OSNet

This implements training of OSNet on the Market-1501 dataset, mainly modified from KaiyangZhou/deep-person-reid.

OSNet Detail

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

Requirements

• Install PyTorch (pytorch.org)

• pip install -r requirements.txt

• Install torchreid

  • python setup.py develop
    

• Download the Market-1501 dataset from https://paperswithcode.com/dataset/market-1501

  • unzip Market-1501-v15.09.15.zip
    

• Move Market-1501 dataset to 'reid-data' path

  • mkdir path_to_osnet/reid-data/
    mv Market-1501-v15.09.15 path_to_osnet/reid-data/market1501 
    

Training

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

# training 1p accuracy
bash test/train_full_1p.sh

# training 1p performance
bash test/train_performance_1p.sh

# training 8p accuracy
bash test/train_full_8p.sh

# training 8p performance
bash test/train_performance_8p.sh

# finetuning
bash test/train_finetune_1p.sh --data_path=real_data_path --weight=real_weight_path

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

# To ONNX
python pthtar2onnx.py 

OSNet training result

	mAP	AMP_Type	Epochs	FPS
1p-GPU	-	O2	1	371.383
1p-NPU	-	O2	1	366.464
8p-GPU	80.3	O2	350	1045.535
8p-NPU	80.2	O2	350	1091.358

公网地址说明

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