sayakpaul/glpn-nyu-finetuned-diode-221121-063504

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收录时间: 2025-10-06 02:58:49

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标签: Depth Estimation

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glpn-nyu-finetuned-diode-221121-063504

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: 0.3533
Mae: 0.2668
Rmse: 0.3716
Abs Rel: 0.3427
Log Mae: 0.1167
Log Rmse: 0.1703
Delta1: 0.5522
Delta2: 0.8362
Delta3: 0.9382

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
1.3991	1.0	72	1.2199	3.6023	3.6519	5.2780	0.7010	0.7461	0.0	0.0007	0.0616
1.1099	2.0	144	0.7471	1.2562	1.5028	1.6644	0.3550	0.4165	0.0965	0.2342	0.4292
0.5036	3.0	216	0.4876	0.5019	0.6198	0.7615	0.2000	0.2599	0.2878	0.5643	0.7803
0.4157	4.0	288	0.3789	0.3008	0.4211	0.3793	0.1291	0.1840	0.4961	0.7961	0.9261
0.4043	5.0	360	0.3795	0.3025	0.4117	0.4028	0.1303	0.1850	0.4889	0.7892	0.9278
0.3638	6.0	432	0.3790	0.3022	0.4019	0.4175	0.1313	0.1862	0.4851	0.7889	0.9262
0.3532	7.0	504	0.3605	0.2756	0.3864	0.3447	0.1201	0.1732	0.5397	0.8202	0.9330
0.3087	8.0	576	0.3599	0.2781	0.3896	0.3365	0.1206	0.1722	0.5312	0.8183	0.9332
0.3232	9.0	648	0.3613	0.2772	0.3879	0.3444	0.1204	0.1733	0.5341	0.8237	0.9334
0.3072	10.0	720	0.3570	0.2752	0.3794	0.3582	0.1195	0.1731	0.5341	0.8270	0.9374
0.2673	11.0	792	0.3633	0.2747	0.3838	0.3390	0.1207	0.1728	0.5330	0.8221	0.9333
0.3222	12.0	864	0.3548	0.2713	0.3783	0.3441	0.1180	0.1711	0.5448	0.8315	0.9367
0.3072	13.0	936	0.3532	0.2668	0.3700	0.3441	0.1168	0.1701	0.5502	0.8353	0.9387
0.3214	14.0	1008	0.3553	0.2674	0.3747	0.3322	0.1177	0.1701	0.5472	0.8324	0.9355
0.3406	15.0	1080	0.3533	0.2668	0.3716	0.3427	0.1167	0.1703	0.5522	0.8362	0.9382

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