|TensorFlow 1 version||View source on GitHub|
NASNet-A models for Keras.
NASNet refers to Neural Architecture Search Network, a family of models that were designed automatically by learning the model architectures directly on the dataset of interest.
Here we consider NASNet-A, the highest performance model that was found for the CIFAR-10 dataset, and then extended to ImageNet 2012 dataset, obtaining state of the art performance on CIFAR-10 and ImageNet 2012. Only the NASNet-A models, and their respective weights, which are suited for ImageNet 2012 are provided.
The below table describes the performance on ImageNet 2012:
Architecture | Top-1 Acc | Top-5 Acc | Multiply-Adds | Params (M)
| NASNet-A (4 @ 1056) | 74.0 % | 91.6 % | 564 M | 5.3 |
| NASNet-A (6 @ 4032) | 82.7 % | 96.2 % | 23.8 B | 88.9 |
NASNetLarge(...): Instantiates a NASNet model in ImageNet mode.
NASNetMobile(...): Instantiates a Mobile NASNet model in ImageNet mode.
decode_predictions(...): Decodes the prediction of an ImageNet model.
preprocess_input(...): Preprocesses a tensor or Numpy array encoding a batch of images.