REQUIREMENTS.TXT FILE ERROR WITHIN YOLOV5

[FatmaDikmen]

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

YOLOv5 Component

Training

Bug

I'm working on image processing with YOLOv5 model on Kaggle using ready-made datasets. However, after cloning YOLOv5 and installing libraries in requirements.txt file with pip, I'm getting library incompatibility errors on Colab. I also tried with YOLOv3 versions. What do you think the solution to my problem is?

Environment

Training YOLOV3 and YOLOV5 models on Kaggle and Colab, Windows 11, Intel i5 processor

Minimal Reproducible Example

!pip install -r requirements.txt

Additional

No response

Are you willing to submit a PR?

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

[github-actions]

👋 Hello @FatmaDikmen, 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.

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.

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

Status

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]

@FatmaDikmen hi there! 👋 It looks like you're encountering library compatibility issues after installing the requirements for YOLOv5 on Colab. This can sometimes happen due to differences in package versions between environments.

Here's a quick suggestion:

1. Use a Virtual Environment: To avoid conflicting with pre-existing libraries, try setting up a virtual environment specifically for your YOLOv5 project. You can do this on Colab with the following commands:

!pip install virtualenv
!virtualenv yolov5env
!source yolov5env/bin/activate
!pip install -r requirements.txt

2. Check for Specific Version Conflicts: After the environment setup, if you still face issues, you might want to manually install or adjust specific libraries within the requirements.txt to versions compatible with both YOLOv5 and the Colab environment.

Since you're willing to submit a PR, you might consider adding a note or a script in the YOLOv5 documentation to help others who might face similar issues in the future! Thanks for contributing to the community. 🚀