glpn-nyu-finetuned-diode-221122-044810

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: 0.3690
Mae: 0.2909
Rmse: 0.4208
Abs Rel: 0.3635
Log Mae: 0.1224
Log Rmse: 0.1793
Delta1: 0.5323
Delta2: 0.8179
Delta3: 0.9258

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: 2e-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: 0.2
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Mae	Rmse	Abs Rel	Log Mae	Log Rmse	Delta1	Delta2	Delta3
1.3864	1.0	72	1.2016	3.4656	3.5204	5.1101	0.6881	0.7346	0.0	0.0011	0.0764
1.0082	2.0	144	0.4607	0.4107	0.5420	0.5254	0.1697	0.2234	0.3596	0.6460	0.8465
0.4656	3.0	216	0.4071	0.3431	0.4758	0.4359	0.1425	0.1992	0.4567	0.7481	0.8958
0.4093	4.0	288	0.3953	0.3261	0.4622	0.4197	0.1363	0.1947	0.4841	0.7624	0.9103
0.392	5.0	360	0.3916	0.3211	0.4463	0.4116	0.1338	0.1896	0.4810	0.7756	0.9176
0.3466	6.0	432	0.3807	0.3075	0.4451	0.3658	0.1293	0.1839	0.5026	0.7921	0.9180
0.3297	7.0	504	0.3811	0.3047	0.4448	0.3534	0.1290	0.1835	0.5066	0.7920	0.9137
0.2768	8.0	576	0.3779	0.3057	0.4283	0.3894	0.1280	0.1832	0.5046	0.7996	0.9256
0.2849	9.0	648	0.3753	0.2978	0.4341	0.3496	0.1259	0.1806	0.5149	0.8041	0.9184
0.2571	10.0	720	0.3825	0.3068	0.4305	0.3896	0.1289	0.1849	0.4998	0.7974	0.9206
0.2246	11.0	792	0.3718	0.2951	0.4235	0.3678	0.1240	0.1803	0.5249	0.8105	0.9248
0.2703	12.0	864	0.3716	0.2945	0.4317	0.3593	0.1235	0.1808	0.5324	0.8122	0.9215
0.2596	13.0	936	0.3692	0.2921	0.4185	0.3690	0.1229	0.1798	0.5294	0.8167	0.9264
0.2604	14.0	1008	0.3684	0.2893	0.4171	0.3601	0.1223	0.1785	0.5325	0.8179	0.9252
0.2679	15.0	1080	0.3690	0.2909	0.4208	0.3635	0.1224	0.1793	0.5323	0.8179	0.9258

数据统计

数据评估

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