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sayakpaul/glpn-nyu-finetuned-diode-230131-041708
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sayakpaul/glpn-nyu-finetuned-diode-230131-041708

glpn-nyu-finetuned-diode-23...

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glpn-nyu-finetuned-diode-230131-041708

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: 0.4425
  • Mae: 0.4270
  • Rmse: 0.6196
  • Abs Rel: 0.4543
  • Log Mae: 0.1732
  • Log Rmse: 0.2288
  • Delta1: 0.3787
  • Delta2: 0.6298
  • Delta3: 0.8083


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
  • 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
0.5276 1.0 72 0.4701 0.4590 0.6348 0.4983 0.1903 0.2393 0.3169 0.5544 0.7661
0.4595 2.0 144 0.4867 0.4690 0.6369 0.5588 0.1956 0.2483 0.3090 0.5269 0.7532
0.4802 3.0 216 0.4854 0.4648 0.6344 0.5581 0.1935 0.2475 0.3135 0.5355 0.7531
0.4566 4.0 288 0.4709 0.4559 0.6756 0.4223 0.1890 0.2516 0.3668 0.6329 0.7696
0.4916 5.0 360 0.4835 0.4555 0.6343 0.5302 0.1881 0.2447 0.3435 0.5716 0.7437
0.4822 6.0 432 0.4756 0.4585 0.6301 0.5264 0.1894 0.2414 0.3238 0.5628 0.7435
0.4588 7.0 504 0.4655 0.4481 0.6509 0.4425 0.1843 0.2413 0.3498 0.6157 0.7809
0.4214 8.0 576 0.4869 0.4706 0.6391 0.5669 0.1961 0.2500 0.3033 0.5388 0.7371
0.426 9.0 648 0.4835 0.4679 0.6472 0.5117 0.1951 0.2486 0.3216 0.5474 0.7399
0.4135 10.0 720 0.4621 0.4439 0.6287 0.4803 0.1825 0.2365 0.3451 0.5887 0.7878
0.3778 11.0 792 0.4756 0.4566 0.6337 0.5174 0.1892 0.2431 0.3297 0.5560 0.7690
0.426 12.0 864 0.4542 0.4362 0.6219 0.4621 0.1779 0.2303 0.3572 0.6083 0.7835
0.4282 13.0 936 0.4514 0.4306 0.6195 0.4678 0.1754 0.2307 0.3661 0.6228 0.8083
0.4045 14.0 1008 0.4575 0.4390 0.6315 0.4530 0.1794 0.2343 0.3641 0.6128 0.7787
0.4351 15.0 1080 0.4669 0.4373 0.6423 0.4322 0.1796 0.2423 0.3917 0.6233 0.7850
0.4001 16.0 1152 0.4540 0.4356 0.6331 0.4336 0.1767 0.2320 0.3919 0.6132 0.7732
0.3741 17.0 1224 0.4890 0.4645 0.6361 0.5707 0.1926 0.2494 0.3253 0.5469 0.7386
0.4128 18.0 1296 0.4815 0.4593 0.6328 0.5511 0.1899 0.2457 0.3302 0.5571 0.7471
0.3809 19.0 1368 0.5002 0.4768 0.6425 0.6061 0.1991 0.2560 0.3105 0.5222 0.7118
0.4089 20.0 1440 0.4503 0.4311 0.6449 0.4081 0.1752 0.2370 0.4147 0.6445 0.7823
0.3612 21.0 1512 0.4541 0.4280 0.6215 0.4543 0.1735 0.2302 0.3823 0.6291 0.7968
0.3664 22.0 1584 0.4425 0.4251 0.6347 0.3970 0.1717 0.2300 0.4181 0.6374 0.7860
0.3787 23.0 1656 0.4722 0.4477 0.6378 0.4868 0.1846 0.2432 0.3541 0.6041 0.7733
0.4184 24.0 1728 0.4749 0.4506 0.6303 0.5329 0.1857 0.2434 0.3465 0.5752 0.7698
0.3928 25.0 1800 0.4646 0.4485 0.6395 0.4744 0.1847 0.2407 0.3528 0.5946 0.7816
0.3704 26.0 1872 0.4492 0.4340 0.6331 0.4344 0.1765 0.2326 0.3778 0.6314 0.7916
0.3462 27.0 1944 0.4467 0.4307 0.6314 0.4296 0.1751 0.2317 0.3840 0.6359 0.7983
0.3808 28.0 2016 0.4758 0.4622 0.6331 0.5236 0.1913 0.2425 0.3230 0.5439 0.7438
0.3641 29.0 2088 0.4609 0.4452 0.6315 0.4545 0.1824 0.2339 0.3484 0.5934 0.7716
0.3602 30.0 2160 0.4546 0.4413 0.6230 0.4729 0.1804 0.2318 0.3515 0.5944 0.7778
0.3638 31.0 2232 0.4498 0.4340 0.6245 0.4449 0.1764 0.2296 0.3725 0.6079 0.7923
0.3699 32.0 2304 0.4472 0.4305 0.6228 0.4568 0.1750 0.2307 0.3757 0.6239 0.8000
0.3805 33.0 2376 0.4647 0.4439 0.6325 0.4875 0.1823 0.2392 0.3609 0.5921 0.7833
0.3454 34.0 2448 0.4640 0.4442 0.6276 0.5008 0.1820 0.2376 0.3573 0.5865 0.7866
0.3452 35.0 2520 0.4646 0.4454 0.6276 0.4966 0.1827 0.2374 0.3489 0.5913 0.7726
0.3509 36.0 2592 0.4522 0.4394 0.6259 0.4605 0.1799 0.2321 0.3534 0.6001 0.7944
0.3432 37.0 2664 0.4656 0.4484 0.6290 0.5067 0.1841 0.2390 0.3487 0.5802 0.7687
0.381 38.0 2736 0.4630 0.4405 0.6287 0.4970 0.1807 0.2387 0.3565 0.6067 0.7907
0.3591 39.0 2808 0.4637 0.4452 0.6269 0.4995 0.1825 0.2374 0.3487 0.5966 0.7654
0.3826 40.0 2880 0.4723 0.4527 0.6307 0.5279 0.1867 0.2421 0.3338 0.5745 0.7713
0.3585 41.0 2952 0.4485 0.4306 0.6238 0.4470 0.1749 0.2297 0.3736 0.6251 0.7995
0.3518 42.0 3024 0.4369 0.4229 0.6293 0.4111 0.1701 0.2277 0.4004 0.6563 0.8009
0.359 43.0 3096 0.4545 0.4348 0.6274 0.4607 0.1777 0.2338 0.3592 0.6237 0.8000
0.3274 44.0 3168 0.4595 0.4359 0.6278 0.4781 0.1779 0.2357 0.3729 0.6093 0.7980
0.3368 45.0 3240 0.4617 0.4434 0.6253 0.5001 0.1819 0.2368 0.3400 0.5966 0.7953
0.3638 46.0 3312 0.4634 0.4380 0.6264 0.4925 0.1794 0.2371 0.3576 0.6158 0.7907
0.3698 47.0 3384 0.4559 0.4343 0.6223 0.4890 0.1776 0.2346 0.3579 0.6103 0.8110
0.3392 48.0 3456 0.4646 0.4477 0.6267 0.5029 0.1837 0.2374 0.3451 0.5798 0.7665
0.3548 49.0 3528 0.4598 0.4394 0.6245 0.4885 0.1793 0.2351 0.3647 0.6016 0.7815
0.3375 50.0 3600 0.4441 0.4271 0.6226 0.4487 0.1729 0.2293 0.3808 0.6354 0.8075
0.3315 51.0 3672 0.4613 0.4403 0.6292 0.4868 0.1805 0.2373 0.3630 0.6016 0.7905
0.3313 52.0 3744 0.4445 0.4307 0.6442 0.4108 0.1746 0.2342 0.3942 0.6577 0.7932
0.3372 53.0 3816 0.4456 0.4258 0.6269 0.4404 0.1720 0.2308 0.3924 0.6489 0.8027
0.3285 54.0 3888 0.4526 0.4348 0.6241 0.4723 0.1772 0.2328 0.3615 0.6160 0.8027
0.3474 55.0 3960 0.4498 0.4369 0.6258 0.4595 0.1782 0.2315 0.3617 0.6070 0.7978
0.3349 56.0 4032 0.4613 0.4428 0.6307 0.4858 0.1819 0.2376 0.3523 0.6012 0.7875
0.3207 57.0 4104 0.4476 0.4342 0.6230 0.4500 0.1765 0.2289 0.3658 0.6151 0.7910
0.3399 58.0 4176 0.4600 0.4413 0.6248 0.4940 0.1812 0.2360 0.3531 0.5954 0.7814
0.3327 59.0 4248 0.4463 0.4339 0.6215 0.4570 0.1770 0.2294 0.3590 0.6069 0.8063
0.3215 60.0 4320 0.4482 0.4317 0.6203 0.4595 0.1756 0.2295 0.3698 0.6154 0.8034
0.3276 61.0 4392 0.4406 0.4218 0.6192 0.4370 0.1705 0.2268 0.3878 0.6425 0.8111
0.3179 62.0 4464 0.4530 0.4331 0.6217 0.4765 0.1764 0.2327 0.3660 0.6121 0.8068
0.3129 63.0 4536 0.4614 0.4398 0.6263 0.5002 0.1803 0.2378 0.3529 0.6023 0.7974
0.3354 64.0 4608 0.4538 0.4374 0.6234 0.4777 0.1788 0.2333 0.3565 0.6013 0.7995
0.3261 65.0 4680 0.4367 0.4258 0.6283 0.4249 0.1724 0.2291 0.3861 0.6484 0.8026
0.3114 66.0 4752 0.4565 0.4366 0.6225 0.4852 0.1780 0.2334 0.3647 0.6073 0.7939
0.3377 67.0 4824 0.4519 0.4308 0.6185 0.4771 0.1755 0.2314 0.3681 0.6175 0.8079
0.3266 68.0 4896 0.4372 0.4216 0.6167 0.4345 0.1702 0.2245 0.3850 0.6392 0.8163
0.3347 69.0 4968 0.4343 0.4193 0.6179 0.4318 0.1690 0.2252 0.3890 0.6557 0.8114
0.3207 70.0 5040 0.4426 0.4269 0.6180 0.4465 0.1728 0.2266 0.3810 0.6296 0.8038
0.3313 71.0 5112 0.4362 0.4234 0.6177 0.4360 0.1712 0.2252 0.3777 0.6386 0.8133
0.326 72.0 5184 0.4392 0.4251 0.6182 0.4431 0.1723 0.2265 0.3783 0.6356 0.8088
0.3141 73.0 5256 0.4532 0.4385 0.6214 0.4818 0.1796 0.2327 0.3513 0.5981 0.8044
0.3301 74.0 5328 0.4536 0.4361 0.6230 0.4808 0.1783 0.2333 0.3585 0.6097 0.8037
0.3194 75.0 5400 0.4501 0.4335 0.6216 0.4698 0.1765 0.2312 0.3623 0.6164 0.8033
0.3071 76.0 5472 0.4455 0.4310 0.6201 0.4598 0.1751 0.2292 0.3625 0.6231 0.8087
0.3174 77.0 5544 0.4472 0.4316 0.6219 0.4625 0.1756 0.2307 0.3654 0.6256 0.8022
0.3171 78.0 5616 0.4461 0.4305 0.6204 0.4614 0.1750 0.2298 0.3663 0.6263 0.8052
0.3244 79.0 5688 0.4501 0.4328 0.6226 0.4725 0.1765 0.2324 0.3611 0.6233 0.8083
0.3188 80.0 5760 0.4427 0.4280 0.6199 0.4507 0.1735 0.2281 0.3757 0.6307 0.8054
0.3212 81.0 5832 0.4383 0.4222 0.6196 0.4365 0.1702 0.2266 0.3875 0.6476 0.8093
0.3234 82.0 5904 0.4434 0.4278 0.6216 0.4479 0.1735 0.2288 0.3728 0.6337 0.8064
0.3024 83.0 5976 0.4502 0.4331 0.6214 0.4728 0.1764 0.2317 0.3645 0.6192 0.8070
0.3145 84.0 6048 0.4409 0.4258 0.6199 0.4475 0.1726 0.2280 0.3778 0.6357 0.8075
0.329 85.0 6120 0.4491 0.4302 0.6221 0.4710 0.1749 0.2322 0.3755 0.6246 0.8065
0.3034 86.0 6192 0.4504 0.4321 0.6241 0.4699 0.1757 0.2325 0.3767 0.6217 0.8035
0.3074 87.0 6264 0.4373 0.4224 0.6188 0.4396 0.1706 0.2267 0.3878 0.6439 0.8107
0.3089 88.0 6336 0.4379 0.4235 0.6191 0.4402 0.1709 0.2266 0.3893 0.6410 0.8089
0.2995 89.0 6408 0.4448 0.4292 0.6193 0.4597 0.1744 0.2292 0.3740 0.6225 0.8065
0.3248 90.0 6480 0.4413 0.4279 0.6227 0.4494 0.1732 0.2283 0.3766 0.6305 0.8100
0.3203 91.0 6552 0.4445 0.4290 0.6213 0.4568 0.1740 0.2295 0.3754 0.6290 0.8085
0.3109 92.0 6624 0.4452 0.4295 0.6203 0.4597 0.1744 0.2297 0.3749 0.6245 0.8035
0.3241 93.0 6696 0.4419 0.4258 0.6190 0.4533 0.1725 0.2285 0.3833 0.6313 0.8092
0.3078 94.0 6768 0.4446 0.4278 0.6201 0.4597 0.1736 0.2297 0.3778 0.6284 0.8097
0.3141 95.0 6840 0.4466 0.4305 0.6208 0.4660 0.1749 0.2306 0.3720 0.6233 0.8058
0.3198 96.0 6912 0.4440 0.4275 0.6194 0.4584 0.1736 0.2293 0.3774 0.6279 0.8088
0.3 97.0 6984 0.4426 0.4269 0.6192 0.4545 0.1731 0.2287 0.3770 0.6302 0.8091
0.3096 98.0 7056 0.4433 0.4274 0.6197 0.4568 0.1735 0.2292 0.3765 0.6296 0.8088
0.3317 99.0 7128 0.4394 0.4244 0.6196 0.4448 0.1716 0.2276 0.3844 0.6374 0.8096
0.3132 100.0 7200 0.4425 0.4270 0.6196 0.4543 0.1732 0.2288 0.3787 0.6298 0.8083


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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