MnasNet Tensorflow 2 Implementation
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le. MnasNet: Platform-Aware Neural Architecture Search for Mobile. CVPR 2019. Arxiv link: https://arxiv.org/abs/1807.11626
Usage
Available implementations: a1, b1, small, d1, d1_320
from MnasNet_models import Build_MnasNet
# Standard model
model = Build_MnasNet('a1')
# Change default parameters:
model = Build_MnasNet('a1', dict(input_shape=(128, 128, 3), dropout_rate=0.5))Pretrained models
| Model | Dataset | Input Size | Depth Multiplier | Top-1 Accuracy | Top-5 Accuracy | Pixel 1 latency (ms) | DownLoad Link |
|---|---|---|---|---|---|---|---|
| MnasNet-A1 | ImageNet | 224*224 | 1.0 | 75.2 | 95.2 | 78ms | Google Drive |