glpn-nyu-finetuned-diode-221116-104421

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: 0.3736
Mae: 0.3079
Rmse: 0.4321
Abs Rel: 0.3666
Log Mae: 0.1288
Log Rmse: 0.1794
Delta1: 0.4929
Delta2: 0.7934
Delta3: 0.9234

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
1.3644	1.0	72	1.1149	2.9299	2.9878	4.3237	0.6329	0.6803	0.0001	0.0241	0.1147
0.7701	2.0	144	0.5115	0.4977	0.6435	0.6806	0.1967	0.2543	0.3113	0.5462	0.7732
0.4351	3.0	216	0.4129	0.3591	0.4868	0.4587	0.1495	0.2034	0.4214	0.7194	0.8869
0.4001	4.0	288	0.4003	0.3421	0.4711	0.4248	0.1418	0.1953	0.4509	0.7446	0.8999
0.3923	5.0	360	0.3928	0.3334	0.4573	0.4139	0.1388	0.1906	0.4562	0.7570	0.9098
0.363	6.0	432	0.3806	0.3176	0.4419	0.3873	0.1328	0.1840	0.4757	0.7786	0.9188
0.3516	7.0	504	0.3760	0.3091	0.4346	0.3697	0.1291	0.1804	0.4933	0.7927	0.9224
0.303	8.0	576	0.3798	0.3131	0.4401	0.3811	0.1307	0.1833	0.4913	0.7886	0.9189
0.3191	9.0	648	0.3766	0.3104	0.4356	0.3738	0.1298	0.1811	0.4907	0.7901	0.9214
0.3102	10.0	720	0.3736	0.3079	0.4321	0.3666	0.1288	0.1794	0.4929	0.7934	0.9234

数据统计

数据评估

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