Comments (1)
lucastabelini
commented on July 17, 2024
1
I see two reasons for that. One is that these kinds of images are very different from the ones in the training set, which means the output is nothing meaningful. The other is that all datasets used on the paper have at least one lane per image. If the model you used was trained on TuSimple, it will have a huge bias towards predicting 3+ lanes, since the vast majority of the images have this number of lanes.
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Related Issues (20)
- A question about the maximum performance HOT 2
- A problem about sharing the top-y (h) in lib/models.py line 103 HOT 3
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- A problem about the "curriculum_steps" in lib/models.py line 65 HOT 1
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- 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
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- IndexError
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