Make Yolo8 colab output not a mess

[satyrmipt]

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• I have searched the YOLOv8 issues and found no similar feature requests.

Description

Have you ever seen how yolo train process output looks in Google Colab?
Could you make table with intermediate results more stuctured?

Use case

No response

Additional

No response

Are you willing to submit a PR?

• Yes I'd like to help by submitting a PR!

[github-actions]

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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

Status

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[glenn-jocher]

Hi there! Thanks for reaching out with your feedback on the YOLOv8 outputs in Google Colab.

I understand your concern about the structure of the output tables during training. Enhancing readability is indeed crucial, especially for monitoring model progress effectively.

A quick tip to improve the current display in Colab might involve using Python’s rich library to print more visually structured tables. Here is a small example of how you could format the output more neatly:

from rich.console import Console
from rich.table import Table

console = Console()

table = Table(show_header=True, header_style="bold magenta")
table.add_column("Epoch", style="dim", width=12)
table.add_column("Loss")
table.add_column("Accuracy", justify="right")

# Assuming you fetch these values from the training process
table.add_row("1", "0.456", "85%")
table.add_row("2", "0.342", "88%")
table.add_row("3", "0.310", "90%")

console.print(table)

This approach should streamline the visual presentation of your logs in Colab. If you have more suggestions or need further modifications, please feel free to share! 😊