EfficientNet - pretrained EfficientNets  are a family of neural network architectures released by Google in 2019 that have been designed by an optimization procedure that maximizes the accuracy for a given computational cost. EMBED (for wordpress.com hosted blogs and archive.org item <description> tags) I can reproduce the results using the pretrained models provided by the original authors (converted to PyTorch). I have not yet trained from scratch of Imagenet, but I will be working on it this weekend! I will also try to train the larger models (efficientnet-b4 to b7) and release the pretrained weights once finished.
A PyTorch implementation of EfficientNet. Contribute to lukemelas/EfficientNet-PyTorch development by creating an account on GitHub. Using Pretrained EfficientNet Checkpoints Keras Models Performance The top-k errors were obtained using Keras Applications with the TensorFlow backend on the 2012 ILSVRC ImageNet validation set and may slightly differ from the original ones.
Aug 06, 2019 · Since then, updates to the kits’ supporting resources have come at a steady clip, and today, Google released a new family of classification models — EfficientNet-EdgeTPU — it says are optimized to run on the Coral boards’ system-on-modules. Both the training code and pretrained models for EfficientNet-EdgeTPU are available on Github.
but in the paper EfficientNet-B0 has these properties: Network top-1 top-5 FLOPS EfficientNet B0 76.3 93.2 0.39B That looks like better accuracy with higher performance to me, but I don't know how much that would actually help something like YOLO.
A PyTorch implementation of EfficientNet. Contribute to lukemelas/EfficientNet-PyTorch development by creating an account on GitHub. Oct 02, 2019 · With EfficientNet the number of parameters is reduces by magnitudes, while achieving state-of-the-art results on ImageNet. Transfer Learning. While EfficientNet reduces the number of parameters, training of convolutional networks is still a time-consuming task. To further reduce the training time, we are able to utilize transfer learning techniques. May 31, 2019 · from efficientnet_pytorch import EfficientNet model = EfficientNet.from_pretrained('efficientnet-b0') And you can install it via pip if you would like: pip install efficientnet_pytorch
but in the paper EfficientNet-B0 has these properties: Network top-1 top-5 FLOPS EfficientNet B0 76.3 93.2 0.39B That looks like better accuracy with higher performance to me, but I don't know how much that would actually help something like YOLO. Objective: Train the Tensorflow EfficientNet model using a Cloud TPU device or Cloud TPU Pod slice (multiple TPU devices). The EfficientNet models are a family of image classification models, which achieve state-of-the-art accuracy, while also being smaller and faster than other models. A PyTorch implementation of EfficientNet. Contribute to lukemelas/EfficientNet-PyTorch development by creating an account on GitHub. More pretrained models to come... Ported Weights. The weights ported from Tensorflow checkpoints for the EfficientNet models do pretty much match accuracy in Tensorflow once a SAME convolution padding equivalent is added, and the same crop factors, image scaling, etc (see table) are used via cmd line args.
下图为EfficientNet-B0的模型结构，其基本构成单元是MBConv & SE. 基于EfficientNet-B0，基于前面提出的复合缩放方法，通过以下两步来缩放它： STEP-1: 首先固定 ，即假设有2倍资源可用，在前面提出的约束下，对αβγ进行小网格搜索，最终得到, , 是最优值。 Jun 04, 2019 · EfficientNet-Keras. This repository contains Keras reimplementation of EfficientNet, the new convolutional neural network architecture from EfficientNet (TensorFlow implementation). If you're new to EfficientNets, here is an explanation straight from the official TensorFlow implementation: