@@ -4,7 +4,7 @@ MobileNetV2 is a significant improvement over MobileNetV1 and pushes the state o
MobileNetV2 builds upon the ideas from MobileNetV1, using depthwise separable convolution as efficient building blocks. However, V2 introduces two new features to the architecture: 1) linear bottlenecks between the layers, and 2) shortcut connections between the bottlenecks1.
[Paper](https://arxiv.org/pdf/1801.04381) Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for MobileNetV2." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.
[Paper](https://arxiv.org/pdf/1801.04381) Sandler, Mark, et al. "Mobilenetv2: Inverted residuals and linear bottlenecks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
@@ -4,7 +4,7 @@ MobileNetV2 is a significant improvement over MobileNetV1 and pushes the state o
MobileNetV2 builds upon the ideas from MobileNetV1, using depthwise separable convolution as efficient building blocks. However, V2 introduces two new features to the architecture: 1) linear bottlenecks between the layers, and 2) shortcut connections between the bottlenecks1.
[Paper](https://arxiv.org/pdf/1801.04381) Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for MobileNetV2." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.
[Paper](https://arxiv.org/pdf/1801.04381) Sandler, Mark, et al. "Mobilenetv2: Inverted residuals and linear bottlenecks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
# Dataset
@@ -16,7 +16,6 @@ Dataset used: imagenet
- Data format: RGB images.
- Note: Data will be processed in src/dataset.py
# Environment Requirements
- Hardware（Ascend)
@@ -48,6 +47,8 @@ Dataset used: imagenet
├── eval.py
```
Notation: Current hyperparameters only test on 4 cards while training, if want to use 8 cards for training, should change parameters like learning rate in 'src/config.py'.
chenzomi