kadirnar / yolov5-strongsort

Minimal PyTorch implementation of YOLOv5 and StrongSort

Yolov5-StrongSort: YOLOv5-Pip + StrongSort

Overview

This repo is a shortened version of yolov5 codes and added StrongSort algorithm.

Installation

git clone https://github.com/kadirnar/yolov5-strongsort
cd yolov5-strongsort
pip install -r requirements.txt

Yolov5 Model + StrongSort Prediction

python yolo_tracker.py --video_path test.mp4 --model_path yolov5m.pt --device cuda:0 --confidence_threshold 0.5 --image_size 640 --config_path osnet_x0_25_market1501.pt

Citations

@article{du2022strongsort,
  title={Strongsort: Make deepsort great again},
  author={Du, Yunhao and Song, Yang and Yang, Bo and Zhao, Yanyun},
  journal={arXiv preprint arXiv:2202.13514},
  year={2022}
}

Reference:

• Yolov5-Pip
• Strongsort

yolov5-strongsort's Issues

clear unnecessary codes in the data file

torch_utils.py write the select_device function in the file.

Orijinal select_device

def select_device(device='', batch_size=0, newline=True):
    # device = 'cpu' or '0' or '0,1,2,3'
    s = f'YOLOv5 🚀 {git_describe() or date_modified()} torch {torch.__version__} '  # string
    device = str(device).strip().lower().replace('cuda:', '')  # to string, 'cuda:0' to '0'
    cpu = device == 'cpu'
    if cpu:
        os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False
    elif device:  # non-cpu device requested
        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable - must be before assert is_available()
        assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \
            f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"

    cuda = not cpu and torch.cuda.is_available()
    if cuda:
        devices = device.split(',') if device else '0'  # range(torch.cuda.device_count())  # i.e. 0,1,6,7
        n = len(devices)  # device count
        if n > 1 and batch_size > 0:  # check batch_size is divisible by device_count
            assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'
        space = ' ' * (len(s) + 1)
        for i, d in enumerate(devices):
            p = torch.cuda.get_device_properties(i)
            s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2:.0f}MiB)\n"  # bytes to MB
    else:
        s += 'CPU\n'

    if not newline:
        s = s.rstrip()
    LOGGER.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s)  # emoji-safe
    return torch.device('cuda:0' if cuda else 'cpu')

To Do: write select_device function simpler.
Assignees: @kadirnar