Comments (8)
👋 Hello @frl93, 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.
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 🚀
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
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@frl93 hi there! It seems like there might be some confusion with the YOLOv5 installation. YOLOv5 is not typically installed as a Docker container, so I suspect that may be the issue here.
Did you follow the installation guide from our YOLOv5 documentation at https://docs.ultralytics.com/yolov5/ ? The installation steps there should help you set up YOLOv5 properly on your laptop.
Let me know if you have any further questions or need assistance with the installation process!
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Hi once again @glenn-jocher!
If you are asking me if I have followed this steps:
Te answer is NO. Where I have done, as you can see in the first image of my previous comment, is first applied this command:
And then, I have selected the gpu option with this command:
May be I have not followed properly the installation steps and before applying "sudo docker pull ultralytics/yolov5:latest" I must install Yolov5 with the steps of the picture that I show in this comment? In this case do I need to delete the docker container that has been created in my docker desktop application?
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@frl93 Thanks for providing the additional details. It looks like there might be some confusion with installing YOLOv5 using Docker. The steps you've followed are indeed for setting up YOLOv5 within a Docker container, however, it seems like you may have missed some crucial setup steps.
To clarify, if you want to use YOLOv5 via Docker, you should follow the steps outlined in the YOLOv5 documentation you shared first to set up the necessary environment and then use the Docker command you mentioned earlier.
You can remove the previous Docker container and then follow the YOLOv5 documentation steps to properly set up the environment before using the Docker command.
Let me know if you need any further assistance!
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@frl93 if you want to use via Docker, create the Dockerfile in your project and specify the image of yolov5 like this:
FROM ultralytics/yolov5:latest
#Give the work directory a name
WORKDIR /app
COPY . /app
CMD ["python", "train.py"]
and then you can check the training directly after performing docker build --tag "yolov5-image" .
and then docker run
respectively
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Hi @enislalmi!
Thanks for your comment. I have a copule of questions related to it:
- I am not very familiarized with the dockers as I have started to work with them a month ago. Where can I create the Dockerfile that you have mentioned
- Where do I have to specify the image of yolov5 in the file that you have tell me.
In addition, I have another trouble. The following picture shows my Docker Desktop at Windows, and you can see that I have a yolov5 image stopped.
If I applied the command: "docker run --ipc=host -it --gpus all --name fermin_yolo5 ultralytics/yolov5:latest". It tells me that the container already exists.
Can you tell my which is the command that I need to apply to activate once again that image.
Thanks!
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Hi everybody!
I have been able to up the docker and get inside it to make the desired predictions.
To up the docker you must use: docker start <container_name>, in my case "fermin_yolo5"
To get inside the container once it is in "run" mode, you must use: docker exec -it <container_name> bash
Thanks!
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@frl93 glad to hear you got the Docker up and running! If you encounter any further questions or run into any issues, feel free to ask. Good luck with your predictions!
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Related Issues (20)
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- A Problem Concerning the Custom Dataset for Object Detection Using YOLOv5 HOT 7
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