Comments (1)
Hi, I directly took the average FPS printed during inferecne as the running speed of my tracker. The profile_model.py
needs the inputs manually prepared, and I do have a messy version. It would be an useful reference, whereas normally it will provide a faster result. Besides, the amount of FLOPs also cannot truly reveal the computation latency of algorithms. The most simple and straightforward way is still running them on the same machine. If you want it, I think you can smoothly modify a script from STARK or OSTrack.
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Related Issues (14)
- 出现“ValueError: The number of weights does not match the population”问题 HOT 5
- 训练结果很差 HOT 1
- Run on custom video HOT 1
- The checkpoints type did not match expectations HOT 1
- About train HOT 1
- about the calculation of attention mask M HOT 1
- about appendix C3 HOT 1
- about nfs30 test?could you tell how to test it? HOT 2
- About computing the mask M HOT 2
- About the 'threshold' inference HOT 1
- Why didn't your model directly apply the mask with infinity and then apply the softmax function? Could you please explain the following code? HOT 1
- How to set learning rate in training? HOT 5
- GRM-L320效果 HOT 1
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