Comments (8)
Hi,
Thank you for your interest!
DHN data is easy to be generated. I was taking gt bboxes and detections provided by MOT15,16 and MOT17, and I calculated the distances (same as dist during training,https://gitlab.inria.fr/yixu/deepmot/-/blob/master/train_tracktor/src/tracktor/tracker.py line 392) between them -> a distance matrix is made. I augumented by thresholding them with different values.
As for ground-truth labels, we input these distances into Hungarian algorithm, and get the ground-truth binary assignment for each distance.
You can surely make your custom data, and define your own distance.
Hope that can help.
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@yihongXU
Hi.
Thanks for your reply!
I am not sure what your words "augument by thresholding them with different values" mean.
Does it mean that zero out the value below the threshold and keep the value above the threshold?
e.g.
The distance matrix is [0.3, 0.5, 0.2], the stochastic threshold is [0.6, 0.4, 0.1], then the distance matrix is augmented to [0.0, 0.5, 0.2].
Is that right?
from deepmot.
Hi,
Suppose that your distance matrix values are [0,1], thresholding means that you threshold out those large distance that cannot be considered as a match. In your example, you threshold the distance matrix [0.3, 0.5, 0.2] by a value [0,1], let say 0.4, then it will become [0.3, inf, 0.2]. Inf referring to a big value ( like in https://github.com/cheind/py-motmetrics, you set it to np.nan) in order not to assign when you input it to Hungarian Algorithm.
from deepmot.
Thanks! I got it.
The output shape of https://gitlab.inria.fr/yixu/deepmot/-/blob/master/train_tracktor/src/tracktor/tracker.py is [N, M], where N is the number of the objects and M is the number of the detections on this frame.
But the shape DHN_data/*/*.npy
is all (10, 9, 15). What do the three dimensions “10, 9, 15” represent separately?
And the names of subdirectories are all in the format of “x_y”. What do x, y represent here?
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Hi,
10 is the batch size, I grouped the matrices having same size to a batch, just to accelerate a bit the training, otherwise you need to train it one by one since the input size varies.
x_y represents h,w of the matrix, as I said I regrouped the matrices by their sizes.
from deepmot.
Thanks! I got it.
But I am still a little confused.
There are train_DHN/DHN_data/train/1_6/2DMOT2015_1_6_7_m.npy
and rain_DHN/DHN_data/train/1_6/2DMOT2015_1_6_12_m.npy
.
What does that number (7 and 12 here) represent?
from deepmot.
Hi,
7 and 12 are just indexes for numbering the matrices. The reason why they are not continuous is that I picked randomly some of the matrices to the validation set.
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@magamig @yihongXU Hello, have you successfully made the data for DHN? I would appreciate if you could inform me about the process of making DHN!
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Related Issues (20)
- What are the meanings of the parameters of the missedMatchErrorV3 method?
- How to train DHN? HOT 1
- evaluation results lower than the results you shown in your paper HOT 1
- request HOT 1
- Why does the running result look so bad?
- verison request HOT 1
- Questions on the dataset for DHN and training process HOT 3
- AttributeError: 'FPN' object has no attribute 'reid_branch' HOT 1
- ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead. HOT 1
- Is it okay to use obsolete branch to reproduce your results? HOT 5
- why DHN? HOT 1
- Not able to run the Singularity image HOT 1
- Not able to launch singularity image HOT 4
- The loss curves shows that training of the model does not converge HOT 1
- The model does not converge based on the loss curve.
- Colab Notebook HOT 1
- Cloning into 'deepmot'... Permission denied (publickey). HOT 1
- why not to use DHN in the testing stage?
- About training on custom datasets
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