sayakpaul/glpn-nyu-finetuned-diode-221228-072509

2023-12-26 11:20 0 微浪网 0

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导语： glpn-nyu-finetuned-diode-22...,

Training Loss	Epoch	Step	Validation Loss	Mae	Rmse	Abs Rel	Log Mae	Log Rmse	Delta1	Delta2	Delta3
1.1571	1.0	72	0.6604	0.6233	0.8403	0.5125	0.3119	0.3691	0.1726	0.3423	0.4877
0.4895	2.0	144	0.4506	0.4460	0.6404	0.4241	0.1812	0.2299	0.3325	0.6053	0.7943
0.4709	3.0	216	0.4414	0.4370	0.6305	0.4243	0.1764	0.2253	0.3537	0.6145	0.7988
0.4436	4.0	288	0.4335	0.4324	0.6285	0.4045	0.1746	0.2245	0.3444	0.6506	0.8096
0.4656	5.0	360	0.4552	0.4515	0.6328	0.4614	0.1838	0.2307	0.3374	0.5762	0.7722
0.4482	6.0	432	0.4234	0.4166	0.6233	0.3805	0.1654	0.2179	0.4035	0.6623	0.8130
0.4099	7.0	504	0.4176	0.4185	0.6238	0.3676	0.1662	0.2150	0.3937	0.6589	0.8153
0.3987	8.0	576	0.4515	0.4431	0.6300	0.4497	0.1792	0.2283	0.3561	0.5906	0.7781
0.396	9.0	648	0.4235	0.4267	0.6347	0.3591	0.1716	0.2224	0.3934	0.6310	0.7963
0.3608	10.0	720	0.4312	0.4181	0.6227	0.4022	0.1666	0.2217	0.4014	0.6586	0.8173
0.3568	11.0	792	0.4322	0.4198	0.6183	0.4047	0.1674	0.2186	0.3870	0.6420	0.8071
0.3923	12.0	864	0.4225	0.4196	0.6294	0.3630	0.1668	0.2181	0.3910	0.6537	0.8151
0.3971	13.0	936	0.4086	0.4105	0.6219	0.3541	0.1614	0.2144	0.4234	0.6820	0.8144
0.372	14.0	1008	0.4127	0.4099	0.6172	0.3668	0.1612	0.2119	0.4046	0.6727	0.8260
0.3884	15.0	1080	0.4060	0.4074	0.6176	0.3528	0.1598	0.2119	0.4109	0.6925	0.8225
0.3616	16.0	1152	0.4078	0.4092	0.6198	0.3532	0.1615	0.2139	0.4162	0.6791	0.8186
0.3504	17.0	1224	0.4202	0.4320	0.6408	0.3613	0.1740	0.2261	0.3769	0.6301	0.7915
0.3823	18.0	1296	0.4328	0.4218	0.6182	0.4198	0.1684	0.2207	0.3916	0.6371	0.8113
0.3437	19.0	1368	0.4133	0.4138	0.6205	0.3638	0.1636	0.2162	0.3967	0.6761	0.8188
0.3739	20.0	1440	0.4040	0.4070	0.6187	0.3486	0.1594	0.2124	0.4214	0.6813	0.8214
0.3397	21.0	1512	0.4180	0.4300	0.6360	0.3601	0.1732	0.2239	0.3708	0.6362	0.8006
0.332	22.0	1584	0.4025	0.4050	0.6182	0.3505	0.1582	0.2114	0.4274	0.6909	0.8275
0.3552	23.0	1656	0.4120	0.4179	0.6305	0.3569	0.1650	0.2188	0.4002	0.6753	0.8102
0.3804	24.0	1728	0.4093	0.4111	0.6223	0.3594	0.1620	0.2152	0.4068	0.6851	0.8166
0.3519	25.0	1800	0.4039	0.4122	0.6237	0.3511	0.1621	0.2137	0.4109	0.6895	0.8171
0.3276	26.0	1872	0.4044	0.4117	0.6183	0.3533	0.1623	0.2127	0.3979	0.6824	0.8251
0.3167	27.0	1944	0.4091	0.4099	0.6189	0.3600	0.1613	0.2135	0.4069	0.6898	0.8218
0.3547	28.0	2016	0.4051	0.4055	0.6192	0.3521	0.1586	0.2119	0.4216	0.6921	0.8256
0.3297	29.0	2088	0.4025	0.4091	0.6215	0.3500	0.1605	0.2126	0.4155	0.6960	0.8224
0.3305	30.0	2160	0.4040	0.4045	0.6171	0.3507	0.1584	0.2120	0.4281	0.6938	0.8255
0.34	31.0	2232	0.4036	0.4082	0.6194	0.3492	0.1606	0.2132	0.4196	0.6851	0.8207
0.3507	32.0	2304	0.4057	0.4120	0.6245	0.3482	0.1619	0.2148	0.4195	0.6777	0.8172
0.3617	33.0	2376	0.4036	0.4098	0.6241	0.3477	0.1606	0.2141	0.4219	0.6871	0.8186
0.3268	34.0	2448	0.4015	0.4060	0.6197	0.3440	0.1593	0.2122	0.4326	0.6868	0.8211
0.3188	35.0	2520	0.4018	0.4032	0.6154	0.3504	0.1575	0.2107	0.4306	0.6952	0.8250
0.3286	36.0	2592	0.4046	0.4103	0.6237	0.3507	0.1611	0.2139	0.4179	0.6883	0.8173
0.3279	37.0	2664	0.3995	0.3993	0.6118	0.3460	0.1558	0.2091	0.4401	0.6979	0.8272
0.3439	38.0	2736	0.4052	0.4063	0.6196	0.3555	0.1590	0.2117	0.4207	0.6972	0.8256
0.3188	39.0	2808	0.4028	0.4028	0.6176	0.3482	0.1574	0.2112	0.4351	0.6916	0.8253
0.3334	40.0	2880	0.4059	0.4093	0.6218	0.3534	0.1607	0.2137	0.4201	0.6885	0.8217
0.3393	41.0	2952	0.4043	0.4048	0.6193	0.3492	0.1584	0.2118	0.4300	0.6906	0.8246
0.3099	42.0	3024	0.4029	0.4041	0.6161	0.3499	0.1583	0.2118	0.4274	0.6966	0.8239
0.3339	43.0	3096	0.4032	0.4056	0.6213	0.3515	0.1584	0.2122	0.4257	0.6995	0.8239
0.3086	44.0	3168	0.4024	0.4049	0.6173	0.3509	0.1586	0.2120	0.4243	0.6994	0.8227
0.3262	45.0	3240	0.4007	0.4035	0.6185	0.3467	0.1575	0.2112	0.4304	0.6994	0.8246
0.3265	46.0	3312	0.4017	0.4033	0.6170	0.3495	0.1574	0.2110	0.4271	0.7043	0.8247
0.3324	47.0	3384	0.4015	0.4056	0.6192	0.3471	0.1587	0.2119	0.4281	0.6944	0.8220
0.3159	48.0	3456	0.4012	0.4036	0.6156	0.3487	0.1581	0.2114	0.4279	0.6982	0.8234
0.3238	49.0	3528	0.4017	0.4024	0.6161	0.3499	0.1571	0.2106	0.4304	0.7008	0.8255
0.3112	50.0	3600	0.4012	0.4030	0.6173	0.3487	0.1574	0.2110	0.4308	0.6997	0.8249

glpn-nyu-finetuned-diode-221228-072509

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: 0.4012

Mae: 0.4030

Rmse: 0.6173

Abs Rel: 0.3487

Log Mae: 0.1574

Log Rmse: 0.2110

Delta1: 0.4308

Delta2: 0.6997

Delta3: 0.8249

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: 0.0001

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: 50

mixed_precision_training: Native AMP

Training results

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

1.1571 1.0 72 0.6604 0.6233 0.8403 0.5125 0.3119 0.3691 0.1726 0.3423 0.4877

0.4895 2.0 144 0.4506 0.4460 0.6404 0.4241 0.1812 0.2299 0.3325 0.6053 0.7943

0.4709 3.0 216 0.4414 0.4370 0.6305 0.4243 0.1764 0.2253 0.3537 0.6145 0.7988

0.4436 4.0 288 0.4335 0.4324 0.6285 0.4045 0.1746 0.2245 0.3444 0.6506 0.8096

0.4656 5.0 360 0.4552 0.4515 0.6328 0.4614 0.1838 0.2307 0.3374 0.5762 0.7722

0.4482 6.0 432 0.4234 0.4166 0.6233 0.3805 0.1654 0.2179 0.4035 0.6623 0.8130

0.4099 7.0 504 0.4176 0.4185 0.6238 0.3676 0.1662 0.2150 0.3937 0.6589 0.8153

0.3987 8.0 576 0.4515 0.4431 0.6300 0.4497 0.1792 0.2283 0.3561 0.5906 0.7781

0.396 9.0 648 0.4235 0.4267 0.6347 0.3591 0.1716 0.2224 0.3934 0.6310 0.7963

0.3608 10.0 720 0.4312 0.4181 0.6227 0.4022 0.1666 0.2217 0.4014 0.6586 0.8173

0.3568 11.0 792 0.4322 0.4198 0.6183 0.4047 0.1674 0.2186 0.3870 0.6420 0.8071

0.3923 12.0 864 0.4225 0.4196 0.6294 0.3630 0.1668 0.2181 0.3910 0.6537 0.8151

0.3971 13.0 936 0.4086 0.4105 0.6219 0.3541 0.1614 0.2144 0.4234 0.6820 0.8144

0.372 14.0 1008 0.4127 0.4099 0.6172 0.3668 0.1612 0.2119 0.4046 0.6727 0.8260

0.3884 15.0 1080 0.4060 0.4074 0.6176 0.3528 0.1598 0.2119 0.4109 0.6925 0.8225

0.3616 16.0 1152 0.4078 0.4092 0.6198 0.3532 0.1615 0.2139 0.4162 0.6791 0.8186

0.3504 17.0 1224 0.4202 0.4320 0.6408 0.3613 0.1740 0.2261 0.3769 0.6301 0.7915

0.3823 18.0 1296 0.4328 0.4218 0.6182 0.4198 0.1684 0.2207 0.3916 0.6371 0.8113

0.3437 19.0 1368 0.4133 0.4138 0.6205 0.3638 0.1636 0.2162 0.3967 0.6761 0.8188

0.3739 20.0 1440 0.4040 0.4070 0.6187 0.3486 0.1594 0.2124 0.4214 0.6813 0.8214

0.3397 21.0 1512 0.4180 0.4300 0.6360 0.3601 0.1732 0.2239 0.3708 0.6362 0.8006

0.332 22.0 1584 0.4025 0.4050 0.6182 0.3505 0.1582 0.2114 0.4274 0.6909 0.8275

0.3552 23.0 1656 0.4120 0.4179 0.6305 0.3569 0.1650 0.2188 0.4002 0.6753 0.8102

0.3804 24.0 1728 0.4093 0.4111 0.6223 0.3594 0.1620 0.2152 0.4068 0.6851 0.8166

0.3519 25.0 1800 0.4039 0.4122 0.6237 0.3511 0.1621 0.2137 0.4109 0.6895 0.8171

0.3276 26.0 1872 0.4044 0.4117 0.6183 0.3533 0.1623 0.2127 0.3979 0.6824 0.8251

0.3167 27.0 1944 0.4091 0.4099 0.6189 0.3600 0.1613 0.2135 0.4069 0.6898 0.8218

0.3547 28.0 2016 0.4051 0.4055 0.6192 0.3521 0.1586 0.2119 0.4216 0.6921 0.8256

0.3297 29.0 2088 0.4025 0.4091 0.6215 0.3500 0.1605 0.2126 0.4155 0.6960 0.8224

0.3305 30.0 2160 0.4040 0.4045 0.6171 0.3507 0.1584 0.2120 0.4281 0.6938 0.8255

0.34 31.0 2232 0.4036 0.4082 0.6194 0.3492 0.1606 0.2132 0.4196 0.6851 0.8207

0.3507 32.0 2304 0.4057 0.4120 0.6245 0.3482 0.1619 0.2148 0.4195 0.6777 0.8172

0.3617 33.0 2376 0.4036 0.4098 0.6241 0.3477 0.1606 0.2141 0.4219 0.6871 0.8186

0.3268 34.0 2448 0.4015 0.4060 0.6197 0.3440 0.1593 0.2122 0.4326 0.6868 0.8211

0.3188 35.0 2520 0.4018 0.4032 0.6154 0.3504 0.1575 0.2107 0.4306 0.6952 0.8250

0.3286 36.0 2592 0.4046 0.4103 0.6237 0.3507 0.1611 0.2139 0.4179 0.6883 0.8173

0.3279 37.0 2664 0.3995 0.3993 0.6118 0.3460 0.1558 0.2091 0.4401 0.6979 0.8272

0.3439 38.0 2736 0.4052 0.4063 0.6196 0.3555 0.1590 0.2117 0.4207 0.6972 0.8256

0.3188 39.0 2808 0.4028 0.4028 0.6176 0.3482 0.1574 0.2112 0.4351 0.6916 0.8253

0.3334 40.0 2880 0.4059 0.4093 0.6218 0.3534 0.1607 0.2137 0.4201 0.6885 0.8217

0.3393 41.0 2952 0.4043 0.4048 0.6193 0.3492 0.1584 0.2118 0.4300 0.6906 0.8246

0.3099 42.0 3024 0.4029 0.4041 0.6161 0.3499 0.1583 0.2118 0.4274 0.6966 0.8239

0.3339 43.0 3096 0.4032 0.4056 0.6213 0.3515 0.1584 0.2122 0.4257 0.6995 0.8239

0.3086 44.0 3168 0.4024 0.4049 0.6173 0.3509 0.1586 0.2120 0.4243 0.6994 0.8227

0.3262 45.0 3240 0.4007 0.4035 0.6185 0.3467 0.1575 0.2112 0.4304 0.6994 0.8246

0.3265 46.0 3312 0.4017 0.4033 0.6170 0.3495 0.1574 0.2110 0.4271 0.7043 0.8247

0.3324 47.0 3384 0.4015 0.4056 0.6192 0.3471 0.1587 0.2119 0.4281 0.6944 0.8220

0.3159 48.0 3456 0.4012 0.4036 0.6156 0.3487 0.1581 0.2114 0.4279 0.6982 0.8234

0.3238 49.0 3528 0.4017 0.4024 0.6161 0.3499 0.1571 0.2106 0.4304 0.7008 0.8255

0.3112 50.0 3600 0.4012 0.4030 0.6173 0.3487 0.1574 0.2110 0.4308 0.6997 0.8249

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sayakpaul/glpn-nyu-finetuned-diode-221228-072509

glpn-nyu-finetuned-diode-221228-072509

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

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