An Unoffical Implementation of PeleeNet by TensorFlow, Keras.
Implemented training with CIFAR-10.
R. J. Wang, X. Li, C. X. Ling. Pelee: A Real-Time Object Detection System on Mobile Devices. NIPS. 2018. https://arxiv.org/abs/1804.06882
The original implementation by Caffe. https://github.com/Robert-JunWang/Pelee
from pelee_net import PeleeNet
model = PeleeNet(input_shape=(224,224,3), use_stem_block=True, n_classes=1000)- input_shape : Resolution of input images. 224x224x3 by default(same as the original).
- use_stem_block : Whether to use Stem Block. If True it's same as the original, if False input is connected directly to the first Dense Layer.
- n_classes : Number of classes in prediction
| Augmentation | Stem Block | No weight Decay | 5e-4 Weight Decay |
|---|---|---|---|
| No | No | 0.7633 | 0.9247 |
| No | Yes | 0.8280 | 0.9218 |
| Yes | No | 0.8881 | 0.9446 |
| Yes | Yes | 0.8996 | 0.9410 |
- Enable stem block cases : Input=(224, 224, 3), Upsampling x7
- Disable stem block cases : Input=(32, 32, 3), No upsampling
Data augmentation is the standard data augmentation(4 pixels shift and horizontal flip).
There was no discussion int the paper on weight decay. But I noticed that weight decay is important in increasing the accuracy of CIFAR-10, so I added it.
DenseNetの軽量版、PeleeNetをKerasで実装した
https://qiita.com/koshian2/items/187e240f478504079e7a