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sayakpaul/glpn-nyu-finetuned-diode-230103-091356
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sayakpaul/glpn-nyu-finetuned-diode-230103-091356

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glpn-nyu-finetuned-diode-230103-091356

This model is a fine-tuned version of vinvino02/glpn-nyu on the diode-subset dataset.
It achieves the following results on the evaluation set:

  • Loss: 0.4360
  • Mae: 0.4251
  • Rmse: 0.6169
  • Abs Rel: 0.4500
  • Log Mae: 0.1721
  • Log Rmse: 0.2269
  • Delta1: 0.3828
  • Delta2: 0.6326
  • Delta3: 0.8051


Model description

More information needed


Intended uses & limitations

More information needed


Training and evaluation data

More information needed


Training procedure


Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0003
  • train_batch_size: 24
  • eval_batch_size: 48
  • seed: 2022
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.15
  • num_epochs: 100
  • mixed_precision_training: Native AMP


Training results

Training Loss Epoch Step Validation Loss Mae Rmse Abs Rel Log Mae Log Rmse Delta1 Delta2 Delta3
1.0762 1.0 72 0.5031 0.4779 0.6690 0.5503 0.2006 0.2591 0.3020 0.5337 0.8000
0.478 2.0 144 0.4653 0.4509 0.6307 0.4891 0.1861 0.2377 0.3300 0.5805 0.7734
0.4668 3.0 216 0.4845 0.4712 0.6373 0.5469 0.1963 0.2471 0.3110 0.5254 0.7235
0.4389 4.0 288 0.4587 0.4368 0.6219 0.4887 0.1787 0.2344 0.3578 0.6099 0.7926
0.4626 5.0 360 0.4879 0.4662 0.6351 0.5617 0.1937 0.2482 0.3135 0.5462 0.7395
0.4534 6.0 432 0.4638 0.4422 0.6236 0.4951 0.1810 0.2358 0.3606 0.5844 0.7831
0.4108 7.0 504 0.4688 0.4508 0.6279 0.5050 0.1856 0.2385 0.3426 0.5701 0.7623
0.3832 8.0 576 0.4759 0.4533 0.6284 0.5257 0.1869 0.2411 0.3331 0.5701 0.7617
0.4097 9.0 648 0.4771 0.4501 0.6303 0.5361 0.1855 0.2433 0.3454 0.5838 0.7609
0.3799 10.0 720 0.4575 0.4375 0.6240 0.4874 0.1790 0.2349 0.3669 0.6032 0.7916
0.3659 11.0 792 0.4718 0.4590 0.6298 0.5176 0.1893 0.2396 0.3283 0.5502 0.7368
0.4145 12.0 864 0.4776 0.4561 0.6298 0.5325 0.1883 0.2421 0.3333 0.5611 0.7540
0.4224 13.0 936 0.4320 0.4138 0.6202 0.4013 0.1655 0.2232 0.4217 0.6641 0.8004
0.4142 14.0 1008 0.4597 0.4440 0.6234 0.4842 0.1813 0.2330 0.3520 0.5895 0.7617
0.4393 15.0 1080 0.4333 0.4251 0.6197 0.4182 0.1712 0.2225 0.3787 0.6303 0.8100
0.4045 16.0 1152 0.4603 0.4356 0.6197 0.4819 0.1776 0.2322 0.3635 0.6050 0.7858
0.3708 17.0 1224 0.4738 0.4567 0.6292 0.5264 0.1886 0.2411 0.3283 0.5557 0.7596
0.4042 18.0 1296 0.5004 0.4802 0.6423 0.6101 0.2008 0.2560 0.3022 0.5165 0.6931
0.3763 19.0 1368 0.4501 0.4361 0.6213 0.4723 0.1772 0.2303 0.3634 0.6034 0.7889
0.4084 20.0 1440 0.4272 0.4133 0.6208 0.3958 0.1649 0.2226 0.4284 0.6684 0.8009
0.3637 21.0 1512 0.4307 0.4145 0.6199 0.4134 0.1665 0.2241 0.3957 0.6847 0.8137
0.3655 22.0 1584 0.4591 0.4374 0.6370 0.4594 0.1791 0.2384 0.3816 0.6264 0.7826
0.3844 23.0 1656 0.4692 0.4444 0.6273 0.5241 0.1824 0.2407 0.3540 0.5990 0.7756
0.428 24.0 1728 0.4982 0.4753 0.6403 0.6084 0.1984 0.2552 0.3099 0.5233 0.7204
0.4051 25.0 1800 0.4824 0.4618 0.6329 0.5533 0.1915 0.2461 0.3248 0.5495 0.7415
0.3584 26.0 1872 0.4434 0.4207 0.6177 0.4468 0.1694 0.2277 0.3975 0.6442 0.8038
0.3443 27.0 1944 0.4602 0.4434 0.6241 0.4912 0.1822 0.2351 0.3431 0.5877 0.7893
0.3714 28.0 2016 0.4818 0.4594 0.6316 0.5521 0.1900 0.2455 0.3283 0.5567 0.7493
0.3688 29.0 2088 0.4443 0.4215 0.6242 0.4386 0.1702 0.2294 0.4024 0.6522 0.8065
0.3615 30.0 2160 0.4462 0.4291 0.6189 0.4500 0.1739 0.2277 0.3792 0.6208 0.7896
0.3655 31.0 2232 0.4808 0.4574 0.6305 0.5524 0.1893 0.2452 0.3322 0.5590 0.7460
0.3576 32.0 2304 0.4321 0.4102 0.6182 0.4079 0.1640 0.2241 0.4296 0.6713 0.8074
0.3947 33.0 2376 0.4468 0.4298 0.6232 0.4574 0.1744 0.2306 0.3873 0.6163 0.7873
0.3402 34.0 2448 0.4565 0.4352 0.6195 0.4913 0.1776 0.2337 0.3734 0.6039 0.7865
0.3412 35.0 2520 0.4438 0.4261 0.6180 0.4546 0.1728 0.2279 0.3778 0.6252 0.8043
0.3547 36.0 2592 0.4577 0.4416 0.6218 0.4868 0.1807 0.2329 0.3517 0.5862 0.7862
0.3425 37.0 2664 0.4682 0.4511 0.6285 0.5210 0.1860 0.2406 0.3411 0.5748 0.7694
0.3853 38.0 2736 0.4752 0.4514 0.6289 0.5458 0.1863 0.2438 0.3408 0.5721 0.7760
0.3643 39.0 2808 0.4737 0.4547 0.6291 0.5401 0.1875 0.2428 0.3316 0.5673 0.7617
0.398 40.0 2880 0.4662 0.4467 0.6274 0.5124 0.1838 0.2394 0.3514 0.5823 0.7700
0.3579 41.0 2952 0.4781 0.4545 0.6290 0.5513 0.1880 0.2446 0.3343 0.5624 0.7718
0.3545 42.0 3024 0.4460 0.4277 0.6221 0.4553 0.1730 0.2294 0.3862 0.6285 0.7999
0.3527 43.0 3096 0.4330 0.4153 0.6169 0.4221 0.1668 0.2240 0.4106 0.6618 0.8084
0.3251 44.0 3168 0.4503 0.4286 0.6172 0.4781 0.1744 0.2313 0.3725 0.6224 0.8095
0.3433 45.0 3240 0.4471 0.4346 0.6187 0.4652 0.1772 0.2293 0.3606 0.6043 0.7952
0.3607 46.0 3312 0.4474 0.4263 0.6166 0.4658 0.1728 0.2293 0.3835 0.6287 0.8039
0.3722 47.0 3384 0.4527 0.4337 0.6205 0.4857 0.1768 0.2329 0.3696 0.6084 0.7922
0.3322 48.0 3456 0.4629 0.4431 0.6236 0.5118 0.1818 0.2373 0.3460 0.5897 0.7954
0.3624 49.0 3528 0.4431 0.4304 0.6203 0.4511 0.1742 0.2277 0.3827 0.6152 0.7917
0.3386 50.0 3600 0.4475 0.4260 0.6173 0.4697 0.1727 0.2301 0.3870 0.6283 0.8102
0.3316 51.0 3672 0.4558 0.4328 0.6194 0.4982 0.1770 0.2345 0.3618 0.6120 0.8124
0.3259 52.0 3744 0.4316 0.4084 0.6165 0.4234 0.1630 0.2245 0.4311 0.6809 0.8148
0.3299 53.0 3816 0.4489 0.4222 0.6198 0.4779 0.1706 0.2327 0.4049 0.6441 0.8021
0.3334 54.0 3888 0.4831 0.4598 0.6319 0.5716 0.1902 0.2476 0.3281 0.5597 0.7549
0.3342 55.0 3960 0.4478 0.4288 0.6166 0.4786 0.1745 0.2310 0.3749 0.6218 0.8091
0.3276 56.0 4032 0.4524 0.4342 0.6192 0.4852 0.1773 0.2326 0.3596 0.6113 0.8007
0.326 57.0 4104 0.4411 0.4226 0.6162 0.4486 0.1704 0.2268 0.3947 0.6403 0.7959
0.3429 58.0 4176 0.4578 0.4418 0.6221 0.4961 0.1812 0.2349 0.3497 0.5956 0.7750
0.3347 59.0 4248 0.4586 0.4409 0.6220 0.4946 0.1808 0.2347 0.3439 0.6004 0.7869
0.3215 60.0 4320 0.4583 0.4382 0.6232 0.4974 0.1789 0.2357 0.3667 0.6008 0.7855
0.331 61.0 4392 0.4412 0.4206 0.6145 0.4579 0.1699 0.2276 0.3966 0.6413 0.8047
0.3124 62.0 4464 0.4455 0.4236 0.6181 0.4727 0.1715 0.2313 0.3902 0.6417 0.8098
0.322 63.0 4536 0.4406 0.4230 0.6143 0.4548 0.1716 0.2269 0.3775 0.6425 0.8115
0.3194 64.0 4608 0.4473 0.4331 0.6193 0.4657 0.1765 0.2297 0.3606 0.6122 0.8014
0.3159 65.0 4680 0.4407 0.4225 0.6186 0.4548 0.1712 0.2293 0.3913 0.6433 0.8075
0.3118 66.0 4752 0.4478 0.4258 0.6169 0.4801 0.1728 0.2315 0.3762 0.6391 0.8064
0.336 67.0 4824 0.4659 0.4463 0.6252 0.5210 0.1834 0.2394 0.3464 0.5820 0.7786
0.3233 68.0 4896 0.4370 0.4208 0.6168 0.4452 0.1696 0.2265 0.4019 0.6425 0.8059
0.3285 69.0 4968 0.4479 0.4340 0.6189 0.4773 0.1771 0.2312 0.3609 0.6136 0.7972
0.3186 70.0 5040 0.4469 0.4308 0.6198 0.4698 0.1751 0.2310 0.3741 0.6219 0.7966
0.3351 71.0 5112 0.4476 0.4292 0.6176 0.4769 0.1745 0.2311 0.3718 0.6220 0.8035
0.3286 72.0 5184 0.4415 0.4229 0.6155 0.4655 0.1713 0.2289 0.3816 0.6376 0.8117
0.3135 73.0 5256 0.4527 0.4335 0.6198 0.4918 0.1769 0.2338 0.3621 0.6152 0.8036
0.3244 74.0 5328 0.4449 0.4290 0.6171 0.4685 0.1746 0.2296 0.3667 0.6234 0.8073
0.3253 75.0 5400 0.4450 0.4303 0.6182 0.4680 0.1750 0.2296 0.3703 0.6185 0.8013
0.3072 76.0 5472 0.4312 0.4212 0.6161 0.4337 0.1700 0.2242 0.3840 0.6411 0.8104
0.3159 77.0 5544 0.4434 0.4314 0.6186 0.4636 0.1754 0.2290 0.3643 0.6171 0.7996
0.3176 78.0 5616 0.4319 0.4207 0.6177 0.4330 0.1695 0.2249 0.3889 0.6524 0.8080
0.3243 79.0 5688 0.4432 0.4304 0.6186 0.4698 0.1752 0.2302 0.3667 0.6218 0.8058
0.3183 80.0 5760 0.4438 0.4288 0.6175 0.4665 0.1742 0.2294 0.3730 0.6235 0.8030
0.323 81.0 5832 0.4365 0.4248 0.6170 0.4480 0.1716 0.2263 0.3820 0.6313 0.8056
0.3348 82.0 5904 0.4385 0.4280 0.6179 0.4532 0.1738 0.2273 0.3651 0.6249 0.8099
0.2948 83.0 5976 0.4456 0.4330 0.6190 0.4727 0.1763 0.2305 0.3622 0.6121 0.7981
0.3156 84.0 6048 0.4349 0.4236 0.6155 0.4442 0.1712 0.2252 0.3834 0.6331 0.8086
0.3227 85.0 6120 0.4352 0.4251 0.6160 0.4423 0.1719 0.2250 0.3799 0.6293 0.8055
0.3044 86.0 6192 0.4349 0.4235 0.6165 0.4444 0.1714 0.2259 0.3858 0.6312 0.8108
0.3067 87.0 6264 0.4293 0.4214 0.6150 0.4293 0.1700 0.2229 0.3862 0.6397 0.8102
0.3083 88.0 6336 0.4260 0.4164 0.6139 0.4229 0.1673 0.2221 0.3989 0.6536 0.8126
0.2989 89.0 6408 0.4381 0.4270 0.6168 0.4526 0.1731 0.2270 0.3766 0.6248 0.8051
0.3232 90.0 6480 0.4352 0.4230 0.6158 0.4480 0.1711 0.2263 0.3854 0.6358 0.8112
0.3201 91.0 6552 0.4361 0.4242 0.6164 0.4462 0.1718 0.2262 0.3842 0.6327 0.8078
0.3096 92.0 6624 0.4390 0.4273 0.6171 0.4563 0.1733 0.2279 0.3790 0.6237 0.8046
0.322 93.0 6696 0.4338 0.4229 0.6157 0.4447 0.1709 0.2258 0.3889 0.6351 0.8069
0.3096 94.0 6768 0.4348 0.4238 0.6160 0.4448 0.1714 0.2256 0.3839 0.6342 0.8077
0.3067 95.0 6840 0.4414 0.4298 0.6181 0.4628 0.1748 0.2290 0.3707 0.6205 0.8027
0.3198 96.0 6912 0.4334 0.4228 0.6162 0.4434 0.1709 0.2258 0.3872 0.6370 0.8077
0.295 97.0 6984 0.4367 0.4261 0.6169 0.4507 0.1728 0.2269 0.3791 0.6283 0.8045
0.305 98.0 7056 0.4373 0.4266 0.6171 0.4524 0.1730 0.2273 0.3781 0.6280 0.8046
0.3304 99.0 7128 0.4334 0.4230 0.6162 0.4432 0.1709 0.2257 0.3874 0.6378 0.8062
0.3099 100.0 7200 0.4360 0.4251 0.6169 0.4500 0.1721 0.2269 0.3828 0.6326 0.8051


Framework versions

  • Transformers 4.24.0
  • Pytorch 1.12.1+cu116
  • Datasets 2.8.0
  • Tokenizers 0.13.2

数据统计

数据评估

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