[1] Mingxing Tan and Quoc V. Le. MixConv: Mixed Depthwise Convolutional Kernels. BMVC 2019. https://arxiv.org/abs/1907.09595
MixNets are a family of mobile-sizes image classification models equipped with MixConv, a new type of mixed depthwise convolutions. They are developed based on AutoML MNAS Mobile framework, with an extended search space including MixConv. 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:
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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.
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