glpn-nyu-finetuned-diode-221116-110652

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: 0.4018
Mae: 0.3272
Rmse: 0.4546
Abs Rel: 0.3934
Log Mae: 0.1380
Log Rmse: 0.1907
Delta1: 0.4598
Delta2: 0.7659
Delta3: 0.9082

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: 1e-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: 0.1
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Mae	Rmse	Abs Rel	Log Mae	Log Rmse	Delta1	Delta2	Delta3
1.3984	1.0	72	1.1606	3.2154	3.2710	4.6927	0.6627	0.7082	0.0	0.0053	0.0893
0.8305	2.0	144	0.5445	0.6035	0.8404	0.8013	0.2102	0.2726	0.2747	0.5358	0.7609
0.4601	3.0	216	0.4484	0.4041	0.5376	0.5417	0.1617	0.2188	0.3771	0.6932	0.8692
0.4211	4.0	288	0.4251	0.3634	0.4914	0.4800	0.1499	0.2069	0.4136	0.7270	0.8931
0.4162	5.0	360	0.4170	0.3537	0.4833	0.4483	0.1455	0.2005	0.4303	0.7444	0.8992
0.3776	6.0	432	0.4115	0.3491	0.4692	0.4558	0.1449	0.1999	0.4281	0.7471	0.9018
0.3729	7.0	504	0.4058	0.3337	0.4590	0.4135	0.1396	0.1935	0.4517	0.7652	0.9072
0.3235	8.0	576	0.4035	0.3304	0.4602	0.4043	0.1383	0.1929	0.4613	0.7679	0.9073
0.3382	9.0	648	0.3990	0.3254	0.4546	0.3937	0.1365	0.1900	0.4671	0.7717	0.9102
0.3265	10.0	720	0.4018	0.3272	0.4546	0.3934	0.1380	0.1907	0.4598	0.7659	0.9082

数据统计

数据评估

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