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After the YOLOv5 version update, does it affect model performance? #13110
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👋 Hello @bjh03205, 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. RequirementsPython>=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 EnvironmentsYOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
StatusIf 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 |
@bjh03205 hello, Thank you for reaching out with your question! It's great to see your interest in understanding the impact of YOLOv5 version updates on model performance. Impact of YOLOv5 Version Updates on Model PerformanceYOLOv5 is actively maintained and regularly updated to improve performance, add new features, and fix bugs. While these updates generally aim to enhance the model's capabilities, they can sometimes lead to variations in performance metrics such as precision, recall, and inference speed. To assess the impact of a version update on your specific use case, I recommend the following steps:
Example Code for Performance EvaluationHere is an example of how you can evaluate the model performance before and after the update: # Before update
python val.py --weights yolov5x.pt --data coco.yaml --img 640 --half
# Update YOLOv5
git pull
pip install -r requirements.txt
# After update
python val.py --weights yolov5x.pt --data coco.yaml --img 640 --half Additional ResourcesFor more detailed information on model evaluation and pruning, you can refer to our Pruning/Sparsity Tutorial. If you encounter any specific issues or discrepancies, please provide a minimum reproducible example as outlined here. This will help us investigate and address any potential bugs effectively. Feel free to reach out if you have any further questions or need additional assistance. The YOLO community and the Ultralytics team are always here to help! |
Thanks a lot for your quick answer :) |
@bjh03205 you're welcome! 😊 To ensure we can effectively address your issue, could you please provide a minimum reproducible code example? This will help us understand the context and reproduce the bug on our end. You can find guidelines on how to create one here. Additionally, please make sure you are using the latest versions of git pull # update YOLOv5
pip install -r requirements.txt # update dependencies Once you've done that, please run your code again to see if the issue persists. If it does, share the reproducible example, and we'll be happy to investigate further. Thank you for your cooperation, and we look forward to resolving this for you! |
👋 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:
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 ⭐ |
I am testing different software to visualize the results, such as heatmaps.
However, there are compatibility differences depending on the model version, such as v6.1-YOLOv5 and v7.0-YOLOv5, for the application.
Therefore, I want to use v6.1-YOLOv5, which is available.
I did not find any updates that would affect the model's performance like mAP in the releases.
I'd like to know if I am missing something.
After updating from v6.1-YOLOv5 to v7.0-YOLOv5, does it affect the model's performance?
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