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

数据统计

数据评估

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