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Periodical Weight Save #5045

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aseprohman opened this issue Oct 5, 2021 · 2 comments · Fixed by #5047
Closed

Periodical Weight Save #5045

aseprohman opened this issue Oct 5, 2021 · 2 comments · Fixed by #5047
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@aseprohman
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Hello @glenn-jocher

How to save weights in periodical epoch step ? for example I have train my dataset for 200 epoch and I want to save my weights every 50 epoch ?
I've try to train data with yolov4 alexeyab and he have methode for save weight in periodical batch dataset.
I hope it will possible too in your training framework

@aseprohman aseprohman added the question Further information is requested label Oct 5, 2021
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github-actions bot commented Oct 5, 2021

👋 Hello @aseprohman, 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 screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available.

For business inquiries or professional support requests please visit https://ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.

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Python>=3.6.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

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

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@glenn-jocher glenn-jocher linked a pull request Oct 5, 2021 that will close this issue
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glenn-jocher commented Oct 5, 2021

@aseprohman good news 😃! Your feature suggestion should now be implemented ✅ in PR #5047. This PR implements a --save-period argument to allow for periodic checkpointing during training, i.e. python train.py --save-period 50 to checkpoint epoch 50, epoch 100, etc.

To receive this update:

  • Gitgit pull from within your yolov5/ directory or git clone https://github.com/ultralytics/yolov5 again
  • PyTorch Hub – Force-reload with model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)
  • Notebooks – View updated notebooks Open In Colab Open In Kaggle
  • Dockersudo docker pull ultralytics/yolov5:latest to update your image Docker Pulls

Thank you for spotting this issue and informing us of the problem. Please let us know if this update resolves the issue for you, and feel free to inform us of any other issues you discover or feature requests that come to mind. Happy trainings with YOLOv5 🚀!

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