person_reid_baseline_pytorch

UTS-Person-reID-Practical

Input (query)

(Image from http://188.138.127.15:81/Datasets/Market-1501-v15.09.15.zip)

Shape : (batch, 3, height, width)

Output (Top 10 images from gallery images that are similar to query)

Usage

Automatically downloads the onnx and prototxt files on the first run. It is necessary to be connected to the Internet while downloading.

For the sample image,

$ python3 person_reid_baseline_pytorch.py

If you want to specify the input image, put the image path after the --input option.
You can use --savepath option to change the name of the output file to save.

$ python3 person_reid_baseline_pytorch.py --input IMAGE_PATH --savepath SAVE_IMAGE_PATH

If you want to specify the directory of gallery image, put the directory path after the --gallery_dir option.

$ python3 person_reid_baseline_pytorch.py --gallery_dir gallery

Now, files in this gallery directory are very restricted.
Many more files can be found in the bounding_box_test directory of Market-1501-v15.09.15.zip.

Once the program run, a intermediate result file containing the features of the gallery image will be created.
By adding the intermediate result file name after the --data option, you can use the intermediate result of the previous inference.

$ python3 person_reid_baseline_pytorch.py --data result_resnet50.npy

By adding the model name after the --model option, you can specify the model.
The model name is selected from 'resnet50', 'fp16', 'dense', 'pcb'.

$ python3 person_reid_baseline_pytorch.py --model resnet50

Reference

• UTS-Person-reID-Practical

Framework

Pytorch

Model Format

ONNX opset=11

Netron

ft_ResNet50.onnx.prototxt fp16.onnx.prototxt ft_net_dense.onnx.prototxt PCB.onnx.prototxt