glpn-nyu-finetuned-diode-221221-102136

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: 0.4222
Mae: 0.4110
Rmse: 0.6292
Abs Rel: 0.3778
Log Mae: 0.1636
Log Rmse: 0.2240
Delta1: 0.4320
Delta2: 0.6806
Delta3: 0.8068

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: 0.0005
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: 0.15
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
0.4953	1.0	72	0.4281	0.4216	0.6448	0.3539	0.1696	0.2312	0.4427	0.6625	0.7765
0.3855	2.0	144	0.4749	0.4444	0.6498	0.4156	0.1846	0.2408	0.3612	0.6027	0.7728
0.4158	3.0	216	0.5042	0.5122	0.7196	0.4385	0.2264	0.2834	0.2797	0.4837	0.6699
0.388	4.0	288	0.4418	0.4304	0.6473	0.4030	0.1745	0.2378	0.4027	0.6497	0.7900
0.4595	5.0	360	0.4394	0.4154	0.6292	0.4012	0.1664	0.2285	0.4262	0.6613	0.8021
0.393	6.0	432	0.4252	0.4060	0.6153	0.3944	0.1617	0.2215	0.4318	0.6747	0.8128
0.3468	7.0	504	0.4413	0.4366	0.6479	0.3835	0.1818	0.2385	0.3778	0.6248	0.7770
0.316	8.0	576	0.4218	0.4048	0.6192	0.3844	0.1606	0.2215	0.4374	0.6896	0.8119
0.3123	9.0	648	0.4263	0.4168	0.6295	0.3765	0.1689	0.2267	0.4139	0.6612	0.7976
0.2973	10.0	720	0.4222	0.4110	0.6292	0.3778	0.1636	0.2240	0.4320	0.6806	0.8068

数据统计

数据评估

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