glpn-nyu-finetuned-diode-221122-030603

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: 0.3597
Mae: 0.3054
Rmse: 0.4481
Abs Rel: 0.3462
Log Mae: 0.1256
Log Rmse: 0.1798
Delta1: 0.5278
Delta2: 0.8055
Delta3: 0.9191

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: 5e-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
1.3722	1.0	72	0.9950	2.2271	2.3100	3.3726	0.5488	0.6025	0.0176	0.0929	0.1687
0.4936	2.0	144	0.4493	0.3961	0.5488	0.4918	0.1648	0.2234	0.3827	0.6877	0.8553
0.4087	3.0	216	0.3971	0.3434	0.4737	0.4250	0.1437	0.1980	0.4393	0.7431	0.8979
0.3696	4.0	288	0.3895	0.3355	0.4652	0.4334	0.1405	0.1968	0.4412	0.7566	0.9174
0.3621	5.0	360	0.3926	0.3432	0.4766	0.4198	0.1416	0.1967	0.4372	0.7591	0.9095
0.3104	6.0	432	0.3615	0.3104	0.4447	0.3630	0.1281	0.1813	0.5032	0.7923	0.9237
0.2835	7.0	504	0.3744	0.3140	0.4506	0.3429	0.1326	0.1824	0.4801	0.7837	0.9135
0.2308	8.0	576	0.3615	0.3013	0.4458	0.3459	0.1243	0.1803	0.5334	0.8139	0.9173
0.2433	9.0	648	0.3609	0.3042	0.4374	0.3570	0.1258	0.1798	0.5223	0.8053	0.9229
0.2097	10.0	720	0.3628	0.3066	0.4312	0.3792	0.1276	0.1815	0.4973	0.8075	0.9254
0.1761	11.0	792	0.3637	0.3111	0.4413	0.3726	0.1286	0.1817	0.4982	0.7979	0.9223
0.22	12.0	864	0.3584	0.3077	0.4619	0.3345	0.1258	0.1808	0.5360	0.8007	0.9119
0.2087	13.0	936	0.3614	0.3078	0.4502	0.3513	0.1265	0.1808	0.5211	0.8055	0.9177
0.2036	14.0	1008	0.3601	0.3086	0.4486	0.3530	0.1267	0.1801	0.5184	0.8023	0.9189
0.2123	15.0	1080	0.3597	0.3054	0.4481	0.3462	0.1256	0.1798	0.5278	0.8055	0.9191

数据统计

数据评估

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