Comments (1)
Hello! Currently, we don't have a specific C++ example for object tracking with YOLOv8 in our official documentation or repositories. However, you can use the exported models (e.g., ONNX, TensorRT) in a C++ environment. For integrating these models into your C++ projects, you might consider using libraries like OpenCV or specific inference engines that support the model format you've exported to.
For more details on exporting models, you can refer to our documentation on model export modes. If you need further assistance or specific guidance on setting this up in C++, please let us know! We're here to help. 😊
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Related Issues (20)
- 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 3
- 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 4
- 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 1
- 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 4
- confusion matrix single HOT 2
- How to add the bounding box values to the labels text files during prediction with a trained YOLO-V8 instance segmentation model? HOT 4
- Class imabalance dataloader HOT 1
- Replace confidence score for forward pass for. yolov8. Default is 0.25 HOT 5
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