readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-221228-072509
    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-221228-072509
  
  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: 0.4012
  - Mae: 0.4030
  - Rmse: 0.6173
  - Abs Rel: 0.3487
  - Log Mae: 0.1574
  - Log Rmse: 0.2110
  - Delta1: 0.4308
  - Delta2: 0.6997
  - Delta3: 0.8249
  
  ## 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: 0.0001
  - 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: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Mae    | Rmse   | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
  | 1.1571        | 1.0   | 72   | 0.6604          | 0.6233 | 0.8403 | 0.5125  | 0.3119  | 0.3691   | 0.1726 | 0.3423 | 0.4877 |
  | 0.4895        | 2.0   | 144  | 0.4506          | 0.4460 | 0.6404 | 0.4241  | 0.1812  | 0.2299   | 0.3325 | 0.6053 | 0.7943 |
  | 0.4709        | 3.0   | 216  | 0.4414          | 0.4370 | 0.6305 | 0.4243  | 0.1764  | 0.2253   | 0.3537 | 0.6145 | 0.7988 |
  | 0.4436        | 4.0   | 288  | 0.4335          | 0.4324 | 0.6285 | 0.4045  | 0.1746  | 0.2245   | 0.3444 | 0.6506 | 0.8096 |
  | 0.4656        | 5.0   | 360  | 0.4552          | 0.4515 | 0.6328 | 0.4614  | 0.1838  | 0.2307   | 0.3374 | 0.5762 | 0.7722 |
  | 0.4482        | 6.0   | 432  | 0.4234          | 0.4166 | 0.6233 | 0.3805  | 0.1654  | 0.2179   | 0.4035 | 0.6623 | 0.8130 |
  | 0.4099        | 7.0   | 504  | 0.4176          | 0.4185 | 0.6238 | 0.3676  | 0.1662  | 0.2150   | 0.3937 | 0.6589 | 0.8153 |
  | 0.3987        | 8.0   | 576  | 0.4515          | 0.4431 | 0.6300 | 0.4497  | 0.1792  | 0.2283   | 0.3561 | 0.5906 | 0.7781 |
  | 0.396         | 9.0   | 648  | 0.4235          | 0.4267 | 0.6347 | 0.3591  | 0.1716  | 0.2224   | 0.3934 | 0.6310 | 0.7963 |
  | 0.3608        | 10.0  | 720  | 0.4312          | 0.4181 | 0.6227 | 0.4022  | 0.1666  | 0.2217   | 0.4014 | 0.6586 | 0.8173 |
  | 0.3568        | 11.0  | 792  | 0.4322          | 0.4198 | 0.6183 | 0.4047  | 0.1674  | 0.2186   | 0.3870 | 0.6420 | 0.8071 |
  | 0.3923        | 12.0  | 864  | 0.4225          | 0.4196 | 0.6294 | 0.3630  | 0.1668  | 0.2181   | 0.3910 | 0.6537 | 0.8151 |
  | 0.3971        | 13.0  | 936  | 0.4086          | 0.4105 | 0.6219 | 0.3541  | 0.1614  | 0.2144   | 0.4234 | 0.6820 | 0.8144 |
  | 0.372         | 14.0  | 1008 | 0.4127          | 0.4099 | 0.6172 | 0.3668  | 0.1612  | 0.2119   | 0.4046 | 0.6727 | 0.8260 |
  | 0.3884        | 15.0  | 1080 | 0.4060          | 0.4074 | 0.6176 | 0.3528  | 0.1598  | 0.2119   | 0.4109 | 0.6925 | 0.8225 |
  | 0.3616        | 16.0  | 1152 | 0.4078          | 0.4092 | 0.6198 | 0.3532  | 0.1615  | 0.2139   | 0.4162 | 0.6791 | 0.8186 |
  | 0.3504        | 17.0  | 1224 | 0.4202          | 0.4320 | 0.6408 | 0.3613  | 0.1740  | 0.2261   | 0.3769 | 0.6301 | 0.7915 |
  | 0.3823        | 18.0  | 1296 | 0.4328          | 0.4218 | 0.6182 | 0.4198  | 0.1684  | 0.2207   | 0.3916 | 0.6371 | 0.8113 |
  | 0.3437        | 19.0  | 1368 | 0.4133          | 0.4138 | 0.6205 | 0.3638  | 0.1636  | 0.2162   | 0.3967 | 0.6761 | 0.8188 |
  | 0.3739        | 20.0  | 1440 | 0.4040          | 0.4070 | 0.6187 | 0.3486  | 0.1594  | 0.2124   | 0.4214 | 0.6813 | 0.8214 |
  | 0.3397        | 21.0  | 1512 | 0.4180          | 0.4300 | 0.6360 | 0.3601  | 0.1732  | 0.2239   | 0.3708 | 0.6362 | 0.8006 |
  | 0.332         | 22.0  | 1584 | 0.4025          | 0.4050 | 0.6182 | 0.3505  | 0.1582  | 0.2114   | 0.4274 | 0.6909 | 0.8275 |
  | 0.3552        | 23.0  | 1656 | 0.4120          | 0.4179 | 0.6305 | 0.3569  | 0.1650  | 0.2188   | 0.4002 | 0.6753 | 0.8102 |
  | 0.3804        | 24.0  | 1728 | 0.4093          | 0.4111 | 0.6223 | 0.3594  | 0.1620  | 0.2152   | 0.4068 | 0.6851 | 0.8166 |
  | 0.3519        | 25.0  | 1800 | 0.4039          | 0.4122 | 0.6237 | 0.3511  | 0.1621  | 0.2137   | 0.4109 | 0.6895 | 0.8171 |
  | 0.3276        | 26.0  | 1872 | 0.4044          | 0.4117 | 0.6183 | 0.3533  | 0.1623  | 0.2127   | 0.3979 | 0.6824 | 0.8251 |
  | 0.3167        | 27.0  | 1944 | 0.4091          | 0.4099 | 0.6189 | 0.3600  | 0.1613  | 0.2135   | 0.4069 | 0.6898 | 0.8218 |
  | 0.3547        | 28.0  | 2016 |