$ python main.py --block_type basic --depth 110 --alpha 84 --outdir results
| Model | Test Error (median of 3 runs) | Test Error (in paper) | Training Time |
|---|---|---|---|
| PyramidNet-110 (alpha=84) | 4.40 | 4.26 ± 0.23 | 11h40m |
| PyramidNet-110 (alpha=270) | 3.92 (1 run) | 3.73 ± 0.04 | 24h12m* |
| PyramidNet-164 bottleneck (alpha=270) | 3.48 ± 0.20 | ||
| PyramidNet-272 bottleneck (alpha=200) | 3.31 ± 0.08 |
- Han, Dongyoon, Jiwhan Kim, and Junmo Kim. "Deep pyramidal residual networks." The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 5927-5935. arXiv:1610.02915, Torch implementation, Caffe implementation, PyTorch implementation