readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-221122-044810
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # glpn-nyu-finetuned-diode-221122-044810
  
  This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/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 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu116
  - Tokenizers 0.13.2
  