Comments (6)
The problem may be due to the version of the environment. The CUDA version I'm using is 11.4, and the TensorRT version is 8.4.x. Remember to deploy on a device that matches the environment used for converting ONNX to TensorRT.
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好的,谢谢,我跑onnx模型没什么问题。项目很惊艳,不过速度慢了点,大佬在提升速度有什么好的建议吗,非常感谢。
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inference速度會受cpu/gpu的影響,另外object detection和lanes detection使用上沒有平行運算所以inference相對較慢,也許可以使用multi-Progress的方式去處理會有所提升,在模型方面可以試著轉換fp16的精度去提升。
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好的,感谢感谢
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demo配置中的模型文件和视频文件上哪里可以下载得到?谢谢!
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可以參考以下issue :
#1 (comment)
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Related Issues (20)
- accuracy of calcDistance(self, boxes) function HOT 4
- Use GPU HOT 1
- Data testing HOT 3
- 请问图像输入分辨率有要求吗,yolov8m的图像输入是否需要跟车道先检测一致呢 HOT 1
- File "F:\Yolop\Vehicle-CV-ADAS\TrafficLaneDetector\ultrafastLaneDetector\ultrafastLaneDetectorV2.py", line 426, in process_output return np.array(list(lanes_points.values())), list(lanes_detected.values()) ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (4,) + inhomogeneous part. HOT 5
- pycuda._driver issue HOT 1
- Errors while running!! HOT 7
- Documentation Requirement
- video demo
- 您好,小白请教一下,这个文件是需要自己建立的吗? HOT 1
- convertPytorchToONNX.py
- python demo.py HOT 1
- python demo.py
- 运行demo.py报错 HOT 7
- Whether there is a train and test code HOT 2
- qt.qpa.xcb: could not connect to display :.0 HOT 1
- errors in the bird's-eye view and the road detection model not working properly
- An error occurred when demo.py was running HOT 1
- Update for yolov10 support HOT 1
- demo.py运行onnx模型时报错 HOT 1
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