Custom Model and Dataset for YOLO Explorer

[Jordan-Pierce]

Search before asking

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

Description

YOLO Explorer is a pretty cool tool, but currently only canned models and datasets can be used from the Dashboard; it be great if we could select our custom models and datasets we have locally

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]

👋 Hello @Jordan-Pierce, 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.

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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

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.

[glenn-jocher]

Hi there! 🌟

We really appreciate your interest in enhancing YOLO Explorer's capabilities! Currently, integrating custom models and datasets directly via the Dashboard is not supported. However, this is a valuable suggestion and could be an interesting feature to consider for future updates.

In the meantime, you can manually load your custom models and datasets by modifying your local setup. For example, you can specify the model and dataset paths directly in the code before initializing the tool:

from ultralytics import YOLO

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

# Use your custom dataset
results = model.train(data='path/to/your/dataset.yaml')

If you're interested in contributing to this feature, we'd love to see your PR! Thanks for your input, and let's keep making YOLO better together! 💻✨