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1 month ago 👋 Hello @EzraKenig, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.
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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 ultralyticsEnvironments
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
Status
If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
Y-T-G
glenn-jocher
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Question
Hi, I am writing this issue because I have trouble understanding the way ground truth labels are created in YOLOv8. From my understanding (correct me if I am wrong), in previous versions of YOLO (the ones that used anchor boxes), the prediction of an object was assigned to one of the three predict layers depending on the best IoU value the ground truth bounding box had with the anchor set. But now that YOLOv8 has stopped using anchor box, I was wondering how to decide to which predict layer one should assign the prediction of a given object.
I hope my question is clear. Best regards and thanks in advance for helping me to answer it.
Additional
No response