HF计算机视觉
sayakpaul/glpn-nyu-finetuned-diode-230103-091356
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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