CSP_resnext50-mish

This implements training of csp_resnext50-mish on the ImageNet dataset, mainly modified from https://github.com/rwightman/pytorch-image-models.

CSP_resnext50-mish Detail

For details, see ./timm/models/cspnet.py

Requirements

• Install PyTorch (pytorch.org)
• Download the ImageNet dataset from http://www.image-net.org/
  • Then, and move validation images to labeled subfolders, using the following shell script

Training

To train a model, run train_1p.py or train_8p.py with the desired model architecture and the path to the ImageNet dataset:

1p prefomance training 1p

bash test/train_performance_1p.sh --data_path=xxx

8p prefomance training 8p

bash test/train_performance_8p.sh --data_path=xxx

1p full training 1p

bash test/train_performance_1p.sh --data_path=xxx

8p full training 8p

bash test/train_performance_8p.sh --data_path=xxx

eval default 8p

bash ./test/train_eval_8p.sh --data_path=xxx

#To ONNX python3 pthtar2onx.py --model-path path/to/model_best.pth.tar

online inference demo

python3 demo.py --model-path /path/to/model_best.pth.tar

## CSP_resnext50-mish training result

| Acc@1    | FPS       | Npu_nums | Epochs   | AMP_Type |
| :------: | :------:  | :------: | :------: | :------: |
| -        | 202       | 1        | 1      | O2       |
| 79.36   | 1807      | 8        | 150      | O2       |

公网地址说明

代码涉及公网地址参考 public_address_statement.md