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glpn-nyu-finetuned-diode-221214-054706

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

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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