Comments (5)
👋 Hello @Amiya-Lahiri-AI, 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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When I get errors like that from TensorRT it is typically because some layer is not quantizable. Yolov9 might have added some layer which is not quantizable. What TensorRT version are you on?
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It looks like you're encountering a compatibility issue with TensorRT and the model's architecture. The error message Unsupported SM: 0x900
suggests that your GPU's compute capability might not be supported by the TensorRT version you're using.
Could you confirm the GPU model you're using? Also, updating to the latest TensorRT version might help if your GPU is relatively new. This can often resolve issues with unsupported layers or features in newer models like YOLOv9.
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@glenn-jocher it turns out I have a problem with the instance I was using for inference.
however I have resolved it my problem by changing to a different instance.
However thanks your support
from ultralytics.
Glad to hear you resolved the issue by switching instances! If you have any more questions or run into other issues, feel free to reach out. Happy coding! 🚀
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Related Issues (20)
- Add cv::cvtColor(image, input_image, cv::COLOR_BGR2RGB); To yolo cpp examples? HOT 2
- How to change class weights in YOLOv8n to address class imbalance? HOT 3
- Validation dataset HOT 1
- How apply segmentation from file on its image HOT 2
- How do I download an older version of ultralytics 8.1.34 and install it in editable mode. HOT 1
- How to handle false positives using Yolov8 detection model HOT 5
- [KNOWN PROBLEM] ❌ Windows inference on CPU 🚧 HOT 8
- IterableSimpleNamespace' object has no attribute 'fuse_score' HOT 3
- Three questions about training size and time
- Accuracy drop in ONNX and two class segmentation model HOT 9
- Triton reasoning onnx model takes category names HOT 1
- Rtsp connection error robustness handling HOT 37
- After installing the ultralytics library, GPU usage decreased by half and Ksampler time doubled HOT 3
- How to decode the keypoints output by YOLO-pose? HOT 5
- Unexpected change in detection probability as image is translated HOT 2
- ultralytics masks to yolo seg convertor not working HOT 10
- Features for Individual Objects HOT 1
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