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An Unoffical Implementation of PeleeNet by TensorFlow, Keras
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An Unoffical Implementation of PeleeNet by TensorFlow, Keras.
Implemented training with CIFAR-10.

Original Paper

R. J. Wang, X. Li, C. X. Ling. Pelee: A Real-Time Object Detection System on Mobile Devices. NIPS. 2018.

The original implementation by Caffe.

Get started

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

Results on CIFAR-10

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).

No weight decay

5e-4 weight decay

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.

Details (Japanese)


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