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Questions on DeT training datasets #11
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Thanks for your interest! |
Hi, I checked the code, in the code, I finally put all three datasets, COCO, LaSOT and DepthTrack train set together for training. And I also checked the BMVC paper, in which the depth DiMP was trained with COCO, LaSOT, and TrackingNet, but I did not use the depth DiMP in BMVC as the initialised weights for DeT, maybe you can tried it. For the ReseNet50, we still use the ImageNet weights. |
Hi, Thanks for the help! I am trying to reproduce the paper result. I use your provided ckpt (DeT_max) to test on DepthTrack. I obtain 61, 57, 59 as metrics. I use the default run_tracker, so i guess the mode is sequential. Therefore, the obtained results are significantly higher than the metrics reported on DeT paper (56 50 53) Do you have any idea about the performance? |
Hi, thanks for your interest! By the way, can you train the DeT with only COCO and LaSOT data? and then test again? |
Hi, I didn't retrain the paper yet. I only used your provided model, your ckpt, and your dataset to reproduce the result, which is strangely higher than your paper ^^ (just curious why this is happening) I am currently preparing the datasets since I am new to the domain. I have downloaded all your generated depth, but the original LaSOT RGB link seems to be broken... I am trying to figure it out. If I have sth, I will definitly let you know. |
Hi, if you download all generated data, it may be too large. It is better you find some better monocular depth estimation methods and generate depth maps at your local machine. Recently there are more sota methods, they may result better performances. |
Hi,
Thanks for the brilliant work on RGB-D tracking.
I am new to the tracking domain. I have several questions regarding the tracking datasets. Can you please help me to clarify several points?
Firstly, in the paper it mentions that the DeT is firstly pretrained on Pseudo LaSOT and Pseudo Coco, and then finetuned on DepthTrack. I would like to know if it makes any changes if we directly train on both three datasets?
Secondly, on the github it mentions that Using the default DiMP50 or ATOM pretrained checkpoints can reduce the training time. It seems that DiMP or ATOM are pretrained with larger RGB datasets (trackingnet, got10k, etc etc). I would like to know if these pretrained weights are adopted to initialize the model weight to produce the paper results? Or in the paper the network is only trained with Pseudo LaSOT, Pseudo Coco, and DepthTrack.
Finally, one question regarding the Table 2: Comparison of the original RGB trackers and their DeT variants. How are the RGB baseline trained? Only with RGB images from Pseudo LaSOT, Pseudo Coco, and DepthTrack? Are they only initialized with pretrained encoder (Imagenet)?
Sorry to bother you with all these questions... Looking forward to hearing from you.
Thanks again
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