sayakpaul/glpn-nyu-finetuned-diode-221122-044810

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sayakpaul/glpn-nyu-finetuned-diode-221122-044810


glpn-nyu-finetuned-diode-221122-044810

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: 0.3690
  • Mae: 0.2909
  • Rmse: 0.4208
  • Abs Rel: 0.3635
  • Log Mae: 0.1224
  • Log Rmse: 0.1793
  • Delta1: 0.5323
  • Delta2: 0.8179
  • Delta3: 0.9258


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
1.3864 1.0 72 1.2016 3.4656 3.5204 5.1101 0.6881 0.7346 0.0 0.0011 0.0764
1.0082 2.0 144 0.4607 0.4107 0.5420 0.5254 0.1697 0.2234 0.3596 0.6460 0.8465
0.4656 3.0 216 0.4071 0.3431 0.4758 0.4359 0.1425 0.1992 0.4567 0.7481 0.8958
0.4093 4.0 288 0.3953 0.3261 0.4622 0.4197 0.1363 0.1947 0.4841 0.7624 0.9103
0.392 5.0 360 0.3916 0.3211 0.4463 0.4116 0.1338 0.1896 0.4810 0.7756 0.9176
0.3466 6.0 432 0.3807 0.3075 0.4451 0.3658 0.1293 0.1839 0.5026 0.7921 0.9180
0.3297 7.0 504 0.3811 0.3047 0.4448 0.3534 0.1290 0.1835 0.5066 0.7920 0.9137
0.2768 8.0 576 0.3779 0.3057 0.4283 0.3894 0.1280 0.1832 0.5046 0.7996 0.9256
0.2849 9.0 648 0.3753 0.2978 0.4341 0.3496 0.1259 0.1806 0.5149 0.8041 0.9184
0.2571 10.0 720 0.3825 0.3068 0.4305 0.3896 0.1289 0.1849 0.4998 0.7974 0.9206
0.2246 11.0 792 0.3718 0.2951 0.4235 0.3678 0.1240 0.1803 0.5249 0.8105 0.9248
0.2703 12.0 864 0.3716 0.2945 0.4317 0.3593 0.1235 0.1808 0.5324 0.8122 0.9215
0.2596 13.0 936 0.3692 0.2921 0.4185 0.3690 0.1229 0.1798 0.5294 0.8167 0.9264
0.2604 14.0 1008 0.3684 0.2893 0.4171 0.3601 0.1223 0.1785 0.5325 0.8179 0.9252
0.2679 15.0 1080 0.3690 0.2909 0.4208 0.3635 0.1224 0.1793 0.5323 0.8179 0.9258

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