glpn-nyu-finetuned-diode-221122-014502

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: 0.3476
Mae: 0.2763
Rmse: 0.4088
Abs Rel: 0.3308
Log Mae: 0.1161
Log Rmse: 0.1700
Delta1: 0.5682
Delta2: 0.8301
Delta3: 0.9279

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: 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
0.7598	1.0	72	0.5809	0.7606	0.9281	0.9834	0.2597	0.3064	0.1320	0.3250	0.6234
0.4481	2.0	144	0.4013	0.3507	0.4879	0.4181	0.1415	0.1950	0.4427	0.7602	0.9021
0.4066	3.0	216	0.3706	0.3081	0.4484	0.3675	0.1269	0.1823	0.5187	0.7977	0.9148
0.3965	4.0	288	0.3641	0.2987	0.4336	0.3607	0.1239	0.1787	0.5294	0.8072	0.9205
0.3942	5.0	360	0.3582	0.2903	0.4251	0.3490	0.1207	0.1753	0.5466	0.8165	0.9232
0.3575	6.0	432	0.3568	0.2898	0.4184	0.3569	0.1211	0.1753	0.5390	0.8171	0.9265
0.3418	7.0	504	0.3490	0.2771	0.4178	0.3248	0.1156	0.1707	0.5783	0.8312	0.9259
0.2916	8.0	576	0.3512	0.2819	0.4172	0.3373	0.1178	0.1725	0.5620	0.8253	0.9262
0.3055	9.0	648	0.3506	0.2808	0.4091	0.3422	0.1180	0.1718	0.5537	0.8248	0.9292
0.2932	10.0	720	0.3518	0.2809	0.4110	0.3441	0.1182	0.1724	0.5548	0.8239	0.9290
0.2518	11.0	792	0.3476	0.2756	0.4115	0.3265	0.1155	0.1700	0.5741	0.8326	0.9264
0.3177	12.0	864	0.3491	0.2784	0.4104	0.3333	0.1169	0.1706	0.5620	0.8290	0.9283
0.3038	13.0	936	0.3503	0.2795	0.4094	0.3410	0.1175	0.1717	0.5596	0.8275	0.9283
0.3299	14.0	1008	0.3460	0.2750	0.4098	0.3257	0.1154	0.1693	0.5721	0.8325	0.9283
0.3325	15.0	1080	0.3476	0.2763	0.4088	0.3308	0.1161	0.1700	0.5682	0.8301	0.9279

数据统计

数据评估

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