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The GPU is not used when running detection with YOLOv5 #13171
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👋 Hello @Angelinnp, 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 |
@Angelinnp hello, Thank you for reaching out and providing detailed information about your issue. It looks like you're experiencing difficulties with GPU utilization on your Jetson Nano while running YOLOv5 detection. To better assist you, could you please provide a minimal reproducible code example? This will help us understand the context and reproduce the issue on our end. You can find more information on creating a minimal reproducible example here. This step is crucial for us to investigate and provide a solution effectively. In the meantime, here are a few steps you can take to troubleshoot the issue:
If the issue persists after trying the above steps, please share the minimal reproducible code example, and we will investigate further. Thank you for your cooperation, and we look forward to resolving this issue with you. |
The following is the detect program code that I run. please help me solve this problem. |
The following is the detect program code that I run. please help me solve this problem. import argparse import torch FILE = Path(file).resolve() from models.common import DetectMultiBackend #Konfigurasi GPS Sensor #Konfigurasi GPS Information
latitude = None @smart_inference_mode()
def parse_opt(): def calcDist(myClass, myWidth): def sendcalc232(category, confidence, x, y, w, h, dist): def write_read(x): def main(opt): if name == "main": |
Hello @Angelinnp, Thank you for sharing your detection code. I see that you've integrated GPS sensor data and are running YOLOv5 on a Jetson Nano. Let's address the issue of the GPU not being utilized. Steps to Ensure GPU Utilization
Example Code AdjustmentsHere are some adjustments to ensure GPU utilization:
Additional DebuggingIf the above steps do not resolve the issue, please provide the output of the following commands:
This will help us understand if PyTorch is correctly detecting your GPU. Thank you for your cooperation, and we look forward to resolving this issue with you. If you have any further questions or need additional assistance, please feel free to ask. |
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YOLOv5 Component
Multi-GPU
Bug
When I run the YOLOv5 detection code, it still uses CPU. And it causes the detection process to be slow, I get fps = 0.4. For installation, CUDA has been activated but the CUDA on the Jetson nano is still not used. Please give me an explanation why it happened and what is the solution?
The following are the versions of CUDA 10.2.300 and pytorch 2.3.1 that I have installed.
I use the virtual environment Python 3.8.0. Please tell which version of Pytorch and CUDA suits my python virtual environment. Please help me
Environment
Minimal Reproducible Example
No response
Additional
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Are you willing to submit a PR?
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