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how using yolov5 with donkeycar ? #11826
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👋 Hello @mohamedesmael10, 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>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started: git clone https://github.com/ultralytics/yolov5 # clone
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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 |
@mohamedesmael10 hi there! To use YOLOv5 with Donkeycar, you can follow these steps:
Please note that integrating YOLOv5 with Donkeycar can be a complex task, so you may need to refer to the specific documentation and codebase of Donkeycar for more detailed guidance on how to modify the I hope this helps, and good luck with your project! Let me know if you have any further questions. |
Donkeycar does have a Stop Sign Detector, but currently uses Mobilenet V2 but requires a Google Coral TPU. There is an issue to address this autorope/donkeycar#953. This is the source code to the Donkeycar 'part' stop_sign_detector.py and here is where it is integrated into the vehicle pipeline complete.py. Here is some high level documentation that describes the donkeycar architecture: parts. |
Hi @Ezward! Thank you for sharing the information and providing the links to the relevant code and documentation in the Donkeycar repository. Based on the provided links, it seems that Donkeycar already has a Stop Sign Detector implemented using MobileNet V2 and requires a Google Coral TPU. You mentioned that there is an open issue to address this (autorope/donkeycar#953). To utilize YOLOv5 with Donkeycar, you can start by referring to the existing implementation of the Stop Sign Detector in Donkeycar and use it as a starting point. You can modify the code to use YOLOv5 instead of MobileNet V2 for object detection. Make sure to follow the Donkeycar architecture and integrate your modified code into the vehicle pipeline. If you need further assistance or have specific questions about integrating YOLOv5 with Donkeycar, feel free to ask. Good luck with your project! |
👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help. For additional resources and information, please see the links below:
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how using yolov5 with donkeycar ?
I'm trying to use yolov5 to detect a stop sign and send a command to the car to stop
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