Comments (3)
TonyXuQAQ
commented on July 28, 2024
1
Hi, sorry that we uploaded the incorrect checkpoints. We have re-upload correct ones and fixed several bugs in the code. Please pull the latest commit and re-download checkpoints for inference. Sorry for the caused inconvenience.
For RNGDet:
APLS: 0.6571125925925926
F1: 0.7686959876145674
Precision: 0.8596718580268147
Recall: 0.6977868628276296
BTW, due to the randomness of RNGDet/RNGDet++ and the evaluation metrics, the actual scores might be slightly different from the number reported in the paper.
Should you have further questions, open an issue for discussion. Thanks.
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TonyXuQAQ
commented on July 28, 2024
Sorry that we uploaded the incorrect checkpoint and there are still some bugs in the code. We will try to fix them ASAP. Thanks for the reported issue.
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Dawn-bin
commented on July 28, 2024
Thanks for ur help! I have repeated the results.
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Related Issues (20)
- Segmentation operations during training HOT 4
- about Training label calculation HOT 1
- How to Batch Training？ HOT 2
- Some issues related to history_map HOT 2
- L1 Loss HOT 2
- Training Label Calculation HOT 4
- CNN backones HOT 1
- Tensorboard Log HOT 3
- aux_loss HOT 1
- About the preparation of the training dataset. HOT 5
- This is a problem about rtree.go HOT 4
- The number of "unexplored_edges" is larger than "num_queries" HOT 2
- About Evaluation Metrics HOT 2
- I am not sure why not try RoadTracer Dataset in RNGDet++ like the experiment conducted in RNGDet HOT 1
- Inference problem on the new trained weights HOT 4
- how long it took for your model, which was trained on 4 RTX4090，and how many epochs it took to achieve the current level of performance. HOT 1
- Intersection Detection HOT 1
- train on custom dataset HOT 2
- dataset
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