glpn-nyu-finetuned-diode-221214-081122

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: 0.3242
Mae: 0.2603
Rmse: 0.3997
Abs Rel: 0.3010
Log Mae: 0.1073
Log Rmse: 0.1624
Delta1: 0.6187
Delta2: 0.8455
Delta3: 0.9378

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: 0.15
num_epochs: 25
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Mae	Rmse	Abs Rel	Log Mae	Log Rmse	Delta1	Delta2	Delta3
0.6896	1.0	72	0.4753	0.4670	0.6226	0.5658	0.1791	0.2313	0.2950	0.6310	0.8562
0.3628	2.0	144	0.3565	0.2956	0.4307	0.3421	0.1226	0.1737	0.5285	0.8079	0.9245
0.3168	3.0	216	0.3486	0.2774	0.3963	0.3464	0.1172	0.1710	0.5561	0.8285	0.9358
0.2734	4.0	288	0.3368	0.2669	0.3962	0.3260	0.1122	0.1671	0.5787	0.8453	0.9383
0.2678	5.0	360	0.3419	0.2700	0.4136	0.3118	0.1130	0.1689	0.5869	0.8399	0.9289
0.2271	6.0	432	0.3270	0.2611	0.3890	0.3050	0.1091	0.1608	0.5899	0.8500	0.9453
0.1905	7.0	504	0.3331	0.2651	0.3996	0.3086	0.1110	0.1645	0.5925	0.8391	0.9405
0.1436	8.0	576	0.3323	0.2648	0.4039	0.3087	0.1097	0.1653	0.6019	0.8475	0.9345
0.1687	9.0	648	0.3274	0.2620	0.3887	0.3129	0.1092	0.1622	0.5954	0.8464	0.9422
0.1407	10.0	720	0.3344	0.2689	0.4134	0.3079	0.1107	0.1667	0.6039	0.8423	0.9287
0.1159	11.0	792	0.3302	0.2675	0.4081	0.3032	0.1103	0.1646	0.6035	0.8406	0.9334
0.16	12.0	864	0.3262	0.2599	0.3989	0.2986	0.1074	0.1621	0.6177	0.8460	0.9371
0.1385	13.0	936	0.3287	0.2616	0.3976	0.3114	0.1085	0.1643	0.6095	0.8472	0.9363
0.156	14.0	1008	0.3291	0.2690	0.4147	0.3048	0.1101	0.1654	0.6082	0.8390	0.9305
0.1534	15.0	1080	0.3267	0.2651	0.3994	0.3084	0.1096	0.1632	0.6030	0.8406	0.9376
0.1196	16.0	1152	0.3248	0.2588	0.4028	0.2908	0.1065	0.1615	0.6265	0.8467	0.9357
0.0983	17.0	1224	0.3249	0.2612	0.4046	0.2940	0.1075	0.1620	0.6198	0.8440	0.9346
0.1347	18.0	1296	0.3209	0.2608	0.4012	0.2971	0.1069	0.1614	0.6201	0.8473	0.9376
0.107	19.0	1368	0.3249	0.2624	0.4026	0.3013	0.1079	0.1628	0.6149	0.8453	0.9362
0.1214	20.0	1440	0.3213	0.2586	0.3976	0.2962	0.1065	0.1609	0.6219	0.8464	0.9382
0.0921	21.0	1512	0.3240	0.2600	0.3971	0.3028	0.1074	0.1624	0.6179	0.8457	0.9383
0.0906	22.0	1584	0.3239	0.2602	0.4025	0.2968	0.1069	0.1622	0.6227	0.8461	0.9365
0.0978	23.0	1656	0.3230	0.2588	0.3990	0.2969	0.1066	0.1617	0.6234	0.8462	0.9371
0.1377	24.0	1728	0.3244	0.2612	0.4013	0.3005	0.1076	0.1626	0.6180	0.8447	0.9359
0.1253	25.0	1800	0.3242	0.2603	0.3997	0.3010	0.1073	0.1624	0.6187	0.8455	0.9378

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu116
Tokenizers 0.13.2

数据统计

数据评估

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sayakpaul/glpn-nyu-finetuned-diode-221214-081122

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glpn-nyu-finetuned-diode-221214-081122

Model description

Intended uses & limitations

Training and evaluation data