Comments (2)
👋 Hello @avenger123456, 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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@avenger123456 hello! 👋
Thank you for bringing this to our attention. It seems like there might be a misconfiguration issue causing the validation process to default to coco128.yaml
instead of using your custom data.yaml
. To ensure the correct dataset configuration file is being used during validation with your converted.pt
model, please make sure that the data
argument is correctly specified and points to your custom my_own.yaml
. The command should look like this:
yolo detect val data=my_own.yaml model=converted.pt imgsz=640
If the issue persists, please check if my_own.yaml
is correctly formatted and located in the expected directory. If everything seems correct yet the issue remains unsolved, there might be a need to delve deeper into the specifics of how converted.pt
was generated and if any modifications might have affected its ability to correctly reference the specified dataset.
Feel free to share more details or ask further questions if needed. We're here to help!
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Related Issues (20)
- setting the value of pixel_per_meter in distance_calculation HOT 2
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- How to disable request to www.google-analytics.com? HOT 10
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- Yolo v8 what does deterministic parameter do? HOT 2
- UnsupportedModelRegistryStoreURIException: Model registry functionality is unavailable; got unsupported URI HOT 6
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- Cold start Slow inference HOT 2
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- Error Implementing model training using YOLOv8 on Android HOT 2
- <A bug in Data Augmentation>YOLOv8-OBB HOT 10
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- AttributeError: 'Segment' object has no attribute 'detect' HOT 10
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