![]() ![]() ![]() 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.Ĭompared 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.ĮfficientNet PyTorch is a PyTorch re-implementation of EfficientNet. 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 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.ĮfficientNets achieve state-of-the-art accuracy on ImageNet with an order of magnitude better efficiency: We develop EfficientNets based on AutoML and Compound Scaling. If you're new to EfficientNets, here is an explanation straight from the official TensorFlow implementation:ĮfficientNets 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. Export EfficientNet models for production.Quickly finetune an EfficientNet on your own dataset.Train new models from scratch on ImageNet with a simple command.Upcoming features: In the next few days, you will be able to: Evaluate EfficientNet models on ImageNet or your own images.Use EfficientNet models for classification or feature extraction.This implementation is a work in progress - new features are currently being implemented. The goal of this implementation is to be simple, highly extensible, and easy to integrate into your own projects. This repository contains an op-for-op PyTorch reimplementation of EfficientNet, along with pre-trained models and examples. from_pretrained( 'efficientnet-b4') Overview ![]() Usage is the same as before:įrom efficientnet_pytorch import EfficientNet model = EfficientNet. Additionally, all pretrained models have been updated to use AutoAugment preprocessing, which translates to better performance across the board. Upgrade the pip package with pip install -upgrade efficientnet-pytorch Thanks to the authors of all the pull requests! Update (July 31, 2019) It also addresses pull requests #72, #73, #85, and #86. This update makes the Swish activation function more memory-efficient. This update addresses issues #88 and #89. To switch to the export-friendly version, simply call t_swish(memory_efficient=False) after loading your desired model. For this purpose, we have also included a standard (export-friendly) swish activation function. ![]() The memory-efficient version is chosen by default, but it cannot be used when exporting using PyTorch JIT. This update allows you to choose whether to use a memory-efficient Swish activation. This update also addresses multiple other issues ( #115, #128). If advprop: # for models using advprop pretrained weights normalize = transforms. As a result, by default, advprop models are not used. It is important to note that the preprocessing required for the advprop pretrained models is slightly different from normal ImageNet preprocessing. This update adds a new category of pre-trained model based on adversarial training, called advprop. This update adds comprehensive comments and documentation (thanks to Update (January 23, 2020)
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