Training Techniques | Weight Decay, SGD with Momentum |
---|---|
Architecture | 1x1 Convolution, Batch Normalization, Convolution, Depthwise Separable Convolution, Dropout, Global Average Pooling, Inverted Residual Block, Residual Connection, ReLU, Max Pooling, Softmax, Squeeze-and-Excitation Block |
ID | mnasnet1_0 |
SHOW MORE |
MnasNet is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. The main building block is an inverted residual block (from MobileNetV2).
To load a pretrained model:
import torchvision.models as models
mnasnet = models.mnasnet1_0(pretrained=True)
Replace the model name with the variant you want to use, e.g. mnasnet1_0
. You can find the IDs in the model summaries at the top of this page.
To evaluate the model, use the image classification recipes from the library.
python train.py --test-only --model='<model_name>'
You can follow the torchvision recipe on GitHub for training a new model afresh.
@article{DBLP:journals/corr/abs-1807-11626,
author = {Mingxing Tan and
Bo Chen and
Ruoming Pang and
Vijay Vasudevan and
Quoc V. Le},
title = {MnasNet: Platform-Aware Neural Architecture Search for Mobile},
journal = {CoRR},
volume = {abs/1807.11626},
year = {2018},
url = {http://arxiv.org/abs/1807.11626},
archivePrefix = {arXiv},
eprint = {1807.11626},
timestamp = {Mon, 13 Aug 2018 16:46:25 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1807-11626.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
BENCHMARK | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK |
---|---|---|---|---|
ImageNet | MNASNet 1.0 | Top 1 Accuracy | 73.51% | # 247 |
Top 5 Accuracy | 91.54% | # 247 |