Low evaluation of custom data sets and storage path problems

[wx-ikll007700]

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Question

Hello, I am very interested in your project. I found my own data set and tried it. My data set has only one category. I trained my own detection weight (marked with gt file). After I ran val.py, I found that the results of bytetrack were all 0, and the results of deepocsort and botsort were the same, and they were very low. I felt that the detector did not work, but both my folders were generated.

[wx-ikll007700]

According to the results, my gt file should have been detected, but my detector doesn't seem to have detected my data.When I use bytetrack, the results are all 0, and the results of deepocsort and botsort are the same but extremely low.

[lmaple24327]

Hello, at present, I am also using YOLOv8 and BOTSORT for target tracking, and I also use my own custom data set, but the evaluation effect is also very poor. May I ask how do you use OCSORT? Do you use this program with many warehouses?

根据结果，我的 gt 文件应该已经被检测到，但我的检测器似乎没有检测到我的数据。当我使用 bytetrack 时，结果都是 0，deepocsort 和 botsort 的结果相同但极低。

[wx-ikll007700]

Hello, at present, I am also using YOLOv8 and BOTSORT for target tracking, and I also use my own custom data set, but the evaluation effect is also very poor. May I ask how do you use OCSORT? Do you use this program with many warehouses?

根据结果，我的 gt 文件应该已经被检测到，但我的检测器似乎没有检测到我的数据。当我使用 bytetrack 时，结果都是 0，deepocsort 和 botsort 的结果相同但极低。

Isn't this boxmot integrated with many tracking algorithms, and it only needs to be selected in the instruction, but my effect is very poor. It seems that I didn't put the path of the file right. Did you deploy it with this boxmot?