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Hello, I have some questions about the parse_model. Could you please help me answer them? #13159
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👋 Hello @xiaoshuomin, 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 |
@xiaoshuomin hello, Thank you for reaching out with your questions about the Questions and Answers
Additional StepsTo ensure you are working with the latest improvements and bug fixes, please verify that you are using the latest versions of git pull
pip install -U torch If you encounter any issues or have further questions, please provide a minimum reproducible code example. This will help us investigate and provide a more accurate solution. You can find more information on creating a minimum reproducible example here. Thank you for your understanding and cooperation. If you have any more questions, feel free to ask! |
Thanks for your answer. If I want to run YOLOv5 on my private dataset, do I need to change the value of anchors? If so, what would be the best value to change it? |
Hello @xiaoshuomin, You're welcome! Running YOLOv5 on a private dataset is a great way to leverage its powerful object detection capabilities. To answer your question about anchors: Do You Need to Change the Anchors?Yes, adjusting the anchor values can significantly improve the performance of YOLOv5 on your custom dataset. The predefined anchors in YOLOv5 are optimized for the COCO dataset, and they might not be ideal for your specific dataset, especially if the object sizes and aspect ratios differ. How to Determine the Best Anchor Values?YOLOv5 provides an automated way to calculate the optimal anchors for your dataset using the
Additional ResourcesFor more detailed information on YOLOv5's architecture and how to customize it for your needs, you can refer to the YOLOv5 Architecture Description. If you encounter any issues or have further questions, please provide a minimum reproducible code example. This will help us investigate and provide a more accurate solution. You can find more information on creating a minimum reproducible example here. Thank you for your interest in YOLOv5, and happy training! 😊 |
Thanks for your answer again! I checked the train.py file and there is no autoanchor feature , only noautoanchor feature,e.g. parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor"), what is the difference between autoanchor and noautoanchor feature? |
Hello @xiaoshuomin, Thank you for your follow-up question! I'm glad to see your interest in fine-tuning YOLOv5 for your custom dataset. AutoAnchor vs. NoAutoAnchorYou are correct that the
Example UsageTo enable the default auto-anchor calculation, simply run your training command without the python train.py --data your_dataset.yaml --cfg yolov5s.yaml --weights yolov5s.pt If you prefer to disable the auto-anchor feature and use the predefined anchors, you can include the python train.py --data your_dataset.yaml --cfg yolov5s.yaml --weights yolov5s.pt --noautoanchor Next Steps
Thank you for your continued interest in YOLOv5. If you have any more questions or need further assistance, feel free to ask. Happy training! 😊 |
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Here are my questions:
elif m in {Detect, Segment}:
args.append([ch[x] for x in f])
if isinstance(args[1], int): # number of anchors
args[1] = [list(range(args[1] * 2))] * len(f)
Additional
No response
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