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A neat pytorch implementation of nasnet and the ported weights from tensorflow
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README.md

A neat pytorch implementation of NASNet

The performance of the ported models on ImageNet (Accuracy):

Model Checkpoint Million Parameters Val Top-1 Val Top-5
NASNet-A_Mobile_224 5.3 70.2 89.4
NASNet-A_large_331 88.9 82.3 96.0

The slight performance drop may be caused by the different spatial padding methods between tensorflow and pytorch.

The porting process is done by tensorflow_dump.py and pytorch_load.py, modified from Cadene's project. Note that NASNets with the original performance can be found there.

You can evaluate the models by running imagenet_eval.py, e.g. evaluate the NASNet-A_Mobile_224 ported model by

python imagenet_eval.py --nas-type mobile --resume /path/to/modelfile --gpus 0 --data /path/to/imagenet_root_dir

The ported model files are provided: NASNet-A_Mobile_224, NASNet-A_large_331.

Future work:

  • add drop path for training
  • more nasnet model settings
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