readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-221228-113625
    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-221228-113625
  
  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: 0.3996
  - Mae: 0.4013
  - Rmse: 0.6161
  - Abs Rel: 0.3535
  - Log Mae: 0.1568
  - Log Rmse: 0.2121
  - Delta1: 0.4381
  - Delta2: 0.7025
  - Delta3: 0.8196
  
  ## 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: 0.0003
  - 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: 75
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Mae    | Rmse   | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
  | 1.0075        | 1.0   | 72   | 0.4809          | 0.4610 | 0.6461 | 0.5165  | 0.1901  | 0.2446   | 0.3157 | 0.5632 | 0.8017 |
  | 0.4692        | 2.0   | 144  | 0.4432          | 0.4491 | 0.6531 | 0.3950  | 0.1821  | 0.2318   | 0.3347 | 0.6198 | 0.7910 |
  | 0.4635        | 3.0   | 216  | 0.4361          | 0.4278 | 0.6252 | 0.4165  | 0.1715  | 0.2230   | 0.3780 | 0.6285 | 0.8090 |
  | 0.4364        | 4.0   | 288  | 0.4255          | 0.4200 | 0.6222 | 0.3930  | 0.1673  | 0.2198   | 0.3824 | 0.6639 | 0.8206 |
  | 0.4632        | 5.0   | 360  | 0.4376          | 0.4267 | 0.6241 | 0.4144  | 0.1708  | 0.2235   | 0.3806 | 0.6337 | 0.8122 |
  | 0.4703        | 6.0   | 432  | 0.4340          | 0.4315 | 0.6354 | 0.3799  | 0.1740  | 0.2262   | 0.3788 | 0.6275 | 0.7945 |
  | 0.4136        | 7.0   | 504  | 0.4453          | 0.4291 | 0.6368 | 0.4144  | 0.1726  | 0.2306   | 0.3965 | 0.6458 | 0.7965 |
  | 0.394         | 8.0   | 576  | 0.4620          | 0.4440 | 0.6297 | 0.4728  | 0.1808  | 0.2336   | 0.3606 | 0.5832 | 0.7826 |
  | 0.4073        | 9.0   | 648  | 0.4485          | 0.4372 | 0.6244 | 0.4439  | 0.1769  | 0.2266   | 0.3511 | 0.6010 | 0.8002 |
  | 0.3967        | 10.0  | 720  | 0.4523          | 0.4320 | 0.6250 | 0.4606  | 0.1750  | 0.2307   | 0.3676 | 0.6255 | 0.8146 |
  | 0.3797        | 11.0  | 792  | 0.4413          | 0.4360 | 0.6332 | 0.4047  | 0.1769  | 0.2258   | 0.3426 | 0.6277 | 0.8025 |
  | 0.439         | 12.0  | 864  | 0.4544          | 0.4365 | 0.6356 | 0.4215  | 0.1768  | 0.2299   | 0.3561 | 0.6282 | 0.8050 |
  | 0.4666        | 13.0  | 936  | 0.4349          | 0.4278 | 0.6267 | 0.3893  | 0.1729  | 0.2227   | 0.3615 | 0.6375 | 0.8053 |
  | 0.4071        | 14.0  | 1008 | 0.4337          | 0.4220 | 0.6235 | 0.3822  | 0.1692  | 0.2202   | 0.3909 | 0.6376 | 0.8044 |
  | 0.4359        | 15.0  | 1080 | 0.4259          | 0.4193 | 0.6266 | 0.3855  | 0.1669  | 0.2217   | 0.4022 | 0.6601 | 0.8100 |
  | 0.39          | 16.0  | 1152 | 0.4268          | 0.4075 | 0.6161 | 0.3981  | 0.1605  | 0.2184   | 0.4214 | 0.6838 | 0.8205 |
  | 0.3654        | 17.0  | 1224 | 0.4503          | 0.4461 | 0.6615 | 0.3791  | 0.1840  | 0.2417   | 0.3783 | 0.6161 | 0.7636 |
  | 0.4256        | 18.0  | 1296 | 0.4743          | 0.4529 | 0.6319 | 0.5162  | 0.1852  | 0.2398   | 0.3461 | 0.5736 | 0.7490 |
  | 0.372         | 19.0  | 1368 | 0.4462          | 0.4326 | 0.6443 | 0.4068  | 0.1752  | 0.2331   | 0.3875 | 0.6410 | 0.7922 |
  | 0.41          | 20.0  | 1440 | 0.4351          | 0.4500 | 0.6579 | 0.3735  | 0.1849  | 0.2365   | 0.3460 | 0.6021 | 0.7751 |
  | 0.3683        | 21.0  | 1512 | 0.4060          | 0.4084 | 0.6177 | 0.3495  | 0.1605  | 0.2107   | 0.4168 | 0.6702 | 0.8235 |
  | 0.36          | 22.0  | 1584 | 0.4447          | 0.4517 | 0.6667 | 0.3788  | 0.1852  | 0.2414   | 0.3676 | 0.6122 | 0.7572 |
  | 0.4257        | 23.0  | 1656 | 0.4297          | 0.4141 | 0.6180 | 0.4066  | 0.1646  | 0.2201   | 0.4134 | 0.6586 | 0.8105 |
  | 0.4344        | 24.0  | 1728 | 0.4545          | 0.4312 | 0.6237 | 0.4587  | 0.1742  | 0.2296   | 0.3769 | 0.6137 | 0.8008 |
  | 0.4057        | 25.0  | 1800 | 0.4161          | 0.4099 | 0.6175 | 0.3744  | 0.1619  | 0.2144   | 0.4100 | 0.6701 | 0.8231 |
  | 0.3569        | 26.0  | 1872 | 0.4199          | 0.4120 | 0.6181 | 0.3840  | 0.1634  | 0.2177   | 0.4039 | 0.6765 | 0.8165 |
  | 0.3479        | 27.0  | 1944 | 0.4327          | 0.4180 | 0.6174 | 0.4138  | 0.1668  | 0.2205   | 0.3912 | 0.6481 | 0.8230 |
  | 0.3732        | 28.0  | 2016 |