mit-han-lab / Vww
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Solution to Visual Wakeup Words Challenge'19 (first place).
Participants: Song Han, Ji Lin, Kuan Wang, Tianzhe Wang, Zhanghao Wu (following alphabetical order)
Contact: [email protected]
Instruction
The demo code on Raspberry Pi and Android is included in this repo under the demos folder.
Youtube: https://youtu.be/7-beBCKVpFE
Article: https://medium.com/tensorflow/visual-wake-words-with-tensorflow-lite-micro-8578e59ea6f9
We have optimized the model with uint8 quantization and converted it to tf-lite format. Here we provide a script to evaluate the model with PyTorch data loader in eval.py
. However, to keep consistent with TensorFlow preprocessing, we used the preprocessing function imported from tensorflow. The preprocessing we used is defined in preprocess.py
.
Our floating point model (model_fp32.pb) can get 95.40%
top-1 accuracy on the minival set of VWW.
Our quantized model (model_quantized.tflite) can get 94.575%
top-1 accuracy on the minival set of VWW.
Usage
Run:
python eval.py
Citation
@article{cai2018proxylessnas,
title={Proxylessnas: Direct neural architecture search on target task and hardware},
author={Cai, Han and Zhu, Ligeng and Han, Song},
journal={International Conference on Learning Representations (ICLR)},
year={2019}
}