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View Code? Open in Web Editor NEWOfficial code for NeurIPS paper "Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach".
Official code for NeurIPS paper "Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach".
I don't have available any gpu with enough memory to execute your training scripts with the config you provided.
Because of that I have executed the pretrain on cityscapes with a batchsize of 2 (instead of 12), ending with a network with the following validation metrics (note that not having the small small validation set, I simply used the whole cityscapes validation set):
PQ | SQ | RQ | PQ_th | SQ_th | RQ_th | PQ_st | SQ_st | RQ_st |
---|---|---|---|---|---|---|---|---|
54.8540 | 80.6902 | 66.7585 | 42.9238 | 79.5514 | 53.7625 | 63.5305 | 81.5184 | 76.2101 |
(the training total_loss was 1.501 at the final iteration)
I then used the last checkpoint of this pretrain for the fully differentiable training, with a bachsize of 8 (instead of 24), but during the training the total_loss went up to around 9.3, while the pq and sq went down to 0 (both the training and the evaluation ones at the end). I also tried lowering the learning rate, but the results were always the same.
Is this a known issue? Are there any solutions?
PS: in the paper you said you used {1, 4, 16, 32, 64, 128} as edge distances with cityscapes, but in your training configuration the maximum distance is 64. Is it intentional?
Hi, is there any script for inference that can be used to see the results on new images?
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