YOLOv8 with AS-One

• AS-One and YOLOv8
  • Installation
• Running AS-One
  • Trackers
    • DeepSort
    • ByteTrack
    • NorFair
  • Detectors (Excerpt)
    • YOLOv8

AS-One is a python wrapper for multiple detection and tracking algorithms all at one place. Different trackers such as ByteTrack, DeepSort or NorFair can be integrated with different versions of YOLO with minimum lines of code. This python wrapper provides YOLO models in both ONNX and PyTorch versions.

AS-One and YOLOv8

git clone https://github.com/augmentedstartups/AS-One.git /run/media/xiaodie/dev/as-one-yolov8/
cd as-one-yolov8

Installation

You can install the dependencies in a virtual environment - if you wish:

python3 -m venv .env
source .env/bin/activate

pip install numpy Cython
pip install cython-bbox
pip install asone

YOLO is build on PyTorch that needs to be installed with our without GPU support:

# for CPU
pip install torch torchvision
# for GPU
pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu113

pip show numpy Cython cython-bbox asone torch torchvision
Name: numpy
Version: 1.23.3
---
Name: Cython
Version: 0.29.33
---
Name: cython-bbox
Version: 0.1.3
---
Name: asone
Version: 0.1.2.dev17
---
Name: torch
Version: 1.13.1
---
Name: torchvision
Version: 0.14.1

Running AS-One

If you run main.py to test tracker on the data/sample_videos/test.mp4 video:

python main.py data/sample_videos/test.mp4

The test code uses YOLOv7 and Bytetrack:

dt_obj = ASOne(
      tracker=asone.BYTETRACK,
      detector=asone.YOLOV7_PYTORCH,
      weights=args.weights,
      use_cuda=args.use_cuda
    )

and starts tracking and counting cars in the test video:

We can change the detector and tracker that is being used with anyone from this list:

Trackers

DeepSort

Model	Model Flag	FPS-GPU	FPS-CPU
DeepSort-ONNX-Yolov5s	DEEPSORT	13	3.2
DeepSort-Pytorch-Yolov5s	DEEPSORT	13	3.2

ByteTrack

Model	Model Flag	FPS-GPU	FPS-CPU
ByteTrack-ONNX-YOLOv5s	BYTETRACK	33.7	17.4
ByteTrack-Pytorch-Sample-YOLOv5s	BYTETRACK	33.7	17.4

NorFair

Model	Model Flag	FPS-GPU	FPS-CPU
tryolab-ONNX-YOLOv5s	NORFAIR	25.8	12
tryolab-Pytorch-YOLOv5s	NORFAIR	25.8	12

Detectors (Excerpt)

YOLOv8

Pytorch
Model Name / Model Flag	FPS-GPU	FPS-CPU
YOLOV8N_PYTORCH	26.7	17.0
YOLOV8S_PYTORCH	26.4	12.3
YOLOV8M_PYTORCH	25.1	6.8
YOLOV8L_PYTORCH	23.6	4.0
YOLOV8X_PYTORCH	20.7	2.8

So to change to YOLOv8 we just have to replace it here main.py:

dt_obj = ASOne(
      tracker=asone.BYTETRACK,
      detector=asone.YOLOV8M_PYTORCH,
      weights=args.weights,
      use_cuda=args.use_cuda
    )

python main.py data/sample_videos/video2.mp4

And this time we are running YOLOv8:

The tracking function in main.py also gives us a few more options - for example to draw trails on tracked moving objects:

track_fn = dt_obj.track_video(args.video_path,
        output_dir=args.output_dir,
        conf_thres=args.conf_thres,
        iou_thres=args.iou_thres,
        display=args.display,
        draw_trails=args.draw_trails,
        filter_classes=filter_classes,
        class_names=None) # class_names=['License Plate'] for custom weights

Which we can set to true by adding the argument:

python main.py data/sample_videos/video2.mp4 --draw_trails