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First place in the Visual Wake Words challenge (TF-lite track) in LPIRC'19
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demos Finalized README and formatting Aug 6, 2019
README.md Update README.md Sep 11, 2019
dataset.py [major] init files Jun 26, 2019
eval.py [major] init files Jun 26, 2019
model_fp32.pb Add float point .pb model. Sep 11, 2019
model_quantized.tflite [major] init files Jun 26, 2019
preprocess.py [major] init files Jun 26, 2019

README.md

Solution to Visual Wakeup Words Challenge'19 (first place).

Participants: Song Han, Ji Lin, Kuan Wang, Tianzhe Wang, Zhanghao Wu (following alphabetical order)

Contact: jilin@mit.edu

Instruction

We have converted our model to tflite format with uint8 quantization. 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.

The demo code on Raspberry Pi and Android is included in this repo under the demos folder.

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}
}
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