Comments (3)
No, the code will sort the lanes for you (here).
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So the targets were sorted in this order, will the predicts of net obey this order?
When calculate loss you simply use MCE or BCE without any matching for targets and predicts, unlike the common object detection method, they usually need to find the connection between anchor and ground truth.
If 4 lanes are labeled in a certain image and the predicts have 5 lanes, it will just use the first 4 lanes of prediction to calculate loss(from here). why does it work?
Another question, If I using tusimple dataset for training, can I just assume the predicts (L1, L2, L3, L4) are exactly (left-left, ego-left, ego-right, right-right)?
from polylanenet.
So the targets were sorted in this order, will the predicts of net obey this order?
Yes, probably.
When calculate loss you simply use MCE or BCE without any matching for targets and predicts, unlike the common object detection method, they usually need to find the connection between anchor and ground truth.
If 4 lanes are labeled in a certain image and the predicts have 5 lanes, it will just use the first 4 lanes of prediction to calculate loss(from here). why does it work?
The model learns to predict all lanes in a sorted manner.
Another question, If I using tusimple dataset for training, can I just assume the predicts (L1, L2, L3, L4) are exactly (left-left, ego-left, ego-right, right-right)?
Not sure. You would have to look at the dataset's original description.
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Related Issues (20)
- A problem about sharing the top-y (h) in lib/models.py line 103 HOT 3
- The problem of predicting performance HOT 1
- Question about sharing the horizon HOT 4
- The question about LPD HOT 2
- A problem about the "curriculum_steps" in lib/models.py line 65 HOT 1
- A consultation on the GPU number HOT 3
- problem about speed HOT 2
- Check param count HOT 2
- How to train my own dataset? HOT 3
- What if we do not predict s and h HOT 1
- h_samples HOT 5
- poly_weight HOT 2
- Have you ever done experiments on CULane datasets? HOT 1
- Confidence Scores HOT 3
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- IndexError
- About testing on other Dataset HOT 1
- change you requirements version
- error encountered while testing
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