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NPU (HUAWEI Ascend)

Usage

Please refer to the building documentation of MMCV to install MMCV on NPU devices

Here we use 8 NPUs on your computer to train the model with the following command:

bash tools/dist_train.sh configs/ssd/ssd300_coco.py 8

Also, you can use only one NPU to train the model with the following command:

python tools/train.py configs/ssd/ssd300_coco.py

Models Results

Model box AP mask AP Config Download
ssd300 25.6 --- config log
ssd512 29.4 --- config log
ssdlite-mbv2* 20.2 --- config log
retinanet-r18 31.8 --- config log
retinanet-r50 36.6 --- config log
yolov3-608 34.7 --- config log
yolox-s** 39.9 --- config log
centernet-r18 26.1 --- config log
fcos-r50* 36.1 --- config log
solov2-r50 --- 34.7 config log

Notes:

  • If not specially marked, the results on NPU are the same as those on the GPU with FP32.

  • (*) The results on the NPU of these models are aligned with the results of the mixed-precision training on the GPU, but are lower than the results of the FP32. This situation is mainly related to the phase of the model itself in mixed-precision training, users may need to adjust the hyperparameters to achieve better results.

  • (**) The accuracy of yolox-s on the GPU in mixed precision is 40.1, with persister_woker=True in the data loader config by default. There are currently some bugs on NPUs that prevent the last few epochs from running, but the accuracy is less affected and the difference can be ignored.

All above models are provided by Huawei Ascend group.

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