Comments (6)
@q36101 tusimple是有普遍接受的train/val/test分配的, 详见 https://github.com/voldemortX/pytorch-auto-drive/blob/master/docs/datasets/TUSIMPLE.md
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我遇到一些問題想請問一下,車道線論文上tusimple有都有94-96%的f1-score,而我自己用訓練scnn-tensorflow網路測試test(2782)只有81%,用val(358)測試有94.2。
我測試f1-score時是使用像素級的計算,用兩張png計算f1-score,而非tusimple的benchmark(因為我不知道如何將生成的特徵圖png轉成json)。
請問您是如何計算f1-score,是用json評估或是png評估?計算時是用test(2782),還是val(358)?
from pytorch-auto-drive.
我遇到一些問題想請問一下,車道線論文上tusimple有都有94-96%的f1-score,而我自己用訓練scnn-tensorflow網路測試test(2782)只有81%,用val(358)測試有94.2。
我測試f1-score時是使用像素級的計算,用兩張png計算f1-score,而非tusimple的benchmark(因為我不知道如何將生成的特徵圖png轉成json)。
請問您是如何計算f1-score,是用json評估或是png評估?計算時是用test(2782),還是val(358)?
我们的repo使用官方的json关键点评测脚本。val和test都支持。
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我知道了,謝謝。
from pytorch-auto-drive.
為甚麼val和test都支持,兩個dataset不是不一樣嗎?
from pytorch-auto-drive.
為甚麼val和test都支持,兩個dataset不是不一樣嗎?
。。。调参用val,评测用test。功能上不是都要支持么
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Related Issues (20)
- Trained LaneATT model HOT 8
- training losses become 0 after doing test among epochs HOT 11
- cannot identify image file 'culane/test/00004.png.lines.txt' HOT 3
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- Testing the LaneDetection on CULane dataset without training HOT 4
- 关于贝塞尔曲线curve fitting的问题 HOT 5
- training environment HOT 4
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- 匈牙利匹配时时用到curve_utils中的Cubic bezier curve segment HOT 6
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- pretrained weights HOT 4
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