Comments (5)
👋 Hello @123456dad, 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.
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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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You can validate your model with a non-square image size by specifying the imgsz
argument in the val
command. Here's how you can do it for a 640x384 image size:
yolo val model=yolov8n.pt imgsz=640,384
This sets the width to 640 and the height to 384 for the validation process. Make sure your model has been trained with similar dimensions for best results!
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But i get same problem like #11860, set imgsz=384,640 failed
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It seems like you're encountering an issue with setting non-standard image sizes. Ensure your model was trained with the capability to handle these dimensions. You can try specifying the image size directly in the validation command like this:
yolo val model=yolov8n.pt imgsz=384,640
Make sure the width and height are placed correctly as per your requirement. If the problem persists, please check if there are any specific constraints or preprocessing steps in your model's training configuration that might affect this.
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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)
- I want to get embeddings of each detected object HOT 2
- training width and height support non-square? HOT 2
- corrupt JPEG data: premature end od data segment | training yolov8m HOT 4
- how can I export onnx when I have dynamic block HOT 1
- pruning yolov8 model HOT 7
- Add all class softmax probability scores to results object HOT 2
- How to Resize window when webcam streaming HOT 2
- Post-processing issues after converting YOLOv8-OBB to NCNN model for inference HOT 4
- Tuner occasionally locks up waiting on a completed training run HOT 6
- Objects in rotated images become smaller than their corresponding boxes. HOT 6
- add epochs training HOT 6
- Can YOLOv8 be used for real-time counting on a camera? HOT 3
- Problem with train_batch bounding boxes HOT 8
- DDP: multi node training erro HOT 1
- When I train yolov8l-cls, Closing dataloader mosaic is not work. HOT 2
- How to save the printed log output to a file HOT 2
- Add new task: Relationship Detection :rocket: HOT 1
- The confusion matrix generated by model validation is inconsistent with the validation summary results HOT 7
- Getting warning for train & Val is not writeable why ? HOT 4
- yolov10x to engine int8 error HOT 5
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