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google-efficientnet

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Replica of code from here: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet

google-efficientnet 최신버젼 다운로드

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Copied the content from here: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet

# EfficientNets to run on GPU

[1] ICML 2019 paper, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
   Arxiv link: https://arxiv.org/abs/1905.11946.

## 1. About EfficientNet Models

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.

Google developed EfficientNets based on AutoML and Compound Scaling. In particular, they first use [AutoML Mobile framework](https://ai.googleblog.com/2018/08/mnasnet-towards-automating-design-of.html) to develop a mobile-size baseline network, named as EfficientNet-B0; Then, they 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, Google's 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](https://arxiv.org/abs/1811.06965).

* In middle-accuracy regime, Google's EfficientNet-B1 is 7.6x smaller and 5.7x faster on CPU inference than [ResNet-152](https://arxiv.org/abs/1512.03385), with similar ImageNet accuracy.

* Compared with the widely used [ResNet-50](https://arxiv.org/abs/1512.03385), Google's EfficientNet-B4 improves the top-1 accuracy from 76.3% of ResNet-50 to 82.6% (+6.3%), under similar FLOPS constraint.

## 2. Using Pretrained EfficientNet Checkpoints

Here is a list of EfficientNet checkpoints for [EfficientNet-B0](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/efficientnet-b0.tar.gz), [EfficientNet-B1](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/efficientnet-b1.tar.gz), [EfficientNet-B2](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/efficientnet-b2.tar.gz), and [EfficientNet-B3](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/efficientnet-b3.tar.gz).

A quick way to use these checkpoints is to use the following commands:

$ export MODEL=efficientnet-b0
    $ wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/${MODEL}.tar.gz
    $ tar zxf ${MODEL}.tar.gz
    $ python eval_ckpt_main.py --model_name=$MODEL --ckpt_dir=$MODEL --example_img=panda.jpg --labels_map_file=labels_map.txt

## Output
<table border="0">
<tr>
    <td>
    <img src="./output.png" width="100%" />
    </td>
</tr>
</table>

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