segmentation fault #1620
Comments
|
Hello @IceAndSea, 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://www.ultralytics.com or email Glenn Jocher at [email protected]. RequirementsPython 3.8 or later with all requirements.txt dependencies installed, including $ pip install -r requirements.txtEnvironmentsYOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
Status
If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), testing (test.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit. |
|
Here is my bug.
(python38) my608@my608-ubuntu:~/object/yolov5$ python detect.py --source ./data/images/ --weights weights/yolov5x.pt --conf 0.4
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='./data/images/', update=False, view_img=False, weights=['weights/yolov5x.pt'])
Using torch 1.7.0 CUDA:0 (GeForce GTX 1080, 8116MB)
Fusing layers...
段错误 (核心已转储)
I use your weights, bus.jpg, zidane.jpg.
…------------------ 原始邮件 ------------------
发件人: "ultralytics/yolov5" <[email protected]>;
发送时间: 2020年12月7日(星期一) 上午8:57
收件人: "ultralytics/yolov5"<[email protected]>;
抄送: "风中逍枫"<[email protected]>;"Mention"<[email protected]>;
主题: Re: [ultralytics/yolov5] segmentation fault (#1620)
Hello @IceAndSea, 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://www.ultralytics.com or email Glenn Jocher at [email protected].
Requirements
Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:
$ pip install -r requirements.txt
Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
Google Colab Notebook with free GPU:
Kaggle Notebook with free GPU: https://www.kaggle.com/ultralytics/yolov5
Google Cloud Deep Learning VM. See GCP Quickstart Guide
Docker Image https://hub.docker.com/r/ultralytics/yolov5. See Docker Quickstart Guide
Status
If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), testing (test.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or unsubscribe.
|
|
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions. |
|
Hello @kim0418, Thank you for reporting this issue. The "segmentation fault" error usually indicates a memory access violation. This can be caused by various factors such as incompatible versions of dependencies or other system-related issues. To help us diagnose the problem, could you please provide more information about your environment? Specifically, the versions of the following:
Additionally, it would be helpful if you could provide the complete stack trace and any relevant error messages that you receive. This will allow us to narrow down the cause of the issue and provide you with a more specific solution. Thank you for your cooperation. We'll do our best to assist you further. Best regards, |
|
Thank you for your answer. @glenn-jocher python=3.11.2, torch=2.1.0. |
|
Hi @kim0418, Thank you for providing the information. It seems like the issue might be related to the versions of Python and PyTorch you are using. Currently, YOLOv5 is only officially tested and supported with Python 3.8 or later, and PyTorch 1.7 or later. Additionally, CUDA and CUDNN are optional dependencies for YOLOv5 but are highly recommended for optimal performance, especially if you are using a GPU. You can check if CUDA and CUDNN are installed separately on your system by running the appropriate commands or checking the system documentation. To resolve the issue, I would suggest the following steps:
After completing these steps, please try running the Thank you for your understanding and cooperation. Let us know if you need further assistance. Glenn Jocher |
|
Thank you so much for your reply @glenn-jocher . I will attach the version of my raspberry pi4, the Python version, the torch version, and the code where the current seg fault occurs. Can CUDA, CUDN be installed and used even without GPU? Thank you so much for your answer. Kim0418 |
|
Thank you for providing the additional information, @kim0418. Based on the details you've shared, it seems that you are using a Raspberry Pi 4, which does not have a compatible GPU for CUDA or CUDNN. CUDA and CUDNN are typically used for GPU acceleration in deep learning tasks. In your case, since you don't have a GPU available, you won't be able to install or use CUDA or CUDNN. YOLOv5 can still run on the CPU without GPU acceleration, but it may be slower compared to running on a GPU. Regarding the segmentation fault issue you encountered, it is likely caused by the incompatible versions of Python and PyTorch. As mentioned earlier, YOLOv5 is officially tested and supported with Python 3.8 or later, and PyTorch 1.7 or later. Since you are using Python 3.11.2 and PyTorch 2.1.0, this could be a potential source of the issue. To resolve the segmentation fault issue, I recommend the following steps:
After completing these steps, please try running the If you encounter any further issues or have any additional questions, please feel free to ask. We're here to assist you. Kim0418 |
|
Hello @glenn-jocher I tried breakpoint on detect.py . Line 88 Do you happen to know the cause? |
|
Hello @kim0418, Thank you for providing the additional information. The segmentation fault occurring at line 88 of To investigate further, we need to understand the execution flow and the dependencies within the Thank you for your patience and cooperation. We'll do our best to assist you further. Glenn Jocher |
|
Hi @kim0418, |
|
sorry @Nicoll2020 |
|
Hi @kim0418, I apologize for the inconvenience. Unfortunately, I haven't been able to resolve the issue yet. I apologize for not being able to help you at this time. Thank you for understanding. Glenn Jocher |
|
Thank you for your consideration to help. |
|
@Nicoll2020 thanks for reaching out to us! We appreciate your interest in YOLOv5 and we're here to assist you. Regarding your query, could you please provide more details about the specific issue or error message you're encountering? This will help us better understand the problem and provide you with appropriate guidance. Please include any relevant code snippets, error messages, or steps to reproduce the issue. Additionally, let us know the versions of Python, PyTorch, and any other relevant software you're using. We'll do our best to assist you and troubleshoot the problem. Thank you for your cooperation. Glenn Jocher, Ultralytics YOLOv5 |
|
I have the same issue. |
|
@SohaibKtb i understand that you're facing the same issue and have been trying to resolve it for a week without success. I'm here to help you troubleshoot and find a solution. To assist you better, could you please provide more details about the specific issue you're facing? It would be helpful if you could provide any error messages or relevant code snippets. Additionally, please let me know the versions of Python, PyTorch, and any other relevant software you're using. With this additional information, I'll be able to provide you with more targeted guidance to solve the problem. Thank you for your patience, and I'm looking forward to helping you further. Glenn Jocher, Ultralytics YOLOv5 |
|
Hello @glenn-jocher, thanks for your fast response. so I am using only these packages: python=3.11.2 with yolov5 and with my own '.pt' file. on Raspberry pi 4: Linux raspberrypi 6.1.0-rpi4-rpi-v8 #1 SMP PREEMPT Debian 1:6.1.54-1+rpt2 (2023-10-05) aarch64 GNU/Linux the error that I am receiving is only "segmentaion fault" after "fusing layers...." message |
|
@SohaibKtb hello, Thank you for providing the details of your setup and the error you are encountering. The "segmentation fault" error after the "fusing layers...." message indicates that there is a problem during the fusion process. One possible cause could be an incompatibility between the versions of PyTorch and TorchVision you are using. First, please ensure that the versions of PyTorch and TorchVision are compatible with each other. It is recommended to use the latest stable versions for both packages. In your case, you mentioned that you are using PyTorch 2.1.0 and TorchVision 0.16.0. However, please note that PyTorch 2.1.0 is not a valid version and it should be something like 1.1.0 or 1.2.0. Next, please check if your '.pt' file is compatible with the YOLOv5 version you are using. Ensure that the model architecture and weights are compatible. If you trained the model using a different version of YOLOv5, it may cause compatibility issues. Additionally, you mentioned that you are using a Raspberry Pi 4. Please make sure you have sufficient resources (RAM, GPU) available on your Raspberry Pi, as running YOLOv5 on resource-constrained devices can sometimes cause issues. If you have already checked these points and the issue still persists, please provide the complete error message and any relevant code snippets so that we can further investigate the problem. Best regards, |
|
Hi @SohaibKrb, You are right, the solution is in the versions of the Pytorch, torchvsion and tensorflow. thank you so much. |
|
Hi @Nicoll2020, I'm glad to hear that you have made progress in resolving the issue. Indeed, ensuring the compatibility of the PyTorch, TorchVision, and TensorFlow versions is crucial for a smooth execution of YOLOv5. If you have any further questions or need assistance with anything else, feel free to reach out. We're here to help! Best regards, |
|
Hi. @glenn-jocher |
|
@kim0418 hello, Thank you for reaching out. Lowering the versions of PyTorch and Python might help resolve the issue. In some cases, YOLOv5 may have compatibility issues with certain versions of PyTorch and Python. By downgrading to older versions that are known to work well with YOLOv5, you may be able to overcome the problem. I would recommend trying a different combination of PyTorch and Python versions and see if the issue persists. You can also refer to the YOLOv5 documentation or community forum for any specific version requirements or recommendations. Please let me know if you have any further questions or need more assistance with this. Thank you. Glenn Jocher, Ultralytics YOLOv5 |
Hi, @Nicoll2020 may i know which version Pytorch, torchvsion you used to make it work ? I have same problem with raspberry pi 4 too |
|
Hi~I am using Raspberry pi 4 too. |
|
Hello, it was torch and torchvision version issue I downgraded python to version 3.9.2, since version 3.11.2 can't work with pytorch less than version 2.1.0 I also downgraded raspberry pi OS to "2022-09-22-raspios-bullseye-arm64", since the latest one by defaut python version on the latest OS is 3.11.2 so the versions are: python=3.9.2 this is the setup that worked for me thanks for your help 🙏 |
|
Good news! I use your setting,i can run yolo on raspberry pi 4 .BUT! |
|
@SohaibKtb hello, That's great to hear that you were able to run YOLOv5 on your Raspberry Pi 4. Congratulations on the progress! Regarding the warning you mentioned in the screenshot, it appears to be related to the deprecation of certain functions in PyTorch. These warnings are informational and should not affect the functionality of YOLOv5. As long as you are able to run YOLOv5 successfully and obtain the desired results, these warnings can be safely ignored. If you have any further questions or need assistance with anything else, please feel free to ask. Happy YOLOv5-ing! Glenn Jocher, Ultralytics YOLOv5 |
|
hello. @glenn-jocher python3 detect.py --source data/images --weights yolov5s is executed. but python3 detect.py --source 0 --weights yolov5s.The pt does not work. I will attach the error that occurs when executing the code below. I confirmed that the camera is operating, and there is no abnormality. Code when implementing docker-container : sudo docker container run -it -d --name <container_name> --privileged --device /dev/vchiq --device /dev/video0:/dev/video0 ultralytics/yolov5:latest-arm64 |
|
@kim0418 hi, It's unfortunate that you were unable to resolve the issue and had to resort to implementing it as a Docker container. Thank you for sharing the error message you encountered. In the error message, it seems that the code fails to execute when using the webcam as a source (--source 0) with the yolov5s weights. It's good to know that you have confirmed that the camera is operating fine. To further investigate the issue, it would be helpful to obtain additional information, such as the versions of PyTorch, TorchVision, and Python that you are currently using. Additionally, providing the complete error stack trace could assist in identifying the root cause of the problem. Looking forward to hearing back from you with more details. Glenn Jocher, Ultralytics YOLOv5 |
|
@glenn-jocher I'm sorry to contact you all of a sudden. Can I access dockerfile and modify the code? Can I solve this without modifying the dockerfile? |
|
|
|
@kim0418 this error usually occurs when the Here's an example of how you can add these steps to your Dockerfile: # ... previous steps ...
# Update package list and install g++
RUN apt-get update && apt-get install -y g++
# Build Meson setup
RUN meson setup build
# ... continue with the rest of your Dockerfile ...Make sure to rebuild your Docker image after making these changes. By including the Let me know if you have any further questions or need additional assistance. Glenn Jocher, Ultralytics YOLOv5 |
|
@glenn-jocher Thank you very much for your help. I tried using journalctl to find the error. |
|
@kim0418 hi there, Thank you for sharing the information about the error you encountered and for investigating further using journalctl. Regarding the unicam error message you found (unicam fe801000.csi: Wrong width or height 640x480), it seems to indicate a mismatch between the expected width/height and the actual values. To address this issue, you may need to modify the camera configuration settings or parameters to ensure that the width and height values are set correctly. You can refer to the documentation or user manual of the camera module or seek assistance from the camera module manufacturer for guidance on adjusting the settings. Please note that the specific files or settings to be modified can vary depending on your camera hardware and the software you are using. It's recommended to consult the camera documentation or seek support from the camera module provider for appropriate guidance. If you have any further questions or need additional assistance, please let us know. Glenn Jocher, Ultralytics YOLOv5 |
|
hello, @glenn-jocher my jornalctl log is happen 'unicam fe8010000.csi : Failed to start media pipeline: -22'. Do you know anything about this? |
|
@kim0418 hi there, To address the issue you're experiencing with the 'unicam fe8010000.csi : Failed to start media pipeline: -22' error message in the journalctl logs, there are a few things you can try when creating your Docker container. Firstly, it's recommended to run the Docker container with the For example: Make sure to replace These steps should help provide the necessary permissions for the Docker container to access the camera device. If the issue persists, please provide additional details or error messages, if any, so that we can further investigate and assist you with resolving the problem. Best, |
|
hello @glenn-jocher Do you happen to have cap in dataloaders.py file.Can I write the location or information of the camera on behalf of cv2.VideoCapture(0)? |
|
@kim0418 it looks like you've encountered an error related to the libcamera library used for accessing the camera in the dataloaders.py file. The error message indicates that the library is unable to access the camera. This could be due to an issue with the specific camera model or its driver support. In the dataloaders.py file, you can attempt to specify the camera location or information by modifying the cv2.VideoCapture() call. Instead of using cv2.VideoCapture(0), you can provide the specific device index or the path to the camera. For example: # Instead of
cap = cv2.VideoCapture(0)
# You can try
cap = cv2.VideoCapture('/dev/video0') # Replace '/dev/video0' with the actual camera device pathBy specifying the device path directly, you can potentially address the issue related to libcamera. Please ensure that the path you specify matches the actual camera device on your system. If you continue to encounter issues, it may be beneficial to refer to the libcamera documentation or seek support from the libcamera community for guidance on resolving the problem. Let me know if you require any further assistance. |
Hi @kurniadi92 |
|
@Nicoll2020 Thank you for sharing the versions of PyTorch, TorchVision, and TensorFlow that worked for you on Raspberry Pi 4. This information will be helpful for others facing similar issues. If you have any further questions or need assistance with anything else related to YOLOv5, feel free to reach out. |
|
@glenn-jocher With your help, the camera works normally. Thank you. The camera was successfully operated. So it doesn't seem to be interrupted because there is no waitkey during the operation. Where should I enter the interruption key? |
|
@kim0418 I'm glad to hear that the camera is now functioning normally. To add an interruption key for the camera operation, you can use the following code snippet as an example to incorporate the use of the 'waitKey' function from OpenCV, which allows you to capture a key press event. You can define the key to be used for interruption, such as ESC key (27 key code) in this example: import cv2
# Open the camera
cap = cv2.VideoCapture(0)
while True:
# Capture the frame
ret, frame = cap.read()
# Display the frame
cv2.imshow('Camera Feed', frame)
# Check for interruption key (ESC key)
if cv2.waitKey(1) & 0xFF == 27:
break
# Release the camera and close the window
cap.release()
cv2.destroyAllWindows()In this example, the You can modify this example as needed to fit into your specific code structure. If you have any further questions or need additional assistance, feel free to ask. |
|
I think I need to write waitkey on the dataloaders.py file, which line should I write? |
|
@kim0418 in the dataloaders.py file, you can incorporate the use of the 'waitKey' function within the 'load_image' function to facilitate the ability to capture key press events and introduce an interruption mechanism. Specifically, you can add the 'waitKey' command right after displaying the image using OpenCV's 'imshow' function. Here's a general example: import cv2
def load_image(self, index):
...
# Your existing code to read and process the image
# Display the image
cv2.imshow('Image', image)
# Check for interruption key (ESC key) and wait for a key press for a specified duration (e.g., 1 millisecond)
if cv2.waitKey(1) & 0xFF == 27:
break
# Continue with other operations or processing as needed
...By adding the 'waitKey' command and checking for a specific key press (e.g., the ESC key) within the 'load_image' function, you can enable the interruption capability when images are loaded and displayed in your YOLOv5 pipeline. Please ensure to adapt this example to fit into your dataloaders.py file and the specific logic and requirements of your YOLOv5 implementation. If you have any further questions or need assistance with the integration, feel free to ask. |
|
hello @glenn-jocher Thank you very much for your help. There is a problem in training the model this time, so I would like to ask you a question. When I use yolov5 train.py , there are so many classes I want to learn that it is difficult to decompress at once. Therefore, I want to use the method of learning one class and then the next class. Is there any way? |
|
@kim0418 You can indeed train your YOLOv5 model to learn classes incrementally. This can be achieved by following a few steps:
By employing this incremental learning approach, you can gradually expand your model's knowledge to encompass all desired classes. If you have any further questions or require detailed guidance on implementing this approach, feel free to ask. |
|
Can you give me an example? |
|
@kim0418 certainly! Here's an example of how you can approach incremental learning with YOLOv5:
This approach allows you to gradually introduce and train on additional classes in a step-by-step manner. The data configuration file is updated to reflect the classes being learned at each phase, and training is performed accordingly. Feel free to customize this example based on your specific class sets and training requirements. If you have further questions or need additional assistance, please let me know. |
|
There are 40 classes we are trying to learn. And all the images in the class are 100,000. Cracks occur to decompress this on the google drive, making it difficult. That's why I want to know how to learn in division. The gradual method has the difficulty of finally releasing all images of 40 classes. |
|
@kim0418 i understand the challenge with handling a large dataset on Google Drive. An alternative approach for handling a large number of classes and images would be to leverage a technique known as "Curriculum Learning." With Curriculum Learning, you can structure the learning process by initially training on a subset of the classes and gradually introducing additional classes as the model's training progresses. This approach allows you to manage the large dataset more effectively and incrementally introduce complexity to the learning process. Here's a high-level overview of how you can adopt Curriculum Learning for your scenario:
By following this approach, you can systematically introduce the entire 40-class dataset to the model while managing the computational and storage challenges associated with a large dataset. If you have specific questions or require further assistance in implementing Curriculum Learning with YOLOv5, feel free to ask. |
|
What is your raspberry os |
|
version |
|
this started happening to me when I changed to bookworm |
|
@pythonstuff8 i apologize for any confusion, but as the maintainer of the Ultralytics YOLOv5 repository, I don't have a specific Raspberry Pi OS version to provide. However, if you're experiencing issues after changing to Debian Bookworm, it's important to ensure that all dependencies for YOLOv5 are compatible with your current OS version. Debian Bookworm is a testing branch that will eventually become the next stable release after Bullseye. Since it's a testing branch, you might encounter more issues due to the less stable nature of the packages. Here are a few steps you can take to troubleshoot the issue:
If you continue to experience issues, you might want to consider using a more stable version of Raspberry Pi OS, such as the latest stable release, or seeking assistance from the Raspberry Pi community forums or Debian support channels. |
when I run detect.py, the code display 'segmentation fault' , my operating system is ubuntu 20，pytorch is 1.7, python 3.8.
Can you tell me how to solve this problem?
The text was updated successfully, but these errors were encountered: