MESSES MY SYSTEM

[Sequential-circuits]

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• I have searched the YOLOv5 issues and found no similar bug report.

YOLOv5 Component

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Bug

I cannot use this with a jetson xavier running jet pack 5.1 because it simply replaces the installed versions of torch and torchvision with the ones it wants

I cannot simply install torch and torchvision with pip in a jetson: I need to install especial packages from nvidia

I tried to remove those 2 in the requirements.txt and still installs them automatically, and it takes me a long time to fix the mess it does in my system!

Environment

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Minimal Reproducible Example

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Additional

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Are you willing to submit a PR?

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

[github-actions]

👋 Hello @Sequential-circuits, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Requirements

Python>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

Environments

YOLOv5 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

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If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

Introducing YOLOv8 🚀

We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!

Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

Check out our YOLOv8 Docs for details and get started with:

pip install ultralytics

[glenn-jocher]

Hey there! 😊

It sounds like you're facing a challenge with the automatic installation of incompatible PyTorch versions on your Jetson Xavier. To prevent YOLOv5 from automatically installing or upgrading PyTorch and torchvision, you can modify the installation process slightly.

Instead of running the standard installation command, you can skip the automatic dependencies installation by using:

pip install --no-deps -e .

This command installs YOLOv5 without any dependencies. After that, you can manually install the necessary packages that are compatible with your system, as specified by NVIDIA for Jetson platforms.

If you need further customization, consider adjusting the requirements.txt file to better fit your environment before running any install commands.

Hope this helps you set up YOLOv5 on your Jetson Xavier without further issues! If you have more questions, feel free to ask. 🚀

[Sequential-circuits]

The problem is not during INSTALLATION, is during EXECUTION

As soon as it loads ultralytics, it starts to change torch and torchvision, among other things

And in jetson torchvision has to be compiled, so it ends up wasting a lot of time spent compiling it

It says for example:
WARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.8/dist-packages)
Installing collected packages: torch, torchvision
Attempting uninstall: torch
Found existing installation: torch 2.1.0a0+41361538.nv23.6
[Info] [DepthPacketStreamParser] 20 packets were lost
Uninstalling torch-2.1.0a0+41361538.nv23.6:
[Info] [DepthPacketStreamParser] 31 packets were lost
Successfully uninstalled torch-2.1.0a0+41361538.nv23.6

[glenn-jocher]

Hey there! 😊

It sounds like the issue arises due to the environment setup during the execution of YOLOv5, which triggers unwanted updates to torch and torchvision. This is likely due to dependency checks in the code.

To prevent this, you can try setting up a virtual environment specifically for running YOLOv5. This way, any changes or installations won't affect your global packages. Here’s how you can set it up:

1. Create a new virtual environment:

   python -m venv yolov5-env

2. Activate the environment:

   source yolov5-env/bin/activate

3. Install the specific versions of torch and torchvision that you compiled for Jetson:

   pip install <your-torch-wheel>
   pip install <your-torchvision-wheel>

4. Run YOLOv5 within this environment.

This should isolate the execution environment and prevent the automatic uninstallation and reinstallation of these packages. Let me know if this helps or if you encounter any other issues! 🚀

[Sequential-circuits]

That solved the problem thanks!

[glenn-jocher]

Hi there! 😊

I'm glad to hear that the solution worked for you! If you have any more questions or run into any other issues, feel free to reach out. The YOLO community and the Ultralytics team are always here to help.

Happy coding and best of luck with your projects! 🚀

Warm