By Xingcheng Zhang, Zhizhong Li, Chen Change Loy, Dahua Lin
Multimedia Laboratory, The Chinese University of Hong Kong
@article{zhang2016polynet,
title={Polynet: A pursuit of structural diversity in very deep networks},
author={Zhang, Xingcheng and Li, Zhizhong and Loy, Chen Change and Lin, Dahua},
journal={arXiv preprint arXiv:1611.05725},
year={2016}
}
-
Models
NOTE: The model is trained using our own deep learning framework Parrots. The caffe model is converted from the Parrots model, and the proto file is based on Yuanjun's fork of Caffe.
| model | training speed* (#imgs/second) | single-crop val top-1 | single-crop val top-5 | single-crop test top-5 | multi-crop val top-1 | multi-crop val top-5 |
|---|---|---|---|---|---|---|
| ResNet-152 | - | 22.16 | 6.16 | - | 19.38 | 4.49 |
| ResNet-152^ | 279 ( 8 GPUs) | 20.93 | 5.54 | 5.50 | 18.50 | 3.97 |
| ResNet-269^ | 245 (16 GPUs) | 19.78 | 4.89 | 4.82 | 17.54 | 3.55 |
| ResNet-500^ | 248 (32 GPUs) | 19.66 | 4.78 | 4.70 | 17.59 | 3.63 |
| Inception-v4 | - | 20.0 | 5.0 | - | 1 7.7 | 3.8 |
| Inception-ResNet-v2 | - | 19.9 | 4.9 | - | 17.8 | 3.7 |
| Inception-ResNet-v2 | 314 ( 8 GPUs) | 20.05 | 5.05 | 5.11 | 18.41 | 3.98 |
| Very Deep Inception-ResNet | 278 (32 GPUs) | 19.10 | 4.48 | 4.46 | 17.39 | 3.56 |
| Very Deep PolyNet | 290 (32 GPUs) | 18.71 | 4.25 | 4.33 | 17.36 | 3.45 |
^ The ResNet models are trained by Tong Xiao;
* Training speed is measured on Parrots using NVIDIA TITAN X Graphics Cards.
