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

数据统计

数据评估

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