Comments (3)
👋 Hello @ZeroLelouch1205, 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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@ZeroLelouch1205 hi there! It seems like you are encountering a conflict when importing torch and tensorrt. This can occur due to compatibility issues between the two libraries. You might want to try using a virtual environment to manage the dependencies more effectively.
You can create a new virtual environment, install the required packages, and then run your YOLOv5 code within that isolated environment. This should help to resolve the import conflicts you are experiencing.
If you need further assistance with setting up a virtual environment or managing dependencies, please refer to the official documentation or feel free to ask for help. Good luck with your implementation, and thank you for your willingness to submit a PR!
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👋 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:
- Docs: https://docs.ultralytics.com
- HUB: https://hub.ultralytics.com
- Community: https://community.ultralytics.com
Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!
Thank you for your contributions to YOLO 🚀 and Vision AI ⭐
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Related Issues (20)
- Saving Augmented Images HOT 2
- TensorRT is slower than pytorch HOT 7
- The accuracy of the .pt model will decrease after being converted to .engine model. HOT 6
- Guide on how to utilize 'Weighted Loss' method in yolov5 custom training HOT 3
- Extract information of Coordinates HOT 3
- ModuleNotFoundError: No module named 'models.yolo' on custom trained weight. Is it a bug? HOT 7
- Why 'targets' values from train.py are different from the ground truth annotation in txt files? HOT 4
- A question between yolov5s-p5 and the parameters imgsz HOT 3
- YOLOv5 Output Size Issue HOT 2
- What Should Be the Output Size of Predictions for Object Detection? HOT 3
- stuck training on NVIDIA H100 HOT 5
- Problem running inference on YOLOv5s for custom trained model using Python HOT 3
- The confidence loss (obj_loss) scales of the training and validation sets are inconsistent. HOT 2
- Can I load YOLOv5 model without Ultralytics HOT 3
- The memory usage of the 0 card will increase until it is out of memory HOT 5
- scale_masks fucntion HOT 2
- cls loss HOT 2
- Problem with training for a single class HOT 6
- Issue when try to validate openvino format model HOT 4
- Is there a problem with the way I fine-tuned the YOLOv5? HOT 4
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