Comments (5)
Hi,
Yes we plan to release sqr-deformable detr in a couple of weeks as the team is on leave
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[update] Released. Please refer to the readme.md
Recommend following the original deformable detr setting (32 as batchsize 8*A100 x 4 imgs per GPU ), but it seems 16 as batchsize performs similarly. Expecting 45.8±0.2 AP. Thanks
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Thank you very much for your reply. I ran your code as soon as you replied. Due to insufficient memory on my device, I am unable to use the same configuration as you. But I got a bad result. After carefully reviewing the code, I found that you did not update the reference points during the decoder process. May I ask why you are handling this in this place?
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Hi,
I am not sure what hyperparameter you changed in config, but some of them are critical, such as query number, feature dimension, encoder layer number, decoder layer number, etc.
For sqr-deformable-detr, we do not collect the reference points, because if the reg_branch is None (one stage), the reference points are not updated, so for SQR, it is also unnecessary to do that.
If you do not have enough memory to run your research, I recommend using sqr-adamixer which runs well with 8x24GB card.
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Thank you a lot for your advice. Wish you have a nice day !!
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Related Issues (11)
- Inference latency HOT 1
- code of computing TP F Rate and FP E Rate HOT 6
- mmcv had no 1.x
- A little question about the thesis HOT 2
- Selective QR HOT 1
- ms_deform_attn_forward gpu support
- More GPU memory cost when adding SQR to DN-DETR HOT 7
- Question concerning the query recollection process: HOT 1
- how to test the SQR decoder individually ?
- fixed an issue in SQR-Deformable DETR 's inference pipeline
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