Skip to content
Branch: master
Find file History
saberkun Typo
PiperOrigin-RevId: 260003407
Latest commit 878ebf4 Jul 25, 2019
Permalink
Type Name Latest commit message Commit time
..
Failed to load latest commit information.
g3doc Add MixNet README. Jul 24, 2019
README.md Typo Jul 26, 2019
__init__.py Initial version of mixnet. Jul 18, 2019
custom_layers.py Initial version of mixnet. Jul 18, 2019
mixnet_builder.py Add eval_ckpt tool for MixNet. Jul 23, 2019
mixnet_eval_example.ipynb Add MixNet README. Jul 24, 2019
mixnet_model.py Add MixNet README. Jul 24, 2019

README.md

MixNet

[1] Mingxing Tan and Quoc V. Le. MixNet: Mixed Depthwise Convolutional Kernels. BMVC 2019. https://arxiv.org/abs/1907.09595

1. About MixNet

MixNets are a family of mobile-sizes image classification models equipped with MDConv, a new type of mixed depthwise convolutions. They are developed based on AutoML MNAS Mobile framework, with an extended search space including MDConv. Currently, MixNets achieve better accuracy and efficiency than previous mobile models. In particular, our MixNet-L achieves a new state-of-the-art 78.9% ImageNet top-1 accuracy under typical mobile FLOPS (<600M) constraint:

2. Using Pretrained Checkpoints

We have provided a list of EfficientNet checkpoints for MixNet-S, MixNet-M, and MixNet-L. A quick way to use these checkpoints is to run:

$ export MODEL=mixnet-s
$ wget https://storage.googleapis.com/cloud-tpu-checkpoints/mixnet/${MODEL}.tar.gz
$ tar zxf ${MODEL}.tar.gz
$ wget https://upload.wikimedia.org/wikipedia/commons/f/fe/Giant_Panda_in_Beijing_Zoo_1.JPG -O panda.jpg
$ wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/eval_data/labels_map.txt
$ python eval_ckpt_main.py --model_name=$MODEL --ckpt_dir=$MODEL --example_img=panda.jpg --labels_map_file=labels_map.txt

Please refer to the following colab for more instructions on how to obtain and use those checkpoints.

  • mixnet_eval_example.ipynb: A colab example to load pretrained checkpoints files and use the restored model to classify images.

3. Training and Evaluating MixNets.

MixNets are trained using the same hyper parameters as MnasNet, except specifying different model_name=mixnet-s/m/l.

For more instructions, please refer to the MnasNet tutorial: https://cloud.google.com/tpu/docs/tutorials/mnasnet

You can’t perform that action at this time.