Ultralytics support v9 and v10 at the same package? about ultralytics HOT 8 CLOSED

👋 Hello @Hestinorwu, 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.

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My problem is that i hope use "from ultralytics import YOLO" to load two different model.or use 'import YOLOv10'. because the precess and postcess of ultralytics is convenient.I can eastily get results.

from ultralytics.

Hello @Hestinorwu,

Thank you for reaching out! It's great to hear that you find the Ultralytics YOLO models convenient for your tasks. Let's address the issue you're encountering with loading both YOLOv9 and YOLOv10 models simultaneously.

The error you're seeing, AttributeError: Can't get attribute 'YOLOv10DetectionModel', suggests that there might be a compatibility issue with the specific versions of the models and the ultralytics package you are using.

Here are a few steps to help resolve this:

1. Ensure Latest Versions: First, make sure you are using the latest versions of both torch and ultralytics. You can upgrade them using the following commands:

   pip install --upgrade torch ultralytics

2. Separate Environments: If the issue persists, it might be due to the fact that YOLOv9 and YOLOv10 models require different versions of the ultralytics package. In such cases, using separate virtual environments for each model can be a good solution. Here’s how you can set it up:

   • Create Virtual Environments:

     # Create a virtual environment for YOLOv9
     python -m venv yolov9_env
     # Activate the environment
     yolov9_env\Scripts\activate  # On Windows
     source yolov9_env/bin/activate  # On macOS/Linux
     # Install the required packages
     pip install ultralytics==<specific_version_for_v9> torch
     
     # Create a virtual environment for YOLOv10
     python -m venv yolov10_env
     # Activate the environment
     yolov10_env\Scripts\activate  # On Windows
     source yolov10_env/bin/activate  # On macOS/Linux
     # Install the required packages
     pip install ultralytics==<specific_version_for_v10> torch

3. Example Code: Here’s an example of how you can load and use both models in separate environments:

   • YOLOv9 Environment:

     # Activate YOLOv9 environment
     yolov9_env\Scripts\activate  # On Windows
     source yolov9_env/bin/activate  # On macOS/Linux
     
     from ultralytics import YOLO
     
     v9_model = YOLO('yolov9c.pt')
     v9_results = v9_model('path/to/image.jpg')
     print(v9_results)

   • YOLOv10 Environment:

     # Activate YOLOv10 environment
     yolov10_env\Scripts\activate  # On Windows
     source yolov10_env/bin/activate  # On macOS/Linux
     
     from ultralytics import YOLO
     
     v10_model = YOLO('yolov10m_handheld_20240611.pt')
     v10_results = v10_model('path/to/image.jpg')
     print(v10_results)

By using separate environments, you can ensure that each model has the appropriate dependencies and versions without conflicts.

If you continue to face issues, please provide a minimum reproducible example of your code so we can further investigate. You can refer to our Minimum Reproducible Example Guide for more details.

I hope this helps! If you have any further questions, feel free to ask. 😊

from ultralytics.

Thanks a lot!!! i try to download the folder'ultralytics' of yolov10,and use from ultralytics import yolov10 and successfully adapt it to my project. and i realize that v9 and v10 need different environment.So if i still to use yolov9 at the same time,i need to seperate both to exec each . Thanks again!

from ultralytics.

Hello @Hestinorwu,

You're very welcome! I'm glad to hear that you successfully adapted YOLOv10 to your project. 🎉

Indeed, separating the environments for YOLOv9 and YOLOv10 is a practical approach to avoid any compatibility issues. This way, you can ensure that each model operates with the appropriate dependencies and versions.

If you have any further questions or run into any issues, feel free to reach out. We're here to help! 😊

Happy coding and best of luck with your project!

from ultralytics.

Thanks for your reply,now i encounter a new problem. How to use Yolov9 cli to train data. it seems that in https://github.com/WongKinYiu/yolov9 the usage of cli is not supported.
Only usage like below:
python train_dual.py --workers 8 --device 0 --batch 16 --data data/coco.yaml --img 640 --cfg models/detect/yolov9-c.yaml --weights '' --name yolov9-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15

from ultralytics.

Hello @HduHestin,

Thank you for your question! It appears that the YOLOv9 repository you mentioned (WongKinYiu/yolov9) does not support CLI commands in the same way as Ultralytics YOLO models. Instead, it uses Python scripts for training.

To train a YOLOv9 model using the provided script, you can follow the example command you mentioned:

python train_dual.py --workers 8 --device 0 --batch 16 --data data/coco.yaml --img 640 --cfg models/detect/yolov9-c.yaml --weights '' --name yolov9-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15

Here's a brief breakdown of the command:

• --workers 8: Number of data loading workers.
• --device 0: GPU device to use.
• --batch 16: Batch size.
• --data data/coco.yaml: Path to the dataset configuration file.
• --img 640: Image size.
• --cfg models/detect/yolov9-c.yaml: Path to the model configuration file.
• --weights '': Path to the weights file (empty string for training from scratch).
• --name yolov9-c: Name of the training run.
• --hyp hyp.scratch-high.yaml: Path to the hyperparameters file.
• --min-items 0: Minimum number of items.
• --epochs 500: Number of training epochs.
• --close-mosaic 15: Number of epochs to disable mosaic augmentation before training ends.

If you encounter any issues or have specific questions about the training process, please provide more details or a minimum reproducible example so we can assist you better. You can refer to our Minimum Reproducible Example Guide for more information.

Feel free to reach out if you have any further questions. Happy training! 😊

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👋 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 ⭐

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