glpn-nyu-finetuned-diode-221214-054706

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: 0.3340
Mae: 0.2649
Rmse: 0.3917
Abs Rel: 0.3138
Log Mae: 0.1111
Log Rmse: 0.1640
Delta1: 0.5843
Delta2: 0.8459
Delta3: 0.9413

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: 2e-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
0.746	1.0	72	0.5730	0.7316	0.8964	0.9501	0.2530	0.3005	0.1427	0.3446	0.6453
0.4215	2.0	144	0.3764	0.3140	0.4548	0.3607	0.1286	0.1819	0.5054	0.7927	0.9161
0.3566	3.0	216	0.3568	0.2894	0.4226	0.3436	0.1206	0.1751	0.5475	0.8178	0.9257
0.322	4.0	288	0.3523	0.2818	0.4069	0.3541	0.1188	0.1740	0.5508	0.8219	0.9325
0.3066	5.0	360	0.3464	0.2777	0.4090	0.3283	0.1164	0.1695	0.5609	0.8328	0.9313
0.256	6.0	432	0.3384	0.2687	0.3927	0.3173	0.1136	0.1647	0.5665	0.8381	0.9394
0.2268	7.0	504	0.3407	0.2747	0.4138	0.3091	0.1143	0.1672	0.5716	0.8380	0.9322
0.1721	8.0	576	0.3346	0.2631	0.3850	0.3176	0.1108	0.1636	0.5804	0.8489	0.9452
0.1898	9.0	648	0.3362	0.2656	0.3994	0.3100	0.1112	0.1650	0.5910	0.8455	0.9368
0.1609	10.0	720	0.3355	0.2667	0.3866	0.3242	0.1124	0.1648	0.5693	0.8452	0.9433
0.139	11.0	792	0.3344	0.2660	0.3954	0.3115	0.1112	0.1639	0.5833	0.8453	0.9409
0.1772	12.0	864	0.3360	0.2667	0.3979	0.3137	0.1115	0.1650	0.5854	0.8449	0.9393
0.1631	13.0	936	0.3353	0.2652	0.3938	0.3134	0.1112	0.1645	0.5858	0.8451	0.9403
0.1736	14.0	1008	0.3367	0.2663	0.3954	0.3163	0.1117	0.1653	0.5840	0.8446	0.9391
0.1736	15.0	1080	0.3340	0.2649	0.3917	0.3138	0.1111	0.1640	0.5843	0.8459	0.9413

数据统计

数据评估

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