Comments (6)
👋 Hello @microchila, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.
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.
Join the vibrant Ultralytics Discord 🎧 community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users.
Install
Pip install the ultralytics
package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.
pip install ultralytics
Environments
YOLOv8 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 Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
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Hello! Thanks for your interest in YOLO models! 🚀
As of now, YOLOv10 hasn't been officially released or discussed in our repository. However, for segmentation tasks, you might want to explore models like YOLOv9-seg which are specifically designed for instance segmentation. We continuously work on improving and adapting our models for various tasks, so keep an eye on our updates!
For any further questions or updates, feel free to check out our GitHub repository. Happy coding!
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@glenn-jocher any chance that yolo-v10 obb will be released in the future?
from ultralytics.
Hello! Thanks for your interest in YOLO models! 🚀
While we don't have specific details on the release of YOLOv10 with oriented bounding boxes (OBB) at the moment, we are always working on advancements and new features. Stay tuned for updates from the Ultralytics team and the YOLO community. Your enthusiasm and support are greatly appreciated!
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Could you clarify if yolo v10 is supported now in this repo, and if yes, can we run pose estimation on top of it?
from ultralytics.
Hello!
As of now, YOLOv10 is not supported in this repository. For pose estimation tasks, you might want to explore using YOLOv8, which includes support for pose/keypoint estimation. We appreciate your interest and encourage you to keep an eye on future updates for new model releases and features!
Thank you for reaching out! 🌟
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Related Issues (20)
- val step slow down during training HOT 7
- Batch inference speed same than looping through a bunch of imgs HOT 3
- Using YOLOv8(seg) with SHAP HOT 5
- yolov8 object_counting in and out doesn't differentiate for defined line HOT 4
- how to set `verbose:false` so that model can predict the batches without printing anything in the terminal HOT 1
- Questions about incremental training HOT 3
- How can I use the segmentation models of previous versions? HOT 4
- yolov8-obb plot train labels maybe error HOT 2
- Error Code 2: Internal Error (Assertion cublasStatus == CUBLAS_STATUS_SUCCESS failed. ) HOT 4
- Yolov10 Can't get attribute 'SCDown' on <module 'ultralytics.nn.modules.block' from 'C:\\Users\\ZHANG\\miniconda3\\lib\\site-packages\\ultralytics\\nn\\modules\\block.py'> HOT 20
- yolov8 -- After the cache is turned on, the memory occupied by reading val data is too large HOT 5
- YOLOv10 Performance Issue: Version 3.12 Fast, But 3.11 and Below Very Slow HOT 8
- yolo8 onnx in opencv HOT 2
- Is OBB available for yolov9 and v10 ? HOT 1
- Clamping in bbox2dist HOT 2
- Question about code of position embedding in rt-detr HOT 5
- Process group init fails when training YOLOv8 after successful tunning [Databricks] [single node GPU] HOT 4
- Train with single gpu HOT 3
- Yolo8-OnnxRuntime-CPP-Inference awful output HOT 6
- confusion matrix single HOT 3
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