Custom tracker weight about ultralytics HOT 4 OPEN

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

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from ultralytics.

Hi there! 😊

1. ByteTrack Version: Ultralytics uses the default ByteTrack configuration as specified in the bytetrack.yaml file.
2. Custom ByteTrack Weights: To use your custom ByteTrack weights, you can modify the tracker argument in your tracking command. Here's an example:

from ultralytics import YOLO

# Load your custom model
model = YOLO('path/to/your/custom_model.pt')

# Use custom ByteTrack weights
results = model.track(source='path/to/your/video.mp4', tracker='path/to/your/custom_bytetrack.yaml')

Feel free to reach out if you have more questions! 🚀

from ultralytics.

Hi!
I tried to change .yaml to use a custom ByteTrack pth, but returned "AssertionError: Only 'bytetrack' and 'botsort' are supported for now, but got 'bytetrack_x_mot17.pth'". Looks like it is not possible by Ultralytics, I will do it manual. Thank you!

from ultralytics.

@MaxwellFB got it! Good luck with your implementation, and feel free to visit https://docs.ultralytics.com/modes/track/ for more information on tracking.

from ultralytics.