models/yolov9/ about ultralytics

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👋 Hello @tangweii222, 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]

Hello! It looks like you're trying to use a YOLOv5 model with the YOLOv8 framework, which unfortunately isn't compatible due to differences in the model architectures and training methods.

To resolve this, you have two options:

1. Train a new model using YOLOv8: You can train a new model directly with the YOLOv8 framework to ensure compatibility.
2. Use an official YOLOv8 model: If retraining is not feasible, consider using one of the pre-trained official YOLOv8 models for your tasks.

If you choose to train a new model and need guidance on how to set up your training in Colab with YOLOv8, feel free to ask here! 😊

[tangweii222]

but my custom model is trained by yolov9-c.pt. i prefer to solve this problem rather then train a new model because google colab have some limit🥲

[glenn-jocher]

Hello! Thanks for clarifying that your model is trained with YOLOv9. To resolve the compatibility issue without retraining, you might consider converting your YOLOv9 model to a format that can be used with YOLOv8, such as ONNX, and then using the converted model in your application.

Here’s a general approach:

1. Export your YOLOv9 model to ONNX format.
2. Use the ONNX model with compatible inference libraries that support ONNX models.

This method allows you to bypass the direct compatibility issues between different YOLO versions while leveraging your existing model. If you need detailed steps on how to export to ONNX and use it, I'm here to help! 😊

[tangweii222]

tks for reply. Let me take a moment to learn how to how to export ONNX and use it.if i still encounter any problems,i will come back to you for help.Thank you very much.

[glenn-jocher]

You're welcome! Take your time with the ONNX export process, and don't hesitate to reach out if you have any questions or run into issues. I'm here to help. Good luck! 😊

[tangweii222]

Using the code below i can export yolov9c.pt to ONNX but my custom model is failed.

from ultralytics import YOLO

# Load the YOLOv9 model
model = YOLO("yolov9c.pt") # model = YOLO("custom_model.pt") is failed

# Export the model to ONNX format
model.export(format="onnx")  # creates 'yolov9c.onnx'

# Load the exported ONNX model
onnx_model = YOLO("yolov9c.onnx")

same error code :
ModuleNotFoundError: No module named 'models'
TypeError: ERROR ❌️ /Users/tangwei/NCU/飛盤機器人/Yolov9/in_out_model/bgry_frisbee.pt appears to be an Ultralytics YOLOv5 model originally trained with https://github.com/ultralytics/yolov5.
This model is NOT forwards compatible with YOLOv8 at https://github.com/ultralytics/ultralytics.
Recommend fixes are to train a new model using the latest 'ultralytics' package or to run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt'

[glenn-jocher]

@tangweii222 hello! It seems like the model you are trying to export (custom_model.pt) was originally trained with a different version of the Ultralytics software, which is not compatible with the current version you are using for export. This compatibility issue is causing the errors you're seeing.

To resolve this, you have a couple of options:

1. Re-train your model using the current version of Ultralytics YOLO that you are using for export. This ensures compatibility and might also benefit from any improvements or optimizations in the newer version.
2. Use the model with the version of Ultralytics YOLO it was originally trained with, if updating the model is not feasible.

If re-training the model is an option for you, I recommend using the latest stable release to ensure the best performance and compatibility moving forward. If you need further assistance or have more questions, feel free to ask. Good luck! 😊