Comments (6)
π Hello @Sequential-circuits, thank you for your interest in YOLOv5 π! Please visit our βοΈ 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 π Bug Report, please provide a minimum reproducible example to help us debug it.
If this is a custom training β 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.
Requirements
Python>=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
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):
- Notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. 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, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
Introducing YOLOv8 π
We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 π!
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
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Hey there! π
It sounds like you're facing a challenge with the automatic installation of incompatible PyTorch versions on your Jetson Xavier. To prevent YOLOv5 from automatically installing or upgrading PyTorch and torchvision, you can modify the installation process slightly.
Instead of running the standard installation command, you can skip the automatic dependencies installation by using:
pip install --no-deps -e .
This command installs YOLOv5 without any dependencies. After that, you can manually install the necessary packages that are compatible with your system, as specified by NVIDIA for Jetson platforms.
If you need further customization, consider adjusting the requirements.txt
file to better fit your environment before running any install commands.
Hope this helps you set up YOLOv5 on your Jetson Xavier without further issues! If you have more questions, feel free to ask. π
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The problem is not during INSTALLATION, is during EXECUTION
As soon as it loads ultralytics, it starts to change torch and torchvision, among other things
And in jetson torchvision has to be compiled, so it ends up wasting a lot of time spent compiling it
It says for example:
WARNING: Ignoring invalid distribution -orch (/usr/local/lib/python3.8/dist-packages)
Installing collected packages: torch, torchvision
Attempting uninstall: torch
Found existing installation: torch 2.1.0a0+41361538.nv23.6
[Info] [DepthPacketStreamParser] 20 packets were lost
Uninstalling torch-2.1.0a0+41361538.nv23.6:
[Info] [DepthPacketStreamParser] 31 packets were lost
Successfully uninstalled torch-2.1.0a0+41361538.nv23.6
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Hey there! π
It sounds like the issue arises due to the environment setup during the execution of YOLOv5, which triggers unwanted updates to torch
and torchvision
. This is likely due to dependency checks in the code.
To prevent this, you can try setting up a virtual environment specifically for running YOLOv5. This way, any changes or installations won't affect your global packages. Hereβs how you can set it up:
- Create a new virtual environment:
python -m venv yolov5-env
- Activate the environment:
source yolov5-env/bin/activate
- Install the specific versions of
torch
andtorchvision
that you compiled for Jetson:pip install <your-torch-wheel> pip install <your-torchvision-wheel>
- Run YOLOv5 within this environment.
This should isolate the execution environment and prevent the automatic uninstallation and reinstallation of these packages. Let me know if this helps or if you encounter any other issues! π
from yolov5.
That solved the problem thanks!
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Hi there! π
I'm glad to hear that the solution worked for you! If you have any more questions or run into any other issues, feel free to reach out. The YOLO community and the Ultralytics team are always here to help.
Happy coding and best of luck with your projects! π
Warm
from yolov5.
Related Issues (20)
- challenges faced in Xtream1 tool while creating class ID's HOT 1
- Facing issues while changing class ID values HOT 1
- background color of image and other causes? HOT 1
- ModuleNotFoundError: No module named 'models.yolo'. HOT 3
- RuntimeError: The size of tensor a (80) must match the size of tensor b (60) at non-singleton dimension 3 HOT 4
- Error During TensorFlow SavedModel and TFLite Export: TFDetect.__init__() got multiple values for argument 'w' and 'NoneType' object has no attribute 'outputs' HOT 1
- hyp.finetune.yaml missing HOT 3
- Regarding the application establishment of preprocessing functions HOT 1
- Custom model exported to tfjs only outputs 100 detections
- Export torchscript with NMS HOT 1
- AttributeError: 'str' object has no attribute 'shape' HOT 3
- yolov5-pip forced boto3 consumption invalidates py3.7-9 support HOT 1
- Report errors while continuing training HOT 1
- RuntimeError: The size of tensor a (6) must match the size of tensor b (7) at non-singleton dimension 2 HOT 1
- RuntimeError: The size of tensor a (6) must match the size of tensor b (7) at non-singleton dimension 2 HOT 1
- Remove detection head HOT 2
- Issue regarding: "cannot identify image file '/content/drive/MyDrive/Data/test/images/00705.jpg'" HOT 5
- yolov5 / detect.py κ²°κ³Όλ₯Ό 10λΆμ£ΌκΈ°λ‘ μΆλ ₯νκ³ μΆμ΅λλ€ HOT 1
- How to training with EMA in model.train()? HOT 3
- close mosaic in yolov5 HOT 1
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