sayakpaul/glpn-nyu-finetuned-diode-221121-113853

2023-12-27 23:11 0 微浪网 0

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导语： glpn-nyu-finetuned-diode-22...,

Training Loss	Epoch	Step	Validation Loss	Mae	Rmse	Abs Rel	Log Mae	Log Rmse	Delta1	Delta2	Delta3
0.7523	1.0	72	0.5772	0.7466	0.9116	0.9709	0.2568	0.3040	0.1373	0.3324	0.6328
0.4281	2.0	144	0.3849	0.3324	0.4673	0.3934	0.1349	0.1874	0.4681	0.7753	0.9142
0.3906	3.0	216	0.3660	0.3048	0.4418	0.3593	0.1258	0.1800	0.5225	0.7997	0.9195
0.3766	4.0	288	0.3556	0.2923	0.4275	0.3383	0.1209	0.1741	0.5450	0.8157	0.9259
0.3744	5.0	360	0.3539	0.2899	0.4171	0.3435	0.1208	0.1724	0.5355	0.8173	0.9307
0.328	6.0	432	0.3498	0.2860	0.4109	0.3418	0.1196	0.1709	0.5402	0.8193	0.9334
0.3166	7.0	504	0.3451	0.2793	0.4110	0.3203	0.1166	0.1677	0.5583	0.8286	0.9331
0.2639	8.0	576	0.3475	0.2823	0.4083	0.3341	0.1182	0.1695	0.5469	0.8251	0.9337
0.2802	9.0	648	0.3422	0.2779	0.4030	0.3249	0.1163	0.1667	0.5524	0.8287	0.9366
0.2701	10.0	720	0.3411	0.2781	0.3962	0.3316	0.1168	0.1664	0.5446	0.8286	0.9396
0.2232	11.0	792	0.3408	0.2755	0.3998	0.3259	0.1154	0.1665	0.5578	0.8332	0.9383
0.2921	12.0	864	0.3391	0.2749	0.3975	0.3220	0.1152	0.1652	0.5553	0.8332	0.9390
0.2837	13.0	936	0.3400	0.2745	0.3979	0.3251	0.1150	0.1660	0.5587	0.8347	0.9386
0.2922	14.0	1008	0.3370	0.2728	0.3965	0.3184	0.1142	0.1644	0.5602	0.8359	0.9401
0.2921	15.0	1080	0.3384	0.2739	0.3959	0.3230	0.1148	0.1651	0.5576	0.8345	0.9398

glpn-nyu-finetuned-diode-221121-113853

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: 0.3384

Mae: 0.2739

Rmse: 0.3959

Abs Rel: 0.3230

Log Mae: 0.1148

Log Rmse: 0.1651

Delta1: 0.5576

Delta2: 0.8345

Delta3: 0.9398

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

0.7523 1.0 72 0.5772 0.7466 0.9116 0.9709 0.2568 0.3040 0.1373 0.3324 0.6328

0.4281 2.0 144 0.3849 0.3324 0.4673 0.3934 0.1349 0.1874 0.4681 0.7753 0.9142

0.3906 3.0 216 0.3660 0.3048 0.4418 0.3593 0.1258 0.1800 0.5225 0.7997 0.9195

0.3766 4.0 288 0.3556 0.2923 0.4275 0.3383 0.1209 0.1741 0.5450 0.8157 0.9259

0.3744 5.0 360 0.3539 0.2899 0.4171 0.3435 0.1208 0.1724 0.5355 0.8173 0.9307

0.328 6.0 432 0.3498 0.2860 0.4109 0.3418 0.1196 0.1709 0.5402 0.8193 0.9334

0.3166 7.0 504 0.3451 0.2793 0.4110 0.3203 0.1166 0.1677 0.5583 0.8286 0.9331

0.2639 8.0 576 0.3475 0.2823 0.4083 0.3341 0.1182 0.1695 0.5469 0.8251 0.9337

0.2802 9.0 648 0.3422 0.2779 0.4030 0.3249 0.1163 0.1667 0.5524 0.8287 0.9366

0.2701 10.0 720 0.3411 0.2781 0.3962 0.3316 0.1168 0.1664 0.5446 0.8286 0.9396

0.2232 11.0 792 0.3408 0.2755 0.3998 0.3259 0.1154 0.1665 0.5578 0.8332 0.9383

0.2921 12.0 864 0.3391 0.2749 0.3975 0.3220 0.1152 0.1652 0.5553 0.8332 0.9390

0.2837 13.0 936 0.3400 0.2745 0.3979 0.3251 0.1150 0.1660 0.5587 0.8347 0.9386

0.2922 14.0 1008 0.3370 0.2728 0.3965 0.3184 0.1142 0.1644 0.5602 0.8359 0.9401

0.2921 15.0 1080 0.3384 0.2739 0.3959 0.3230 0.1148 0.1651 0.5576 0.8345 0.9398

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sayakpaul/glpn-nyu-finetuned-diode-221121-113853

glpn-nyu-finetuned-diode-221121-113853

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

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