# NPU (华为昇腾)

## 使用方法

首先，请参考 {external+mmcv:doc}`教程 <get_started/build>` 安装带有 NPU 支持的 MMCV。

使用如下命令，可以利用 8 个 NPU 在机器上训练模型（以 ResNet 为例）：

```shell
bash tools/dist_train.sh configs/cspnet/resnet50_8xb32_in1k.py 8 --device npu
```

或者，使用如下命令，在一个 NPU 上训练模型（以 ResNet 为例）：

```shell
python tools/train.py configs/cspnet/resnet50_8xb32_in1k.py --device npu
```

## 经过验证的模型

|                            模型                            | Top-1 (%) | Top-5 (%) |                            配置文件                            |                            相关下载                            |
| :--------------------------------------------------------: | :-------: | :-------: | :------------------------------------------------------------: | :------------------------------------------------------------: |
|            [CSPResNeXt50](../papers/cspnet.md)             |   77.10   |   93.55   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/cspresnext50_8xb32_in1k.log.json) |
|            [DenseNet121](../papers/densenet.md)            |   72.62   |   91.04   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet121_4xb256_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/densenet121_4xb256_in1k.log.json) |
| [EfficientNet-B4(AA + AdvProp)](../papers/efficientnet.md) |   75.55   |   92.86   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/efficientnet-b4_8xb32-01norm_in1k.log.json) |
|              [HRNet-W18](../papers/hrnet.md)               |   77.01   |   93.46   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/hrnet-w18_4xb32_in1k.log.json) |
|            [ResNetV1D-152](../papers/resnet.md)            |   77.11   |   94.54   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) |                    [model](<>) \| [log](<>)                    |
|              [ResNet-50](../papers/resnet.md)              |   76.40   |     -     | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) |                    [model](<>) \| [log](<>)                    |
|         [ResNetXt-32x4d-50](../papers/resnext.md)          |   77.55   |   93.75   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/resnext50-32x4d_8xb32_in1k.log.json) |
|           [SE-ResNet-50](../papers/seresnet.md)            |   77.64   |   93.76   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/seresnet50_8xb32_in1k.log.json) |
|                 [VGG-11](../papers/vgg.md)                 |   68.92   |   88.83   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/vgg11_8xb32_in1k.log.json) |
|      [ShuffleNetV2 1.0x](../papers/shufflenet_v2.md)       |   69.53   |   88.82   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) |                    [model](<>) \| [log](<>)                    |

**以上所有模型权重及训练日志均由华为昇腾团队提供**