Training instructions for MOT 17 about memotr HOT 3 CLOSED

My bad. I forgot to upload this config file into configs but only put it here.

Now, as this commit, I have already uploaded the config file. Thanks a lot for your reminder.

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Thank you so much!!

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I think you may be conducting experiments on MOT17, and there are some things that I need to explain to you. I did not reply earlier because I was too busy in the past several days. Sorry about that.

First of all, due to the merging of several versions of code, there was a problem in passing parameters during data processing, which I have corrected in this commit.

Moreover, there are some differences in the configuration on MOT17 (compared with DanceTrack/SportsMOT):

1. The optimizer setting:

   LR_POINTS: 2.0e-5    # 1.0e-5 on DanceTrack
   WEIGHT_DECAY: 0.0001    # 0.0005 on DanceTrack

   I set the lr_points and weight_decay to 2.0e-5 and 0.0005 because of the stable convergence. This setting requires more training epochs, e.g. 18 (in our paper) to 20 (in our open-source code) on DanceTrack. However, as I discussed in #11, training on MOT17 is a delicate balance, so I haven't tried this new training configuration yet to see if it can achieve the desired results.
2. The image size during augmentation:

   COCO_SIZE: True    # False on DanceTrack

   This setting comes from MOTR. I don't quite understand the reason for this, but it does achieve better results.
3. Whether the bounding boxes can exceed the image size:

   OVERFLOW_BBOX: True    # False on DanceTrack

   Referring to some previous work, this approach can achieve better detection performance, but it will have a loss on the AssA metric. This is a difficult balance to choose.

BTW, it is also very common in MOT17 that newborn objects are suppressed by tracked targets, resulting in detection failure. Several recent research (CO-MOT/MOTRv3) focus on this. We hope we can resolve this issue in the future.

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