glpn-nyu-finetuned-diode-221122-082237

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: 0.3421
Mae: 0.2700
Rmse: 0.4042
Abs Rel: 0.3279
Log Mae: 0.1132
Log Rmse: 0.1688
Delta1: 0.5839
Delta2: 0.8408
Delta3: 0.9309

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
0.7577	1.0	72	0.5814	0.7579	0.9267	0.9816	0.2592	0.3062	0.1341	0.3276	0.6248
0.4447	2.0	144	0.3947	0.3412	0.4785	0.4051	0.1381	0.1912	0.4585	0.7697	0.9072
0.4034	3.0	216	0.3657	0.2988	0.4357	0.3629	0.1242	0.1802	0.5321	0.8054	0.9202
0.3726	4.0	288	0.3576	0.2896	0.4176	0.3647	0.1215	0.1769	0.5376	0.8178	0.9270
0.3656	5.0	360	0.3547	0.2818	0.4098	0.3504	0.1186	0.1732	0.5551	0.8225	0.9283
0.3211	6.0	432	0.3495	0.2743	0.4160	0.3250	0.1155	0.1708	0.5773	0.8317	0.9258
0.3027	7.0	504	0.3471	0.2724	0.4123	0.3226	0.1146	0.1695	0.5801	0.8345	0.9283
0.2438	8.0	576	0.3494	0.2735	0.4097	0.3304	0.1157	0.1708	0.5741	0.8328	0.9267
0.2512	9.0	648	0.3448	0.2700	0.4121	0.3160	0.1134	0.1683	0.5880	0.8395	0.9266
0.2416	10.0	720	0.3439	0.2688	0.4017	0.3255	0.1135	0.1682	0.5781	0.8397	0.9324
0.1971	11.0	792	0.3456	0.2730	0.4059	0.3348	0.1148	0.1703	0.5730	0.8370	0.9305
0.2382	12.0	864	0.3446	0.2708	0.4069	0.3283	0.1139	0.1696	0.5818	0.8394	0.9295
0.237	13.0	936	0.3417	0.2674	0.4038	0.3218	0.1123	0.1680	0.5901	0.8424	0.9307
0.2378	14.0	1008	0.3421	0.2686	0.4030	0.3241	0.1130	0.1683	0.5852	0.8408	0.9311
0.2409	15.0	1080	0.3421	0.2700	0.4042	0.3279	0.1132	0.1688	0.5839	0.8408	0.9309

数据统计

数据评估

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