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Mosaic #13145
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👋 Hello @wyyt1202, 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
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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 |
@wyyt1202 hello! Thank you for your question and for checking the existing issues and discussions before posting. 😊 When you set Mosaic augmentation is a powerful technique that helps improve the robustness of your model by combining four training images into one, which can enhance the model's ability to generalize. However, it's always a good idea to monitor your training process to ensure that the augmentation is benefiting your specific dataset and task. If you have any further questions or need additional clarification, feel free to ask. We're here to help! |
thanks |
@wyyt1202 hello! Thank you for your kind words and for your engagement with the YOLOv5 community. 😊 To address your question about Mosaic augmentation: When you set Mosaic augmentation is a powerful technique that combines four training images into one, which can significantly enhance the model's ability to generalize by providing more diverse training samples. However, it's important to monitor your training process to ensure that this augmentation is beneficial for your specific dataset and task. If you encounter any issues or have further questions, please provide a minimum reproducible code example so we can better assist you. You can find more information on how to create one here. Additionally, please ensure you are using the latest versions of Feel free to reach out if you need more assistance. We're here to help! |
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Question
if I set Mosaic=1, does it mean that Mosaic will be used throughout the entire training process, for example, if the training rounds are 300, will Mosaic be used in all these 300 rounds?
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