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ModelZoo
mobilenet_prune_pytorch
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dc59df31
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dc59df31
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Jan 06, 2024
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dcuai
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@@ -13,6 +13,9 @@ mobilenetv2:
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@@ -13,6 +13,9 @@ mobilenetv2:
*
mobilenetv2 https://arxiv.org/pdf/1801.04381.pdf
*
mobilenetv2 https://arxiv.org/pdf/1801.04381.pdf
*
mobilenetv3 https://arxiv.org/pdf/1905.02244.pdf
*
mobilenetv3 https://arxiv.org/pdf/1905.02244.pdf
## 算法原理
## 算法原理
mobilenetv2:
具有线性瓶颈的反转残差(inverted residual ):该模块将低维压缩表示作为输入,首先将其扩展为高维度并使用轻量级沿深度卷积(depthwiseconvolution)进行滤波。随后通过线性卷积将特征投射回低维表示。中间层使用轻量级的沿深度卷积来对特征进行滤波作为非线性的来源。

## 数据集
## 数据集
`CIFAR-10`
`CIFAR-10`
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@@ -72,7 +75,7 @@ mobilenetv3
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python3 main.py --mode test --model mobilenetv3 --batch-size 128 --restore ./run/cifar10/prune/cifar10-global-l1-mobilenetv3/cifar10_mobilenetv3_l1.pt --dataset cifar10 --method l1 --speed-up 2.11 --global-pruning
python3 main.py --mode test --model mobilenetv3 --batch-size 128 --restore ./run/cifar10/prune/cifar10-global-l1-mobilenetv3/cifar10_mobilenetv3_l1.pt --dataset cifar10 --method l1 --speed-up 2.11 --global-pruning
```
```
## result
## result

### 精度
### 精度
method l1
method l1
| model | Base Acc | Pruned Acc |
| model | Base Acc | Pruned Acc |
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