Update to the strongest Yolo World model

ofirkris commented 1 month ago

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[X] I have searched the YOLOv8 issues and found no similar feature requests.

Description

Hi, I've compared the accuracy of the yolov8x-worldv2.pt This model by Yolo worldV2 shows better results Can you add this to this repo?

Use case

get better results

Additional

For comparison, you can check the HF spaces model which works better

Are you willing to submit a PR?

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

github-actions[bot] commented 1 month ago

👋 Hello @ofirkris, 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 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 commented 1 month ago

@ofirkris hey there! 🙌

Thanks for bringing this to our attention. It's great to hear about your interest in enhancing YOLOv8 with stronger models like the one from Yolo WorldV2. At the moment, for the inclusion of new models, we typically undergo a rigorous validation and compatibility check to ensure they align with YOLOv8's architecture and standards.

I'd recommend keeping an eye on the releases page for any updates on new model integrations. In the meantime, if you're savvy with model conversion, you could consider converting the Yolo WorldV2 model to a format compatible with YOLOv8 and manually loading it for your use case. Here's a very basic example on how you might approach loading a custom model:

from ultralytics import YOLO

# Custom model path
model_path = 'path/to/your/custom_model.pt'

# Load your custom model
model = YOLO(model_path)

# Proceed with inference or other operations

Keep in mind this is a simplified example, and compatibility may vary based on the specific model and its training parameters. Stay tuned to our GitHub repo for official updates and feel free to share any success you have with your custom integrations. We love seeing the community's innovations! 😊

github-actions[bot] commented 3 weeks ago

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

Docs: https://docs.ultralytics.com
HUB: https://hub.ultralytics.com
Community: https://community.ultralytics.com

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐

ultralytics / ultralytics