PCB

This implements training of PCB on the Market-1501 dataset, mainly modified from syfafterzy/PCB_RPP_for_reID.

PCB Detail

As of the current date, Ascend-Pytorch is still inefficient for contiguous operations.Therefore, PCB is re-implemented using semantics such as custom OP.

Requirements

Install PyTorch (pytorch.org)

PyTorch版本：CANN 5.0.T205 PT>=20210618

pip install -r requirements.txt Note: pillow recommends installing a newer version. If the corresponding torchvision version cannot be installed directly, you can use the source code to install the corresponding version. The source code reference link: Suggestion the pillow is 9.1.0 and the torchvision is 0.6.0
Download the Market-1501 dataset from https://paperswithcode.com/dataset/market-1501
- ```
unzip Market-1501-v15.09.15.zip
```

Training

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

# training 1p accuracy
bash scripts/train_full_1p.sh --data_path=real_data_path

# training 1p performance
bash scripts/train_performance_1p.sh --data_path=real_data_path

# training 8p accuracy
bash scripts/train_full_8p.sh --data_path=real_data_path

# training 8p performance
bash scripts/train_performance_8p.sh --data_path=real_data_path

# Online inference demo
python demo.py --data_path real_data_path --device npu
## 备注： 识别前后图片保存到 `inference/` 文件夹下

# To ONNX
python pthtar2onnx.py

PCB training result

	mAP	AMP_Type	Epochs	FPS
1p-GPU	-	O2	1	568.431
1p-NPU	-	O2	1	571.723
8p-GPU	77.2	O2	60	3600.983
8p-NPU	77.5	O2	60	2750.401

公网地址说明

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