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Pretrained Models #2
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Thanks for your question, but unfortunately i do not keep a pretrained model |
Depth_map is used in the program..but i did not find it in your dataset |
@voqtuyen @OrkhanHI @610821216 @tuyenqv @OrkhanHI Hi, I have got the ACER as 40 % by using the OULU-NPU dataset with the CDCN Sorce code and the roc curve is very flat CAn you please reply , me it will be a great help Thank you in advance Oulu-NPU, P1: epoch:1, Val: val_threshold= 0.0003, val_ACC= 0.4341, val_ACER= 0.5731 epoch:21, Val: val_threshold= 0.0007, val_ACC= 0.5659, val_ACER= 0.4511 epoch:41, Val: val_threshold= 0.0008, val_ACC= 0.4806, val_ACER= 0.5265 epoch:61, Val: val_threshold= 0.0008, val_ACC= 0.5504, val_ACER= 0.4556 epoch:81, Val: val_threshold= 0.0009, val_ACC= 0.4884, val_ACER= 0.5182 epoch:101, Val: val_threshold= 0.0008, val_ACC= 0.5116, val_ACER= 0.5040 epoch:121, Val: val_threshold= 0.0008, val_ACC= 0.5039, val_ACER= 0.5036 epoch:141, Val: val_threshold= 0.0006, val_ACC= 0.4574, val_ACER= 0.5542 epoch:161, Val: val_threshold= 0.0009, val_ACC= 0.4496, val_ACER= 0.5664 epoch:181, Val: val_threshold= 0.0008, val_ACC= 0.3953, val_ACER= 0.5943 epoch:201, Val: val_threshold= 0.0007, val_ACC= 0.5504, val_ACER= 0.4477 epoch:221, Val: val_threshold= 0.0009, val_ACC= 0.5891, val_ACER= 0.4292 epoch:241, Val: val_threshold= 0.0007, val_ACC= 0.5969, val_ACER= 0.4047 epoch:261, Val: val_threshold= 0.0005, val_ACC= 0.5426, val_ACER= 0.4660 epoch:281, Val: val_threshold= 0.0007, val_ACC= 0.6047, val_ACER= 0.4019 epoch:301, Val: val_threshold= 0.0008, val_ACC= 0.5659, val_ACER= 0.4401 epoch:321, Val: val_threshold= 0.0009, val_ACC= 0.6589, val_ACER= 0.3960 epoch:341, Val: val_threshold= 0.0007, val_ACC= 0.5891, val_ACER= 0.4202 epoch:361, Val: val_threshold= 0.0010, val_ACC= 0.5116, val_ACER= 0.5026 epoch:381, Val: val_threshold= 0.0010, val_ACC= 0.6047, val_ACER= 0.4141 epoch:401, Val: val_threshold= 0.0005, val_ACC= 0.5194, val_ACER= 0.4879 epoch:421, Val: val_threshold= 0.0006, val_ACC= 0.5581, val_ACER= 0.4469 epoch:441, Val: val_threshold= 0.0006, val_ACC= 0.4884, val_ACER= 0.5074 epoch:461, Val: val_threshold= 0.0005, val_ACC= 0.5659, val_ACER= 0.4511 epoch:481, Val: val_threshold= 0.0004, val_ACC= 0.4729, val_ACER= 0.5331 epoch:501, Val: val_threshold= 0.0008, val_ACC= 0.6202, val_ACER= 0.3919 epoch:521, Val: val_threshold= 0.0006, val_ACC= 0.5194, val_ACER= 0.4804 epoch:541, Val: val_threshold= 0.0005, val_ACC= 0.4884, val_ACER= 0.5132 epoch:561, Val: val_threshold= 0.0005, val_ACC= 0.5271, val_ACER= 0.4886 epoch:581, Val: val_threshold= 0.0005, val_ACC= 0.5116, val_ACER= 0.5014 epoch:601, Val: val_threshold= 0.0007, val_ACC= 0.5039, val_ACER= 0.5086 epoch:621, Val: val_threshold= 0.0006, val_ACC= 0.5271, val_ACER= 0.4839 epoch:641, Val: val_threshold= 0.0005, val_ACC= 0.5039, val_ACER= 0.4975 epoch:661, Val: val_threshold= 0.0008, val_ACC= 0.6279, val_ACER= 0.3868 epoch:681, Val: val_threshold= 0.0005, val_ACC= 0.6434, val_ACER= 0.3637 epoch:701, Val: val_threshold= 0.0005, val_ACC= 0.5194, val_ACER= 0.4871 epoch:721, Val: val_threshold= 0.0006, val_ACC= 0.5194, val_ACER= 0.4876 epoch:741, Val: val_threshold= 0.0007, val_ACC= 0.4884, val_ACER= 0.5259 epoch:761, Val: val_threshold= 0.0007, val_ACC= 0.5194, val_ACER= 0.4681 epoch:781, Val: val_threshold= 0.0006, val_ACC= 0.6124, val_ACER= 0.3849 epoch:801, Val: val_threshold= 0.0003, val_ACC= 0.5969, val_ACER= 0.4325 epoch:821, Val: val_threshold= 0.0004, val_ACC= 0.5814, val_ACER= 0.4420 epoch:841, Val: val_threshold= 0.0004, val_ACC= 0.5426, val_ACER= 0.4662 epoch:861, Val: val_threshold= 0.0005, val_ACC= 0.4496, val_ACER= 0.5631 epoch:881, Val: val_threshold= 0.0006, val_ACC= 0.4961, val_ACER= 0.5168 epoch:901, Val: val_threshold= 0.0004, val_ACC= 0.4729, val_ACER= 0.5457 epoch:921, Val: val_threshold= 0.0007, val_ACC= 0.5581, val_ACER= 0.4561 epoch:941, Val: val_threshold= 0.0003, val_ACC= 0.4341, val_ACER= 0.5713 epoch:961, Val: val_threshold= 0.0005, val_ACC= 0.5194, val_ACER= 0.4883 epoch:981, Val: val_threshold= 0.0006, val_ACC= 0.5116, val_ACER= 0.5058 epoch:1001, Val: val_threshold= 0.0007, val_ACC= 0.5349, val_ACER= 0.4638 epoch:1021, Val: val_threshold= 0.0004, val_ACC= 0.5426, val_ACER= 0.4660 epoch:1041, Val: val_threshold= 0.0004, val_ACC= 0.4806, val_ACER= 0.5293 epoch:1061, Val: val_threshold= 0.0004, val_ACC= 0.5504, val_ACER= 0.4516 epoch:1081, Val: val_threshold= 0.0005, val_ACC= 0.4651, val_ACER= 0.5411 epoch:1101, Val: val_threshold= 0.0004, val_ACC= 0.5504, val_ACER= 0.4763 epoch:1121, Val: val_threshold= 0.0005, val_ACC= 0.5271, val_ACER= 0.4827 epoch:1141, Val: val_threshold= 0.0003, val_ACC= 0.5659, val_ACER= 0.4435 epoch:1161, Val: val_threshold= 0.0003, val_ACC= 0.5969, val_ACER= 0.4171 epoch:1181, Val: val_threshold= 0.0003, val_ACC= 0.5659, val_ACER= 0.4511 epoch:1201, Val: val_threshold= 0.0004, val_ACC= 0.5039, val_ACER= 0.4924 epoch:1221, Val: val_threshold= 0.0005, val_ACC= 0.5194, val_ACER= 0.4797 epoch:1241, Val: val_threshold= 0.0004, val_ACC= 0.5194, val_ACER= 0.4928 epoch:1261, Val: val_threshold= 0.0005, val_ACC= 0.5659, val_ACER= 0.4435 epoch:1281, Val: val_threshold= 0.0004, val_ACC= 0.5271, val_ACER= 0.4756 As the absolute loss and contrastive depth loss are good, but the ACER is not performing well, Can you please clear my questions it will be a great help Hope for a reply |
Hi, do you have pretrained models?
Thanks,
Orkhan
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