Comments (2)
TonyXuQAQ
commented on July 28, 2024
Sorry for the late reply.
After obtaining the graph, we first rasterize the graph into a binary image, or you can say a skeleton (the road has one-pixel width).
After obtaining the skeletons of pred_graph and gt_graph, we can calculate the metric score based on pixels. This metric is used for validation in RNGDet++ due to its simplicity (code here). During the inference of RNGDet++, we only used metrics proposed by Sat2Graph for evaluation, and this metics is not adopted.
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yuzhaoluone
commented on July 28, 2024
Thank you very much for your answer, I didn't read this code carefully before, I have no more questions.
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Related Issues (20)
- Segmentation operations during training HOT 4
- about Training label calculation HOT 1
- About RNGDet result HOT 3
- 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
- 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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