Description

A PyTorch implementation of EfficientNet

Services available

!
?

Need anything else?

Contributors list

EfficientNet PyTorch

Quickstart

Install with

pip install efficientnet_pytorch
and load a pretrained EfficientNet with:
python
from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_pretrained('efficientnet-b0')

Updates

Update (Aug 25, 2020)

This update adds: * A new

include_top
(default:
True
) option (#208) * Continuous testing with sotabench * Code quality improvements and fixes (#215 #223)

Update (May 14, 2020)

This update adds comprehensive comments and documentation (thanks to @workingcoder).

Update (January 23, 2020)

This update adds a new category of pre-trained model based on adversarial training, called advprop. It is important to note that the preprocessing required for the advprop pretrained models is slightly different from normal ImageNet preprocessing. As a result, by default, advprop models are not used. To load a model with advprop, use:

python
model = EfficientNet.from_pretrained("efficientnet-b0", advprop=True)
There is also a new, large
efficientnet-b8
pretrained model that is only available in advprop form. When using these models, replace ImageNet preprocessing code as follows:
python
if advprop:  # for models using advprop pretrained weights
    normalize = transforms.Lambda(lambda img: img * 2.0 - 1.0)
else:
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])
This update also addresses multiple other issues (#115, #128).

Update (October 15, 2019)

This update allows you to choose whether to use a memory-efficient Swish activation. The memory-efficient version is chosen by default, but it cannot be used when exporting using PyTorch JIT. For this purpose, we have also included a standard (export-friendly) swish activation function. To switch to the export-friendly version, simply call

model.set_swish(memory_efficient=False)
after loading your desired model. This update addresses issues #88 and #89.

Update (October 12, 2019)

This update makes the Swish activation function more memory-efficient. It also addresses pull requests #72, #73, #85, and #86. Thanks to the authors of all the pull requests!

Update (July 31, 2019)

Upgrade the pip package with

pip install --upgrade efficientnet-pytorch

The B6 and B7 models are now available. Additionally, all pretrained models have been updated to use AutoAugment preprocessing, which translates to better performance across the board. Usage is the same as before:

python
from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_pretrained('efficientnet-b7')

Update (June 29, 2019)

This update adds easy model exporting (#20) and feature extraction (#38).

It is also now incredibly simple to load a pretrained model with a new number of classes for transfer learning:

python
model = EfficientNet.from_pretrained('efficientnet-b1', num_classes=23)

Update (June 23, 2019)

The B4 and B5 models are now available. Their usage is identical to the other models:

python
from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_pretrained('efficientnet-b4')

Overview

This repository contains an op-for-op PyTorch reimplementation of EfficientNet, along with pre-trained models and examples.

The goal of this implementation is to be simple, highly extensible, and easy to integrate into your own projects. This implementation is a work in progress -- new features are currently being implemented.

At the moment, you can easily: * Load pretrained EfficientNet models * Use EfficientNet models for classification or feature extraction * Evaluate EfficientNet models on ImageNet or your own images

Upcoming features: In the next few days, you will be able to: * Train new models from scratch on ImageNet with a simple command * Quickly finetune an EfficientNet on your own dataset * Export EfficientNet models for production

Table of contents

  1. About EfficientNet
  2. About EfficientNet-PyTorch
  3. Installation
  4. Usage
  5. Contributing

About EfficientNet

If you're new to EfficientNets, here is an explanation straight from the official TensorFlow implementation:

EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, yet being an order-of-magnitude smaller and faster than previous models. We develop EfficientNets based on AutoML and Compound Scaling. In particular, we first use AutoML Mobile framework to develop a mobile-size baseline network, named as EfficientNet-B0; Then, we use the compound scaling method to scale up this baseline to obtain EfficientNet-B1 to B7.

EfficientNets achieve state-of-the-art accuracy on ImageNet with an order of magnitude better efficiency:

  • In high-accuracy regime, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet with 66M parameters and 37B FLOPS, being 8.4x smaller and 6.1x faster on CPU inference than previous best Gpipe.

  • In middle-accuracy regime, our EfficientNet-B1 is 7.6x smaller and 5.7x faster on CPU inference than ResNet-152, with similar ImageNet accuracy.

  • Compared with the widely used ResNet-50, our EfficientNet-B4 improves the top-1 accuracy from 76.3% of ResNet-50 to 82.6% (+6.3%), under similar FLOPS constraint.

About EfficientNet PyTorch

EfficientNet PyTorch is a PyTorch re-implementation of EfficientNet. It is consistent with the original TensorFlow implementation, such that it is easy to load weights from a TensorFlow checkpoint. At the same time, we aim to make our PyTorch implementation as simple, flexible, and extensible as possible.

If you have any feature requests or questions, feel free to leave them as GitHub issues!

Installation

Install via pip:

bash
pip install efficientnet_pytorch

Or install from source:

bash
git clone https://github.com/lukemelas/EfficientNet-PyTorch
cd EfficientNet-Pytorch
pip install -e .

Usage

Loading pretrained models

Load an EfficientNet:

python
from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_name('efficientnet-b0')

Load a pretrained EfficientNet:

python
from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_pretrained('efficientnet-b0')

Details about the models are below:

| Name |# Params|Top-1 Acc.|Pretrained?| |:-----------------:|:--------:|:----------:|:-----------:| |

efficientnet-b0
| 5.3M | 76.3 | ✓ | |
efficientnet-b1
| 7.8M | 78.8 | ✓ | |
efficientnet-b2
| 9.2M | 79.8 | ✓ | |
efficientnet-b3
| 12M | 81.1 | ✓ | |
efficientnet-b4
| 19M | 82.6 | ✓ | |
efficientnet-b5
| 30M | 83.3 | ✓ | |
efficientnet-b6
| 43M | 84.0 | ✓ | |
efficientnet-b7
| 66M | 84.4 | ✓ |

Example: Classification

Below is a simple, complete example. It may also be found as a jupyter notebook in

examples/simple
or as a Colab Notebook.

We assume that in your current directory, there is a

img.jpg
file and a
labels_map.txt
file (ImageNet class names). These are both included in
examples/simple
.
import json
from PIL import Image
import torch
from torchvision import transforms

from efficientnet_pytorch import EfficientNet model = EfficientNet.from_pretrained('efficientnet-b0')

Preprocess image

tfms = transforms.Compose([transforms.Resize(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),]) img = tfms(Image.open('img.jpg')).unsqueeze(0) print(img.shape) # torch.Size([1, 3, 224, 224])

Load ImageNet class names

labels_map = json.load(open('labels_map.txt')) labels_map = [labels_map[str(i)] for i in range(1000)]

Classify

model.eval() with torch.no_grad(): outputs = model(img)

Print predictions

print('-----') for idx in torch.topk(outputs, k=5).indices.squeeze(0).tolist(): prob = torch.softmax(outputs, dim=1)[0, idx].item() print('{label:<75} ({p:.2f}%)'.format(label=labels_map[idx], p=prob*100))

Example: Feature Extraction

You can easily extract features with

model.extract_features
: ```python from efficientnetpytorch import EfficientNet model = EfficientNet.frompretrained('efficientnet-b0')

... image preprocessing as in the classification example ...

print(img.shape) # torch.Size([1, 3, 224, 224])

features = model.extract_features(img) print(features.shape) # torch.Size([1, 1280, 7, 7]) ```

Example: Export to ONNX

Exporting to ONNX for deploying to production is now simple: ```python import torch from efficientnet_pytorch import EfficientNet

model = EfficientNet.frompretrained('efficientnet-b1') dummyinput = torch.randn(10, 3, 240, 240)

model.setswish(memoryefficient=False) torch.onnx.export(model, dummy_input, "test-b1.onnx", verbose=True) ```

Here is a Colab example.

ImageNet

See

examples/imagenet
for details about evaluating on ImageNet.

Contributing

If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.

I look forward to seeing what the community does with these models!

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.