Change evaluation period about ultralytics

[glenn-jocher]

Hello!

Thank you for your kind words and for using YOLOv8! 😊

To change the evaluation period during the training process, you can use the val_period argument. This argument allows you to specify how often (in terms of epochs) you want the validation to run. By default, validation runs after each epoch, but you can adjust this to fit your needs.

Here's how you can set the evaluation to run every 10 epochs:

Using Python

from ultralytics import YOLO

# Load your model
model = YOLO('yolov8n.pt')  # or your custom model

# Train the model with validation every 10 epochs
results = model.train(data='coco128.yaml', epochs=100, imgsz=640, val_period=10)

Using CLI

yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640 val_period=10

This will ensure that the validation step is performed every 10 epochs instead of after every epoch.

Feel free to reach out if you have any more questions or need further assistance. Happy training! 🚀

[github-actions]

👋 Hello @june94, 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.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.

Join the vibrant Ultralytics Discord 🎧 community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users.

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

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.

[june94]

Thank you!

[glenn-jocher]

@june94 you're welcome! 😊

If you have any more questions or need further assistance, feel free to ask. We're here to help! Happy training and best of luck with your projects! 🚀

If you encounter any issues or bugs, please make sure to provide a minimum reproducible code example. This will help us investigate and resolve the issue more efficiently. You can find more information on how to create one here: Minimum Reproducible Example.

Additionally, ensure that you are using the most recent versions of torch and ultralytics. If you haven't updated recently, please upgrade your packages and try again.

Thank you for being a part of the YOLO community!