HF计算机视觉
sayakpaul/glpn-nyu-finetuned-diode-230131-041708
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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