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glpn-nyu-finetuned-diode-221214-081122

Depth Estimation Transformers PyTorch glpn vision generated_from_trainer License: apache-2.0
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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
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Evaluation results

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