Participants: Song Han, Ji Lin, Kuan Wang, Tianzhe Wang, Zhanghao Wu (following alphabetical order)
Contact: jilin@mit.edu
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.
Run:
python eval.py
@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}
}