Resnet18 trained from scrach on ImageNet.
the original KaimingHe implement：https://github.com/KaimingHe/deep-residual-networks
Facebook AI Research (FAIR)：https://github.com/facebookarchive/fb.resnet.torch
| Network | Top-1 error | Top-5 error |
|---|---|---|
| KaimingHe | / | / |
| FAIR | 30.43 | 10.76 |
| our | 30.8625 | 11.6625 |
the trained model can be access from: https://pan.baidu.com/s/1_dPXOZd9Fkvb_67yhaMHNw code: fc7m
I own use random crop and mirror, and the Top-1 error is 0.4% less than FAIR's. Currently I am working with more data augmentation.
the training process is shown below
see demo_image.py