Comments (5)
wangziniu1109
commented on July 30, 2024
Dear authors,
thank you very much for your work. I would like to ask you a few questions.
First, when I evaluate your provided network, I get the following results:
OLD_test_iter pretrain 2d car --> easy: 0.9277, mod: 0.8439, hard: 0.6785 NEW_test_iter pretrain 2d car --> easy: 0.9342, mod: 0.8377, hard: 0.6742 OLD_test_iter pretrain gr car --> easy: 0.3349, mod: 0.2507, hard: 0.1983 NEW_test_iter pretrain gr car --> easy: 0.3225, mod: 0.2268, hard: 0.1722 OLD_test_iter pretrain 3d car --> easy: 0.2490, mod: 0.2077, hard: 0.1729 NEW_test_iter pretrain 3d car --> easy: 0.2317, mod: 0.1621, hard: 0.1234 OLD_test_iter pretrain 2d pedestrian --> easy: 0.6618, mod: 0.5812, hard: 0.4975 NEW_test_iter pretrain 2d pedestrian --> easy: 0.6896, mod: 0.5670, hard: 0.4756 OLD_test_iter pretrain gr pedestrian --> easy: 0.0628, mod: 0.0512, hard: 0.0483 NEW_test_iter pretrain gr pedestrian --> easy: 0.0471, mod: 0.0391, hard: 0.0321 OLD_test_iter pretrain 3d pedestrian --> easy: 0.0436, mod: 0.0445, hard: 0.0396 NEW_test_iter pretrain 3d pedestrian --> easy: 0.0371, mod: 0.0293, hard: 0.0270 OLD_test_iter pretrain 2d cyclist --> easy: 0.6234, mod: 0.4608, hard: 0.3972 NEW_test_iter pretrain 2d cyclist --> easy: 0.6301, mod: 0.4180, hard: 0.3816 OLD_test_iter pretrain gr cyclist --> easy: 0.0344, mod: 0.0296, hard: 0.0306 NEW_test_iter pretrain gr cyclist --> easy: 0.0295, mod: 0.0168, hard: 0.0168 OLD_test_iter pretrain 3d cyclist --> easy: 0.0293, mod: 0.0270, hard: 0.0262 NEW_test_iter pretrain 3d cyclist --> easy: 0.0263, mod: 0.0149, hard: 0.0148These are OK results for the car class but not for pedestrian and cyclist classes. Also, these results are not the same that you provide in your paper. I mean these results:
Also, when I run train.sh, I get similar results to the results that I get using the provided model, but these results are still not the same as in the paper. In fact, it is significantly better for the pedestrian class and better for the cyclist class.
OLD_test_iter 40000 2d car --> easy: 0.8290, mod: 0.7506, hard: 0.5892 NEW_test_iter 40000 2d car --> easy: 0.8759, mod: 0.7708, hard: 0.6137 OLD_test_iter 40000 gr car --> easy: 0.3448, mod: 0.2528, hard: 0.2053 NEW_test_iter 40000 gr car --> easy: 0.3066, mod: 0.2115, hard: 0.1653 OLD_test_iter 40000 3d car --> easy: 0.2671, mod: 0.1953, hard: 0.1754 NEW_test_iter 40000 3d car --> easy: 0.2230, mod: 0.1503, hard: 0.1193 OLD_test_iter 40000 2d pedestrian --> easy: 0.5670, mod: 0.4883, hard: 0.4096 NEW_test_iter 40000 2d pedestrian --> easy: 0.5822, mod: 0.4813, hard: 0.3946 OLD_test_iter 40000 gr pedestrian --> easy: 0.1323, mod: 0.1156, hard: 0.1137 NEW_test_iter 40000 gr pedestrian --> easy: 0.0528, mod: 0.0424, hard: 0.0351 OLD_test_iter 40000 3d pedestrian --> easy: 0.0473, mod: 0.0482, hard: 0.0413 NEW_test_iter 40000 3d pedestrian --> easy: 0.0405, mod: 0.0314, hard: 0.0287 OLD_test_iter 40000 2d cyclist --> easy: 0.4861, mod: 0.3255, hard: 0.3241 NEW_test_iter 40000 2d cyclist --> easy: 0.4460, mod: 0.2657, hard: 0.2633 OLD_test_iter 40000 gr cyclist --> easy: 0.1132, mod: 0.1058, hard: 0.1064 NEW_test_iter 40000 gr cyclist --> easy: 0.0375, mod: 0.0242, hard: 0.0238 OLD_test_iter 40000 3d cyclist --> easy: 0.1070, mod: 0.0909, hard: 0.0909 NEW_test_iter 40000 3d cyclist --> easy: 0.0213, mod: 0.0141, hard: 0.0144Can you please tell me how can I obtain the same results as in the paper?
Thank you!
the same question，thank you
from d4lcn.
wangziniu1109
commented on July 30, 2024
Dear authors,
thank you very much for your work. I would like to ask you a few questions.
First, when I evaluate your provided network, I get the following results:
OLD_test_iter pretrain 2d car --> easy: 0.9277, mod: 0.8439, hard: 0.6785 NEW_test_iter pretrain 2d car --> easy: 0.9342, mod: 0.8377, hard: 0.6742 OLD_test_iter pretrain gr car --> easy: 0.3349, mod: 0.2507, hard: 0.1983 NEW_test_iter pretrain gr car --> easy: 0.3225, mod: 0.2268, hard: 0.1722 OLD_test_iter pretrain 3d car --> easy: 0.2490, mod: 0.2077, hard: 0.1729 NEW_test_iter pretrain 3d car --> easy: 0.2317, mod: 0.1621, hard: 0.1234 OLD_test_iter pretrain 2d pedestrian --> easy: 0.6618, mod: 0.5812, hard: 0.4975 NEW_test_iter pretrain 2d pedestrian --> easy: 0.6896, mod: 0.5670, hard: 0.4756 OLD_test_iter pretrain gr pedestrian --> easy: 0.0628, mod: 0.0512, hard: 0.0483 NEW_test_iter pretrain gr pedestrian --> easy: 0.0471, mod: 0.0391, hard: 0.0321 OLD_test_iter pretrain 3d pedestrian --> easy: 0.0436, mod: 0.0445, hard: 0.0396 NEW_test_iter pretrain 3d pedestrian --> easy: 0.0371, mod: 0.0293, hard: 0.0270 OLD_test_iter pretrain 2d cyclist --> easy: 0.6234, mod: 0.4608, hard: 0.3972 NEW_test_iter pretrain 2d cyclist --> easy: 0.6301, mod: 0.4180, hard: 0.3816 OLD_test_iter pretrain gr cyclist --> easy: 0.0344, mod: 0.0296, hard: 0.0306 NEW_test_iter pretrain gr cyclist --> easy: 0.0295, mod: 0.0168, hard: 0.0168 OLD_test_iter pretrain 3d cyclist --> easy: 0.0293, mod: 0.0270, hard: 0.0262 NEW_test_iter pretrain 3d cyclist --> easy: 0.0263, mod: 0.0149, hard: 0.0148These are OK results for the car class but not for pedestrian and cyclist classes. Also, these results are not the same that you provide in your paper. I mean these results:
Also, when I run train.sh, I get similar results to the results that I get using the provided model, but these results are still not the same as in the paper. In fact, it is significantly better for the pedestrian class and better for the cyclist class.
OLD_test_iter 40000 2d car --> easy: 0.8290, mod: 0.7506, hard: 0.5892 NEW_test_iter 40000 2d car --> easy: 0.8759, mod: 0.7708, hard: 0.6137 OLD_test_iter 40000 gr car --> easy: 0.3448, mod: 0.2528, hard: 0.2053 NEW_test_iter 40000 gr car --> easy: 0.3066, mod: 0.2115, hard: 0.1653 OLD_test_iter 40000 3d car --> easy: 0.2671, mod: 0.1953, hard: 0.1754 NEW_test_iter 40000 3d car --> easy: 0.2230, mod: 0.1503, hard: 0.1193 OLD_test_iter 40000 2d pedestrian --> easy: 0.5670, mod: 0.4883, hard: 0.4096 NEW_test_iter 40000 2d pedestrian --> easy: 0.5822, mod: 0.4813, hard: 0.3946 OLD_test_iter 40000 gr pedestrian --> easy: 0.1323, mod: 0.1156, hard: 0.1137 NEW_test_iter 40000 gr pedestrian --> easy: 0.0528, mod: 0.0424, hard: 0.0351 OLD_test_iter 40000 3d pedestrian --> easy: 0.0473, mod: 0.0482, hard: 0.0413 NEW_test_iter 40000 3d pedestrian --> easy: 0.0405, mod: 0.0314, hard: 0.0287 OLD_test_iter 40000 2d cyclist --> easy: 0.4861, mod: 0.3255, hard: 0.3241 NEW_test_iter 40000 2d cyclist --> easy: 0.4460, mod: 0.2657, hard: 0.2633 OLD_test_iter 40000 gr cyclist --> easy: 0.1132, mod: 0.1058, hard: 0.1064 NEW_test_iter 40000 gr cyclist --> easy: 0.0375, mod: 0.0242, hard: 0.0238 OLD_test_iter 40000 3d cyclist --> easy: 0.1070, mod: 0.0909, hard: 0.0909 NEW_test_iter 40000 3d cyclist --> easy: 0.0213, mod: 0.0141, hard: 0.0144Can you please tell me how can I obtain the same results as in the paper?
Thank you!
OLD_test_iter 40000 2d car --> easy: 0.8559, mod: 0.7633, hard: 0.6673
NEW_test_iter 40000 2d car --> easy: 0.9023, mod: 0.7834, hard: 0.6453
OLD_test_iter 40000 gr car --> easy: 0.1754, mod: 0.1422, hard: 0.1190
NEW_test_iter 40000 gr car --> easy: 0.1770, mod: 0.1230, hard: 0.1005
OLD_test_iter 40000 3d car --> easy: 0.1317, mod: 0.0964, hard: 0.0698
NEW_test_iter 40000 3d car --> easy: 0.1184, mod: 0.0762, hard: 0.0596
OLD_test_iter 40000 2d pedestrian --> easy: 0.5614, mod: 0.4844, hard: 0.4063
NEW_test_iter 40000 2d pedestrian --> easy: 0.5609, mod: 0.4403, hard: 0.3724
OLD_test_iter 40000 gr pedestrian --> easy: 0.1065, mod: 0.1057, hard: 0.1051
NEW_test_iter 40000 gr pedestrian --> easy: 0.0278, mod: 0.0242, hard: 0.0194
OLD_test_iter 40000 3d pedestrian --> easy: 0.1027, mod: 0.1024, hard: 0.0909
NEW_test_iter 40000 3d pedestrian --> easy: 0.0193, mod: 0.0167, hard: 0.0125
OLD_test_iter 40000 2d cyclist --> easy: 0.0612, mod: 0.0686, hard: 0.0715
NEW_test_iter 40000 2d cyclist --> easy: 0.0505, mod: 0.0377, hard: 0.0393
OLD_test_iter 40000 gr cyclist --> easy: 0.0048, mod: 0.0048, hard: 0.0048
NEW_test_iter 40000 gr cyclist --> easy: 0.0013, mod: 0.0013, hard: 0.0013
OLD_test_iter 40000 3d cyclist --> easy: 0.0043, mod: 0.0043, hard: 0.0043
NEW_test_iter 40000 3d cyclist --> easy: 0.0008, mod: 0.0008, hard: 0.0008
sorry,i got the results like this when i run train.sh,could you please tell me how i can get the results like yours
from d4lcn.
vobecant
commented on July 30, 2024
Dear authors,
thank you very much for your work. I would like to ask you a few questions.
First, when I evaluate your provided network, I get the following results:OLD_test_iter pretrain 2d car --> easy: 0.9277, mod: 0.8439, hard: 0.6785 NEW_test_iter pretrain 2d car --> easy: 0.9342, mod: 0.8377, hard: 0.6742 OLD_test_iter pretrain gr car --> easy: 0.3349, mod: 0.2507, hard: 0.1983 NEW_test_iter pretrain gr car --> easy: 0.3225, mod: 0.2268, hard: 0.1722 OLD_test_iter pretrain 3d car --> easy: 0.2490, mod: 0.2077, hard: 0.1729 NEW_test_iter pretrain 3d car --> easy: 0.2317, mod: 0.1621, hard: 0.1234 OLD_test_iter pretrain 2d pedestrian --> easy: 0.6618, mod: 0.5812, hard: 0.4975 NEW_test_iter pretrain 2d pedestrian --> easy: 0.6896, mod: 0.5670, hard: 0.4756 OLD_test_iter pretrain gr pedestrian --> easy: 0.0628, mod: 0.0512, hard: 0.0483 NEW_test_iter pretrain gr pedestrian --> easy: 0.0471, mod: 0.0391, hard: 0.0321 OLD_test_iter pretrain 3d pedestrian --> easy: 0.0436, mod: 0.0445, hard: 0.0396 NEW_test_iter pretrain 3d pedestrian --> easy: 0.0371, mod: 0.0293, hard: 0.0270 OLD_test_iter pretrain 2d cyclist --> easy: 0.6234, mod: 0.4608, hard: 0.3972 NEW_test_iter pretrain 2d cyclist --> easy: 0.6301, mod: 0.4180, hard: 0.3816 OLD_test_iter pretrain gr cyclist --> easy: 0.0344, mod: 0.0296, hard: 0.0306 NEW_test_iter pretrain gr cyclist --> easy: 0.0295, mod: 0.0168, hard: 0.0168 OLD_test_iter pretrain 3d cyclist --> easy: 0.0293, mod: 0.0270, hard: 0.0262 NEW_test_iter pretrain 3d cyclist --> easy: 0.0263, mod: 0.0149, hard: 0.0148These are OK results for the car class but not for pedestrian and cyclist classes. Also, these results are not the same that you provide in your paper. I mean these results:
Also, when I run train.sh, I get similar results to the results that I get using the provided model, but these results are still not the same as in the paper. In fact, it is significantly better for the pedestrian class and better for the cyclist class.OLD_test_iter 40000 2d car --> easy: 0.8290, mod: 0.7506, hard: 0.5892 NEW_test_iter 40000 2d car --> easy: 0.8759, mod: 0.7708, hard: 0.6137 OLD_test_iter 40000 gr car --> easy: 0.3448, mod: 0.2528, hard: 0.2053 NEW_test_iter 40000 gr car --> easy: 0.3066, mod: 0.2115, hard: 0.1653 OLD_test_iter 40000 3d car --> easy: 0.2671, mod: 0.1953, hard: 0.1754 NEW_test_iter 40000 3d car --> easy: 0.2230, mod: 0.1503, hard: 0.1193 OLD_test_iter 40000 2d pedestrian --> easy: 0.5670, mod: 0.4883, hard: 0.4096 NEW_test_iter 40000 2d pedestrian --> easy: 0.5822, mod: 0.4813, hard: 0.3946 OLD_test_iter 40000 gr pedestrian --> easy: 0.1323, mod: 0.1156, hard: 0.1137 NEW_test_iter 40000 gr pedestrian --> easy: 0.0528, mod: 0.0424, hard: 0.0351 OLD_test_iter 40000 3d pedestrian --> easy: 0.0473, mod: 0.0482, hard: 0.0413 NEW_test_iter 40000 3d pedestrian --> easy: 0.0405, mod: 0.0314, hard: 0.0287 OLD_test_iter 40000 2d cyclist --> easy: 0.4861, mod: 0.3255, hard: 0.3241 NEW_test_iter 40000 2d cyclist --> easy: 0.4460, mod: 0.2657, hard: 0.2633 OLD_test_iter 40000 gr cyclist --> easy: 0.1132, mod: 0.1058, hard: 0.1064 NEW_test_iter 40000 gr cyclist --> easy: 0.0375, mod: 0.0242, hard: 0.0238 OLD_test_iter 40000 3d cyclist --> easy: 0.1070, mod: 0.0909, hard: 0.0909 NEW_test_iter 40000 3d cyclist --> easy: 0.0213, mod: 0.0141, hard: 0.0144Can you please tell me how can I obtain the same results as in the paper?
Thank you!
OLD_test_iter 40000 2d car --> easy: 0.8559, mod: 0.7633, hard: 0.6673
NEW_test_iter 40000 2d car --> easy: 0.9023, mod: 0.7834, hard: 0.6453
OLD_test_iter 40000 gr car --> easy: 0.1754, mod: 0.1422, hard: 0.1190
NEW_test_iter 40000 gr car --> easy: 0.1770, mod: 0.1230, hard: 0.1005
OLD_test_iter 40000 3d car --> easy: 0.1317, mod: 0.0964, hard: 0.0698
NEW_test_iter 40000 3d car --> easy: 0.1184, mod: 0.0762, hard: 0.0596
OLD_test_iter 40000 2d pedestrian --> easy: 0.5614, mod: 0.4844, hard: 0.4063
NEW_test_iter 40000 2d pedestrian --> easy: 0.5609, mod: 0.4403, hard: 0.3724
OLD_test_iter 40000 gr pedestrian --> easy: 0.1065, mod: 0.1057, hard: 0.1051
NEW_test_iter 40000 gr pedestrian --> easy: 0.0278, mod: 0.0242, hard: 0.0194
OLD_test_iter 40000 3d pedestrian --> easy: 0.1027, mod: 0.1024, hard: 0.0909
NEW_test_iter 40000 3d pedestrian --> easy: 0.0193, mod: 0.0167, hard: 0.0125
OLD_test_iter 40000 2d cyclist --> easy: 0.0612, mod: 0.0686, hard: 0.0715
NEW_test_iter 40000 2d cyclist --> easy: 0.0505, mod: 0.0377, hard: 0.0393
OLD_test_iter 40000 gr cyclist --> easy: 0.0048, mod: 0.0048, hard: 0.0048
NEW_test_iter 40000 gr cyclist --> easy: 0.0013, mod: 0.0013, hard: 0.0013
OLD_test_iter 40000 3d cyclist --> easy: 0.0043, mod: 0.0043, hard: 0.0043
NEW_test_iter 40000 3d cyclist --> easy: 0.0008, mod: 0.0008, hard: 0.0008
sorry,i got the results like this when i run train.sh,could you please tell me how i can get the results like yours
The difference in the results can be caused just by a different dynamics of the training and by a different initialization.
from d4lcn.
wangziniu1109
commented on July 30, 2024
Dear authors,
thank you very much for your work. I would like to ask you a few questions.
First, when I evaluate your provided network, I get the following results:
OLD_test_iter pretrain 2d car --> easy: 0.9277, mod: 0.8439, hard: 0.6785 NEW_test_iter pretrain 2d car --> easy: 0.9342, mod: 0.8377, hard: 0.6742 OLD_test_iter pretrain gr car --> easy: 0.3349, mod: 0.2507, hard: 0.1983 NEW_test_iter pretrain gr car --> easy: 0.3225, mod: 0.2268, hard: 0.1722 OLD_test_iter pretrain 3d car --> easy: 0.2490, mod: 0.2077, hard: 0.1729 NEW_test_iter pretrain 3d car --> easy: 0.2317, mod: 0.1621, hard: 0.1234 OLD_test_iter pretrain 2d pedestrian --> easy: 0.6618, mod: 0.5812, hard: 0.4975 NEW_test_iter pretrain 2d pedestrian --> easy: 0.6896, mod: 0.5670, hard: 0.4756 OLD_test_iter pretrain gr pedestrian --> easy: 0.0628, mod: 0.0512, hard: 0.0483 NEW_test_iter pretrain gr pedestrian --> easy: 0.0471, mod: 0.0391, hard: 0.0321 OLD_test_iter pretrain 3d pedestrian --> easy: 0.0436, mod: 0.0445, hard: 0.0396 NEW_test_iter pretrain 3d pedestrian --> easy: 0.0371, mod: 0.0293, hard: 0.0270 OLD_test_iter pretrain 2d cyclist --> easy: 0.6234, mod: 0.4608, hard: 0.3972 NEW_test_iter pretrain 2d cyclist --> easy: 0.6301, mod: 0.4180, hard: 0.3816 OLD_test_iter pretrain gr cyclist --> easy: 0.0344, mod: 0.0296, hard: 0.0306 NEW_test_iter pretrain gr cyclist --> easy: 0.0295, mod: 0.0168, hard: 0.0168 OLD_test_iter pretrain 3d cyclist --> easy: 0.0293, mod: 0.0270, hard: 0.0262 NEW_test_iter pretrain 3d cyclist --> easy: 0.0263, mod: 0.0149, hard: 0.0148These are OK results for the car class but not for pedestrian and cyclist classes. Also, these results are not the same that you provide in your paper. I mean these results:
Also, when I run train.sh, I get similar results to the results that I get using the provided model, but these results are still not the same as in the paper. In fact, it is significantly better for the pedestrian class and better for the cyclist class.
OLD_test_iter 40000 2d car --> easy: 0.8290, mod: 0.7506, hard: 0.5892 NEW_test_iter 40000 2d car --> easy: 0.8759, mod: 0.7708, hard: 0.6137 OLD_test_iter 40000 gr car --> easy: 0.3448, mod: 0.2528, hard: 0.2053 NEW_test_iter 40000 gr car --> easy: 0.3066, mod: 0.2115, hard: 0.1653 OLD_test_iter 40000 3d car --> easy: 0.2671, mod: 0.1953, hard: 0.1754 NEW_test_iter 40000 3d car --> easy: 0.2230, mod: 0.1503, hard: 0.1193 OLD_test_iter 40000 2d pedestrian --> easy: 0.5670, mod: 0.4883, hard: 0.4096 NEW_test_iter 40000 2d pedestrian --> easy: 0.5822, mod: 0.4813, hard: 0.3946 OLD_test_iter 40000 gr pedestrian --> easy: 0.1323, mod: 0.1156, hard: 0.1137 NEW_test_iter 40000 gr pedestrian --> easy: 0.0528, mod: 0.0424, hard: 0.0351 OLD_test_iter 40000 3d pedestrian --> easy: 0.0473, mod: 0.0482, hard: 0.0413 NEW_test_iter 40000 3d pedestrian --> easy: 0.0405, mod: 0.0314, hard: 0.0287 OLD_test_iter 40000 2d cyclist --> easy: 0.4861, mod: 0.3255, hard: 0.3241 NEW_test_iter 40000 2d cyclist --> easy: 0.4460, mod: 0.2657, hard: 0.2633 OLD_test_iter 40000 gr cyclist --> easy: 0.1132, mod: 0.1058, hard: 0.1064 NEW_test_iter 40000 gr cyclist --> easy: 0.0375, mod: 0.0242, hard: 0.0238 OLD_test_iter 40000 3d cyclist --> easy: 0.1070, mod: 0.0909, hard: 0.0909 NEW_test_iter 40000 3d cyclist --> easy: 0.0213, mod: 0.0141, hard: 0.0144Can you please tell me how can I obtain the same results as in the paper?
Thank you!
could you please show me your depth_guided_config.py settings when you run train.sh,when i run train.sh,i couldn't get the results like yours.thank you
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Chenzixi1
commented on July 30, 2024
hi, had you solve this problem. i am comfused too
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