zh/guides/yolo-common-issues/

[giscus[bot]]

zh/guides/yolo-common-issues/

在Ultralytics 生态系统中使用YOLOv8 时遇到的常见问题的全面故障排除指南。

https://docs.ultralytics.com/zh/guides/yolo-common-issues/

[yaoshisa]

Why is the progress bar not displayed when training on the GPU？

[pderrenger]

@yaoshisa hi there! It sounds like the progress bar issue might be related to the environment setup or an oversight in the process. Sometimes, when training on a GPU, if the verbose flag isn't set correctly, it could prevent the progress bar from displaying.

Here’s a quick check you can do:

from ultralytics import YOLO

# Ensure verbose is set to True
model = YOLO('yolov8n.pt', verbose=True)
results = model.train(data='coco128.yaml')

Setting verbose=True ensures that progress logs are displayed. If this doesn’t resolve the issue, it could be helpful to check whether your environment supports progress bar visualization or if other output settings might be interfering.

Let me know if this helps or if you need further assistance! 😊

[echo385]

Hello, thank you for your contribution! I use the 1280 of imgsz yolov8 to train my 640 images, but my gpu always is stuck ,why？this happens on A100 and 4090 all the time.

[pderrenger]

Hello! It sounds like you're experiencing GPU hangs when training with a larger imgsz than your original image sizes. This might be due to several reasons:

1. Memory Overload: The increase in imgsz significantly raises GPU memory requirements. Even high-end GPUs like A100 and 4090 might struggle if the configuration exceeds their memory capacity.

2. Batch Size: If you're using a larger imgsz, consider lowering the batch size to reduce memory demands.

Here’s what you can try:

• Reduce the imgsz to match your actual image dimensions or slightly increase it, but not as much as 1280 if it's not necessary.
• Decrease the batch size to ensure it fits within your GPU's memory limits.
• Monitor GPU usage: Use tools like nvidia-smi to watch the memory usage and ensure it's within limits.

If you need further assistance adjusting these settings, feel free to share more details about your setup and training configuration. 🛠️

[yileicc]

The bus.jpg has been placed under the assets path during the run, but the " FileNotFoundError: ultralytics/assets/bus.jpg does not exist”

[glenn-jocher]

Hello!

Thank you for reaching out. It seems like you're encountering a FileNotFoundError for ultralytics/assets/bus.jpg. Let's work through this together.

1. Verify File Path: Ensure that the bus.jpg file is indeed located in the ultralytics/assets/ directory. You can do this by navigating to the directory and checking if the file exists.

2. Check Code Implementation: If you are running a script, make sure the path to the image is correctly specified. Here’s an example of how to load and use the image in a script:

   from ultralytics import YOLO
   
   # Load a pre-trained YOLOv8 model
   model = YOLO("yolov8n.pt")
   
   # Specify the source image path
   source = "ultralytics/assets/bus.jpg"
   
   # Make predictions
   results = model.predict(source, save=True, imgsz=320, conf=0.5)

3. Update Packages: Ensure you are using the latest versions of torch and ultralytics. You can update them using:

   pip install --upgrade torch ultralytics

4. Reproducible Example: If the issue persists, please provide a minimum reproducible example of your code. This will help us better understand the context and provide a more accurate solution. You can find guidance on creating a minimum reproducible example here.

By following these steps, we should be able to pinpoint and resolve the issue. If you have any further questions or need additional assistance, feel free to ask!

[sjocnjfjd]

100 epochs completed in 0.146 hours.
Optimizer stripped from runs/detect/train/weights/last.pt, 5.5MB
Optimizer stripped from runs/detect/train/weights/best.pt, 5.5MB

Validating runs/detect/train/weights/best.pt...
Ultralytics 8.3.44 🚀 Python-3.10.12 torch-2.5.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
YOLO11n summary (fused): 238 layers, 2,582,347 parameters, 0 gradients, 6.3 GFLOPs

FileNotFoundError Traceback (most recent call last)
in <cell line: 4>()
2
3 model = YOLO('https://hub.ultralytics.com/models/kYDnuTY9JD3hHyFdnc5T')
----> 4 results = model.train(data='"D://python//新建文件夹 (2)//hakusenninnshiki.v2i.yolov11//lane.yaml"', project='runs/train', name='exp', epochs=100)

6 frames
/usr/local/lib/python3.10/dist-packages/ultralytics/data/utils.py in check_det_dataset(dataset, autodownload)
327 else:
328 m += f"\nNote dataset download directory is '{DATASETS_DIR}'. You can update this in '{SETTINGS_FILE}'"
--> 329 raise FileNotFoundError(m)
330 t = time.time()
331 r = None # success

FileNotFoundError:
Dataset '/content/datasets/lane/lane.yaml' images not found ⚠️, missing path '/content/datasets/D:python新/建文件夹 (2)/hakusenninnshiki.v2i.yolov11/lane.yaml/images/val'
Note dataset download directory is '/content/datasets'. You can update this in '/root/.config/Ultralytics/settings.json'
why can i deploy

[glenn-jocher]

@sjocnjfjd the error indicates a file path issue. Ensure your data parameter points to the correct yaml file and check that the dataset paths defined in the yaml file are valid and accessible. For guidance on dataset preparation and troubleshooting, refer to YOLO Common Issues. Let us know if you need further clarification!

[sjocnjfjd]

it is my.yaml： path: D:python新建文件夹 (2)/hakusenninnshiki.v2i.yolov11/lane.yaml train: images/train val: images/val test: images/test nc: 1 names: ['hakusen'] roboflow: &nbsp; workspace: kitazonoken &nbsp; project: hakusenninnshiki &nbsp; version: 2 &nbsp; license: CC BY 4.0 &nbsp; url: https://universe.roboflow.com/kitazonoken/hakusenninnshiki/dataset/2 is it right ?

…

---Original--- From: "Glenn ***@***.***&gt; Date: Fri, Dec 6, 2024 23:59 PM To: ***@***.***&gt;; Cc: ***@***.******@***.***&gt;; Subject: Re: [ultralytics/ultralytics] zh/guides/yolo-common-issues/(Discussion #11739) @sjocnjfjd the error indicates a file path issue. Ensure your data parameter points to the correct yaml file and check that the dataset paths defined in the yaml file are valid and accessible. For guidance on dataset preparation and troubleshooting, refer to YOLO Common Issues. Let us know if you need further clarification! — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: ***@***.***&gt;

[glenn-jocher]

@sjocnjfjd your YAML file seems mostly correct, but be cautious with the file path formatting. Replace D:python新建文件夹 (2)/hakusenninnshiki.v2i.yolov11/lane.yaml with a properly formatted path (e.g., D:/python/新建文件夹 (2)/hakusenninnshiki.v2i.yolov11/lane.yaml). Ensure all dataset paths are accessible and the Roboflow url is valid. If issues persist, refer to the YOLO Common Issues guide for troubleshooting.

[sjocnjfjd]

I have looled&nbsp; the issue&nbsp; guides,but I can not find what I&nbsp; want to solve my problem

…

---Original--- From: "Glenn ***@***.***&gt; Date: Sat, Dec 7, 2024 01:27 AM To: ***@***.***&gt;; Cc: ***@***.******@***.***&gt;; Subject: Re: [ultralytics/ultralytics] zh/guides/yolo-common-issues/(Discussion #11739) @sjocnjfjd your YAML file seems mostly correct, but be cautious with the file path formatting. Replace D:python新建文件夹 (2)/hakusenninnshiki.v2i.yolov11/lane.yaml with a properly formatted path (e.g., D:/python/新建文件夹 (2)/hakusenninnshiki.v2i.yolov11/lane.yaml). Ensure all dataset paths are accessible and the Roboflow url is valid. If issues persist, refer to the YOLO Common Issues guide for troubleshooting. — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: ***@***.***&gt;

[glenn-jocher]

@sjocnjfjd thank you for your comment. If you've already reviewed the issue guides and couldn't find a solution, please provide specific details about your problem, such as the error message or unexpected behavior you're encountering. This will help us assist you more effectively. Additionally, you can reference the YOLO Common Issues guide for further troubleshooting. Let us know how we can assist!