Comments (3)
ifzhang
commented on May 14, 2024
We use a V100 GPU for inference and the YOLOX-X detection part takes about 30 ms using fp16. You can check whether you use fp16 for inference.
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Bruce-Si
commented on May 14, 2024
您好，我在v100上用fp16跑了demo，FPS只有2.4左右...是哪里参数没用对吗？ @ifzhang
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dcstempt
commented on May 14, 2024
我用A4000显卡，采用C++/TensorRT推理加速，YOLOv8s模型为检测头，模型推理一帧6ms，跟踪预测一帧，大概在500微秒的样子。总共一帧的预测+跟踪，大概就是6.5ms
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Related Issues (20)
- ByteTrack ncnn Android demo
- The loss value of my training has been fluctuating around a high value, there is no obvious downward trend
- ModuleNotFoundError: No module named 'yolox.tracker' HOT 2
- details about the Standard models
- I want to change the maximum ID value for tracking HOT 2
- May I only use ByteTrack to improve the result of detection ?
- Use Bytetrack with custom detector HOT 1
- Pretrained model results different from that in README.md
- Unavailable dataset "Citypersons" on the link
- 你好，训练mot17-half检测器时选取可见度大于多少的目标框？
- PIP Build dependencies error HOT 1
- id change
- How can I restrict the number of IDs being tracked without increasing the ID count in a byte track?
- how to tune the params to get best performance?
- What does match_thresh mean? HOT 1
- gtstnvtracker:obj 4 Class mismatch! 2->0
- Tracking Outputs vs Detection Outputs
- how to convet mmdetection yolox model to yolox model
- I've encountered while using the YOLOX trained model for object tracking.
- AttributeError: module 'numpy' has no attribute 'float' HOT 6
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