Comments (3)
π Hello @wereign, thank you for your interest in Ultralytics YOLOv8 π! 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 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.
Join the vibrant Ultralytics Discord π§ 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.
from ultralytics.
@wereign aloha!
Thank you for your detailed inquiry regarding the integration of Comet ML with YOLOv8 for hyperparameter tuning. Your suggestions for enhancing the flexibility of experiment tracking during the tuning process are indeed valuable.
Currently, the Comet ML integration with our YOLOv8 models does not directly support custom configurations for each tuning iteration out-of-the-box. However, your proposed methods are interesting and could potentially be implemented as enhancements to our existing framework.
For now, as a workaround, you might consider manually controlling the experiment creation within your tuning loop by using the Comet ML API directly in your script. This way, you can set custom names, API keys, or any other configuration parameters for each experiment iteration.
We appreciate your feedback as it helps us improve the integration features. I'll forward your suggestions to our development team for consideration in future updates.
Thank you for using Ultralytics YOLO models and for your proactive approach to enhancing the toolset!
from ultralytics.
π 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 π and Vision AI β
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Related Issues (20)
- Predict result HOT 2
- How to determine which predict layer is responsible for detection ? HOT 4
- about drawing the ground truth box HOT 1
- YOLOv8 OBB height issues HOT 1
- Custom Data Training for Action Recognition with YOLOv8 HOT 4
- Invalid parameter settings during trainingοΌ HOT 2
- Got a wrong result in onnx C++ detect YoloV8 HOT 2
- AttributeError: 'Pose' object has no attribute 'detect' HOT 2
- How to prune yolov8s segmentation model ? HOT 2
- Raspberry Pi 4B NCNN樑ε ζ¨ηζΆζ₯ι HOT 3
- 2-step detection validation HOT 1
- It has been modified to receive a total of 3 inputs: RGB, Depth, and thermal image. HOT 1
- Detection Validator not working for YOLOv10 models HOT 1
- Failed to call AMP HOT 1
- When converting the ncnn model in Windows , the pnnx.exe system cannot move files to different disk drives HOT 1
- problem when loading my quantized model HOT 8
- Validation script reporting near-perfect results for mismatched model and dataset HOT 7
- How do you combine yolov8 with tracking algorithms other than botsort and bytetrack? HOT 10
- Pre & Post Processing (Yolov8 OBB, TFLite C++) HOT 5
- While using track and persist=True, different detections based on image position HOT 3
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