sayakpaul/glpn-nyu-finetuned-diode-221214-081122

Training Loss	Epoch	Step	Validation Loss	Mae	Rmse	Abs Rel	Log Mae	Log Rmse	Delta1	Delta2	Delta3
0.6896	1.0	72	0.4753	0.4670	0.6226	0.5658	0.1791	0.2313	0.2950	0.6310	0.8562
0.3628	2.0	144	0.3565	0.2956	0.4307	0.3421	0.1226	0.1737	0.5285	0.8079	0.9245
0.3168	3.0	216	0.3486	0.2774	0.3963	0.3464	0.1172	0.1710	0.5561	0.8285	0.9358
0.2734	4.0	288	0.3368	0.2669	0.3962	0.3260	0.1122	0.1671	0.5787	0.8453	0.9383
0.2678	5.0	360	0.3419	0.2700	0.4136	0.3118	0.1130	0.1689	0.5869	0.8399	0.9289
0.2271	6.0	432	0.3270	0.2611	0.3890	0.3050	0.1091	0.1608	0.5899	0.8500	0.9453
0.1905	7.0	504	0.3331	0.2651	0.3996	0.3086	0.1110	0.1645	0.5925	0.8391	0.9405
0.1436	8.0	576	0.3323	0.2648	0.4039	0.3087	0.1097	0.1653	0.6019	0.8475	0.9345
0.1687	9.0	648	0.3274	0.2620	0.3887	0.3129	0.1092	0.1622	0.5954	0.8464	0.9422
0.1407	10.0	720	0.3344	0.2689	0.4134	0.3079	0.1107	0.1667	0.6039	0.8423	0.9287
0.1159	11.0	792	0.3302	0.2675	0.4081	0.3032	0.1103	0.1646	0.6035	0.8406	0.9334
0.16	12.0	864	0.3262	0.2599	0.3989	0.2986	0.1074	0.1621	0.6177	0.8460	0.9371
0.1385	13.0	936	0.3287	0.2616	0.3976	0.3114	0.1085	0.1643	0.6095	0.8472	0.9363
0.156	14.0	1008	0.3291	0.2690	0.4147	0.3048	0.1101	0.1654	0.6082	0.8390	0.9305
0.1534	15.0	1080	0.3267	0.2651	0.3994	0.3084	0.1096	0.1632	0.6030	0.8406	0.9376
0.1196	16.0	1152	0.3248	0.2588	0.4028	0.2908	0.1065	0.1615	0.6265	0.8467	0.9357
0.0983	17.0	1224	0.3249	0.2612	0.4046	0.2940	0.1075	0.1620	0.6198	0.8440	0.9346
0.1347	18.0	1296	0.3209	0.2608	0.4012	0.2971	0.1069	0.1614	0.6201	0.8473	0.9376
0.107	19.0	1368	0.3249	0.2624	0.4026	0.3013	0.1079	0.1628	0.6149	0.8453	0.9362
0.1214	20.0	1440	0.3213	0.2586	0.3976	0.2962	0.1065	0.1609	0.6219	0.8464	0.9382
0.0921	21.0	1512	0.3240	0.2600	0.3971	0.3028	0.1074	0.1624	0.6179	0.8457	0.9383
0.0906	22.0	1584	0.3239	0.2602	0.4025	0.2968	0.1069	0.1622	0.6227	0.8461	0.9365
0.0978	23.0	1656	0.3230	0.2588	0.3990	0.2969	0.1066	0.1617	0.6234	0.8462	0.9371
0.1377	24.0	1728	0.3244	0.2612	0.4013	0.3005	0.1076	0.1626	0.6180	0.8447	0.9359
0.1253	25.0	1800	0.3242	0.2603	0.3997	0.3010	0.1073	0.1624	0.6187	0.8455	0.9378

glpn-nyu-finetuned-diode-221214-081122

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: 0.3242

Mae: 0.2603

Rmse: 0.3997

Abs Rel: 0.3010

Log Mae: 0.1073

Log Rmse: 0.1624

Delta1: 0.6187

Delta2: 0.8455

Delta3: 0.9378

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: 5e-05

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: 25

mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Mae Rmse Abs Rel Log Mae Log Rmse Delta1 Delta2 Delta3

0.6896 1.0 72 0.4753 0.4670 0.6226 0.5658 0.1791 0.2313 0.2950 0.6310 0.8562

0.3628 2.0 144 0.3565 0.2956 0.4307 0.3421 0.1226 0.1737 0.5285 0.8079 0.9245

0.3168 3.0 216 0.3486 0.2774 0.3963 0.3464 0.1172 0.1710 0.5561 0.8285 0.9358

0.2734 4.0 288 0.3368 0.2669 0.3962 0.3260 0.1122 0.1671 0.5787 0.8453 0.9383

0.2678 5.0 360 0.3419 0.2700 0.4136 0.3118 0.1130 0.1689 0.5869 0.8399 0.9289

0.2271 6.0 432 0.3270 0.2611 0.3890 0.3050 0.1091 0.1608 0.5899 0.8500 0.9453

0.1905 7.0 504 0.3331 0.2651 0.3996 0.3086 0.1110 0.1645 0.5925 0.8391 0.9405

0.1436 8.0 576 0.3323 0.2648 0.4039 0.3087 0.1097 0.1653 0.6019 0.8475 0.9345

0.1687 9.0 648 0.3274 0.2620 0.3887 0.3129 0.1092 0.1622 0.5954 0.8464 0.9422

0.1407 10.0 720 0.3344 0.2689 0.4134 0.3079 0.1107 0.1667 0.6039 0.8423 0.9287

0.1159 11.0 792 0.3302 0.2675 0.4081 0.3032 0.1103 0.1646 0.6035 0.8406 0.9334

0.16 12.0 864 0.3262 0.2599 0.3989 0.2986 0.1074 0.1621 0.6177 0.8460 0.9371

0.1385 13.0 936 0.3287 0.2616 0.3976 0.3114 0.1085 0.1643 0.6095 0.8472 0.9363

0.156 14.0 1008 0.3291 0.2690 0.4147 0.3048 0.1101 0.1654 0.6082 0.8390 0.9305

0.1534 15.0 1080 0.3267 0.2651 0.3994 0.3084 0.1096 0.1632 0.6030 0.8406 0.9376

0.1196 16.0 1152 0.3248 0.2588 0.4028 0.2908 0.1065 0.1615 0.6265 0.8467 0.9357

0.0983 17.0 1224 0.3249 0.2612 0.4046 0.2940 0.1075 0.1620 0.6198 0.8440 0.9346

0.1347 18.0 1296 0.3209 0.2608 0.4012 0.2971 0.1069 0.1614 0.6201 0.8473 0.9376

0.107 19.0 1368 0.3249 0.2624 0.4026 0.3013 0.1079 0.1628 0.6149 0.8453 0.9362

0.1214 20.0 1440 0.3213 0.2586 0.3976 0.2962 0.1065 0.1609 0.6219 0.8464 0.9382

0.0921 21.0 1512 0.3240 0.2600 0.3971 0.3028 0.1074 0.1624 0.6179 0.8457 0.9383

0.0906 22.0 1584 0.3239 0.2602 0.4025 0.2968 0.1069 0.1622 0.6227 0.8461 0.9365

0.0978 23.0 1656 0.3230 0.2588 0.3990 0.2969 0.1066 0.1617 0.6234 0.8462 0.9371

0.1377 24.0 1728 0.3244 0.2612 0.4013 0.3005 0.1076 0.1626 0.6180 0.8447 0.9359

0.1253 25.0 1800 0.3242 0.2603 0.3997 0.3010 0.1073 0.1624 0.6187 0.8455 0.9378

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sayakpaul/glpn-nyu-finetuned-diode-221214-081122

glpn-nyu-finetuned-diode-221214-081122

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

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