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Provided checkpoint does NOT have weights for the Embedding head #138
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Hi, |
Hi, Thank you for the prompt response. That checkpoint appears to be for the bdd100k-trained model. I'm actually looking for the MOT17-trained checkpoint. Do you think you can send me the link to that(with trained weights for the embedding head)? |
Hi, Honestly I am still struggling to make all commands (train, test, inference, evaluate etc.) running properly. |
Hi,
I understand.
I've been using the older version of QDTrack from 2021. I've trained it
myself but I'm really interested in the new checkpoints because it's
pretrained on crowdhuman, which I've done myself as well but I can't seem
to reproduce results.
In any case, yea, working with this repo is proving difficult if you want
to do customized stuff, due to the extreme abstraction of MMCV.
Anyways, I'd appreciate any kind of help. No rush. I'm just interested in
the MOT17 checkpoint that you use to get the new results.
…On Thu, May 18, 2023 at 1:26 PM Raspberry-beans ***@***.***> wrote:
Hi,
I actually don't know about MOT17 weights. Also in the repo we have
weights only for BDD and Tao dataset. One way could be to train on MOT17
with given MOT17 config file.
Honestly I am still struggling to make all commands (train, test,
inference, evaluate etc.) running properly.
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Yeah mmcv extraction is a headache. I have to focus on BDD dataset. Till now only my inference is successfully using But now facing issues to reproduce the evaluation metrics result on BDD like mMOTA etc. |
I am facing the same issue now. The testing pipeline seems to work fine but the results do not match the ones in the paper. Did you manage to find a solution? @Raspberry-beans |
Hi there.
Thank you for sharing such a powerful Detector-Tracker.
I noticed that the checkpoint you provide does not have pretrained weights for the embeddings head:
I printed out the keys of the state dict of the checkpoint you provide(first list) and the newly initialized model(second list). Notice how the embedding head is absent on the second list:
First List (keys of state_dict from provided ckpt):
Second List (keys of state_dict from newly initialized model ):
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