| Training Techniques | RMSProp, Weight Decay |
|---|---|
| Architecture | 1x1 Convolution, Batch Normalization, Convolution, Dense Connections, Depthwise Separable Convolution, Dropout, Global Average Pooling, Hard Swish, Inverted Residual Block, Residual Connection, ReLU, Softmax, Squeeze-and-Excitation Block |
| ID | tf_mobilenetv3_large_075 |
| SHOW MORE |
| Training Techniques | RMSProp, Weight Decay |
|---|---|
| Architecture | 1x1 Convolution, Batch Normalization, Convolution, Dense Connections, Depthwise Separable Convolution, Dropout, Global Average Pooling, Hard Swish, Inverted Residual Block, Residual Connection, ReLU, Softmax, Squeeze-and-Excitation Block |
| ID | tf_mobilenetv3_large_100 |
| SHOW MORE |
| Training Techniques | RMSProp, Weight Decay |
|---|---|
| Architecture | 1x1 Convolution, Batch Normalization, Convolution, Dense Connections, Depthwise Separable Convolution, Dropout, Global Average Pooling, Hard Swish, Inverted Residual Block, Residual Connection, ReLU, Softmax, Squeeze-and-Excitation Block |
| ID | tf_mobilenetv3_large_minimal_100 |
| SHOW MORE |
| Training Techniques | RMSProp, Weight Decay |
|---|---|
| Architecture | 1x1 Convolution, Batch Normalization, Convolution, Dense Connections, Depthwise Separable Convolution, Dropout, Global Average Pooling, Hard Swish, Inverted Residual Block, Residual Connection, ReLU, Softmax, Squeeze-and-Excitation Block |
| ID | tf_mobilenetv3_small_075 |
| SHOW MORE |
| Training Techniques | RMSProp, Weight Decay |
|---|---|
| Architecture | 1x1 Convolution, Batch Normalization, Convolution, Dense Connections, Depthwise Separable Convolution, Dropout, Global Average Pooling, Hard Swish, Inverted Residual Block, Residual Connection, ReLU, Softmax, Squeeze-and-Excitation Block |
| ID | tf_mobilenetv3_small_100 |
| SHOW MORE |
| Training Techniques | RMSProp, Weight Decay |
|---|---|
| Architecture | 1x1 Convolution, Batch Normalization, Convolution, Dense Connections, Depthwise Separable Convolution, Dropout, Global Average Pooling, Hard Swish, Inverted Residual Block, Residual Connection, ReLU, Softmax, Squeeze-and-Excitation Block |
| ID | tf_mobilenetv3_small_minimal_100 |
| SHOW MORE |
MobileNetV3 is a convolutional neural network that is designed for mobile phone CPUs. The network design includes the use of a hard swish activation and squeeze-and-excitation modules in the MBConv blocks.
To load a pretrained model:
import timm
m = timm.create_model('tf_mobilenetv3_large_075', pretrained=True)
m.eval()
Replace the model name with the variant you want to use, e.g. tf_mobilenetv3_large_075. You can find the IDs in the model summaries at the top of this page.
You can follow the timm recipe scripts for training a new model afresh.
@article{DBLP:journals/corr/abs-1905-02244,
author = {Andrew Howard and
Mark Sandler and
Grace Chu and
Liang{-}Chieh Chen and
Bo Chen and
Mingxing Tan and
Weijun Wang and
Yukun Zhu and
Ruoming Pang and
Vijay Vasudevan and
Quoc V. Le and
Hartwig Adam},
title = {Searching for MobileNetV3},
journal = {CoRR},
volume = {abs/1905.02244},
year = {2019},
url = {http://arxiv.org/abs/1905.02244},
archivePrefix = {arXiv},
eprint = {1905.02244},
timestamp = {Tue, 12 Jan 2021 15:30:06 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1905-02244.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Image Classification on ImageNet
| MODEL | TOP 1 ACCURACY | TOP 5 ACCURACY |
|---|---|---|
| tf_mobilenetv3_large_100 | 75.51% | 92.61% |
| tf_mobilenetv3_large_075 | 73.45% | 91.34% |
| tf_mobilenetv3_large_minimal_100 | 72.24% | 90.64% |
| tf_mobilenetv3_small_100 | 67.92% | 87.68% |
| tf_mobilenetv3_small_075 | 65.72% | 86.13% |
| tf_mobilenetv3_small_minimal_100 | 62.91% | 84.24% |
