Memory Error When Training YOLOv5 Using Git Bash

[Reza-Rezvan]

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Question

hi I am encountering a memory management issue while trying to train YOLOv5 models on a dataset consisting of approximately 37,000 images and corresponding labels. My training environment includes the use of the Ultralytics YOLO library and direct cloning of the YOLOv5 repository.

Issue Details: When I utilize the Ultralytics YOLO library and import the YOLO model directly in Python, I can run any YOLO model configuration without facing any memory issues. The RAM usage stays within manageable limits (35% to 50% of available RAM).

code: from ultralytics import YOLO import os os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'

model = YOLO("yolov5s.yaml")

results = model.train(data="Main/config.yaml", epochs=12)

However, when I clone the YOLOv5 repository and attempt to train using Git Bash with the following command:

"python train.py --img 320 --batch -1 --epochs 20 --data config.yaml --weights yolov5s.pt --cache"

I encounter a severe memory error. The system uses up to 100% of available RAM and eventually throws a MemoryError.

Steps Taken to Resolve the Issue:

I increased the system's RAM to 32 GB. I reduced the input image size to 320 pixels. I decreased the batch size. I have set the environment variable KMP_DUPLICATE_LIB_OK='TRUE' to try and address potential multiprocessing issues. I reduced my dataset by approximately 50%, yet the issue persists without significant improvement.

Despite these adjustments, training through Git Bash leads to unsustainable memory usage and subsequent failure.

Questions:

• Is there a known issue with memory management when using Git Bash to train YOLOv5 on larger datasets?
• Could there be specific configuration settings or adjustments within the YOLOv5 training scripts that might help better manage memory usage?
• Are there recommended practices or further modifications I can implement to ensure stable memory consumption during training?
• Could you direct me to specific parts of the YOLOv5 repository that might help me address this memory management issue? Are there particular scripts or settings in the configuration files that I should focus on to better manage memory usage during training?
• Why is there a noticeable difference in memory usage when using the YOLO model through the Ultralytics library directly in Python compared to training the model using the cloned YOLOv5 repository through Git Bash? What might cause these discrepancies, especially given that both methods are based on the same underlying architecture?

I would greatly appreciate any insights or suggestions you could provide on how to resolve these memory overflow issues when training YOLOv5 models using Git Bash.

Thank you for your support and looking forward to your guidance.

Additional

No response

[github-actions[bot]]

👋 Hello @Reza-Rezvan, 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.

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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]

Hi there! 👋

Thank you for providing a detailed description of your issue. It sounds like you're encountering a significant challenge with memory management during training. Here are a few insights and suggestions that might help:

1. Dataset Caching: The flag --cache caches images into RAM for faster training. While beneficial speed-wise, it significantly increases RAM usage for large datasets. Consider training without this flag to reduce RAM consumption.

2. Batch Size: You mentioned reducing the batch size, which is a good practice. However, ensure the batch size is set explicitly in your command, e.g., --batch 16, instead of --batch -1 to avoid automatically using the maximum batch size your memory can handle.

3. Image Size: Reducing the image size to 320 pixels is a step in the right direction. Smaller dimensions significantly reduce memory usage but keep an eye on how it might affect model performance.

4. Multiprocessing and Memory: The environment variable KMP_DUPLICATE_LIB_OK='TRUE' is specifically for macOS to bypass a library loading issue and might not address memory problems. Training processes can be memory-intensive, independent of this setting.

5. Discrepancy in Memory Usage: The difference in memory usage between direct library use and training via Git Bash could stem from how datasets and models are loaded into memory. Direct use might employ more efficient memory handling in some cases.

6. Investigating Further: There isn't a known specific issue with Git Bash and memory management. It could be related more to the training configurations and dataset handling than the terminal environment used. Delve into the training script (train.py) to understand how data loading and batching are handled. Adjusting the data loader's num_workers parameter could also help manage memory usage.

For further optimization, consider monitoring memory usage with different configurations step by step to pinpoint what changes result in the best memory management for your setup.

Sadly, if you're dealing with extremely large datasets or limited by hardware, you might have to balance between training time and memory usage by making compromises on batch size, image size, or dataset size.

If the issue persists and you're in need of more advanced or tailored advice, reviewing the documentation on our Ultralytics Docs might provide further insights.

Best of luck with your project, and remember, we're here to help! 🚀

[Reza-Rezvan]

Thank you so much for your prompt and insightful response. Following your recommendations, I was able to resolve the issue effectively. Thank you again for your support and the swift resolution of my query.

Hi there! 👋

Thank you for providing a detailed description of your issue. It sounds like you're encountering a significant challenge with memory management during training. Here are a few insights and suggestions that might help:

1. Dataset Caching: The flag --cache caches images into RAM for faster training. While beneficial speed-wise, it significantly increases RAM usage for large datasets. Consider training without this flag to reduce RAM consumption.
2. Batch Size: You mentioned reducing the batch size, which is a good practice. However, ensure the batch size is set explicitly in your command, e.g., --batch 16, instead of --batch -1 to avoid automatically using the maximum batch size your memory can handle.
3. Image Size: Reducing the image size to 320 pixels is a step in the right direction. Smaller dimensions significantly reduce memory usage but keep an eye on how it might affect model performance.
4. Multiprocessing and Memory: The environment variable KMP_DUPLICATE_LIB_OK='TRUE' is specifically for macOS to bypass a library loading issue and might not address memory problems. Training processes can be memory-intensive, independent of this setting.
5. Discrepancy in Memory Usage: The difference in memory usage between direct library use and training via Git Bash could stem from how datasets and models are loaded into memory. Direct use might employ more efficient memory handling in some cases.
6. Investigating Further: There isn't a known specific issue with Git Bash and memory management. It could be related more to the training configurations and dataset handling than the terminal environment used. Delve into the training script (train.py) to understand how data loading and batching are handled. Adjusting the data loader's num_workers parameter could also help manage memory usage.

For further optimization, consider monitoring memory usage with different configurations step by step to pinpoint what changes result in the best memory management for your setup.

Sadly, if you're dealing with extremely large datasets or limited by hardware, you might have to balance between training time and memory usage by making compromises on batch size, image size, or dataset size.

If the issue persists and you're in need of more advanced or tailored advice, reviewing the documentation on our Ultralytics Docs might provide further insights.

Best of luck with your project, and remember, we're here to help! 🚀

Thank you so much for your prompt and insightful response. Following your recommendations, I was able to resolve the issue effectively. Thank you again for your support and the swift resolution of my query.

[glenn-jocher]

@Reza-Rezvan you're very welcome! 🌟 I'm thrilled to hear that the suggestions were helpful and that you've successfully navigated through the issue. If you have any more questions or encounter further challenges down the road, don't hesitate to reach out. Happy coding and best of luck with your projects! Remember, the Ultralytics community and team are always here to support you. 😊