readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-230125-042306
    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-230125-042306
  
  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: 0.4380
  - Mae: 0.4255
  - Rmse: 0.6150
  - Abs Rel: 0.4444
  - Log Mae: 0.1724
  - Log Rmse: 0.2247
  - Delta1: 0.3675
  - Delta2: 0.6329
  - Delta3: 0.8147
  
  ## 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: 100
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Mae    | Rmse   | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
  | 1.0761        | 1.0   | 72   | 0.5029          | 0.4779 | 0.6689 | 0.5504  | 0.2005  | 0.2590   | 0.3023 | 0.5336 | 0.8000 |
  | 0.4776        | 2.0   | 144  | 0.4638          | 0.4495 | 0.6305 | 0.4854  | 0.1854  | 0.2371   | 0.3323 | 0.5842 | 0.7749 |
  | 0.4668        | 3.0   | 216  | 0.4843          | 0.4705 | 0.6368 | 0.5459  | 0.1961  | 0.2469   | 0.3115 | 0.5258 | 0.7237 |
  | 0.439         | 4.0   | 288  | 0.4596          | 0.4383 | 0.6224 | 0.4903  | 0.1794  | 0.2347   | 0.3564 | 0.6054 | 0.7900 |
  | 0.4629        | 5.0   | 360  | 0.4846          | 0.4622 | 0.6347 | 0.5505  | 0.1914  | 0.2466   | 0.3240 | 0.5567 | 0.7432 |
  | 0.4557        | 6.0   | 432  | 0.4660          | 0.4399 | 0.6223 | 0.5107  | 0.1801  | 0.2373   | 0.3605 | 0.5922 | 0.7992 |
  | 0.4131        | 7.0   | 504  | 0.4737          | 0.4466 | 0.6291 | 0.4877  | 0.1847  | 0.2387   | 0.3592 | 0.5753 | 0.7545 |
  | 0.3742        | 8.0   | 576  | 0.4756          | 0.4555 | 0.6363 | 0.5127  | 0.1879  | 0.2424   | 0.3462 | 0.5642 | 0.7581 |
  | 0.3943        | 9.0   | 648  | 0.4816          | 0.4606 | 0.6340 | 0.5566  | 0.1901  | 0.2459   | 0.3304 | 0.5512 | 0.7484 |
  | 0.3699        | 10.0  | 720  | 0.4779          | 0.4527 | 0.6289 | 0.5402  | 0.1869  | 0.2433   | 0.3419 | 0.5659 | 0.7699 |
  | 0.3695        | 11.0  | 792  | 0.4335          | 0.4185 | 0.6141 | 0.4174  | 0.1685  | 0.2210   | 0.3837 | 0.6484 | 0.8142 |
  | 0.4268        | 12.0  | 864  | 0.4831          | 0.4622 | 0.6345 | 0.5491  | 0.1912  | 0.2456   | 0.3283 | 0.5515 | 0.7369 |
  | 0.4295        | 13.0  | 936  | 0.4512          | 0.4421 | 0.6267 | 0.4498  | 0.1803  | 0.2292   | 0.3508 | 0.5951 | 0.7803 |
  | 0.4071        | 14.0  | 1008 | 0.4632          | 0.4514 | 0.6295 | 0.4755  | 0.1842  | 0.2334   | 0.3514 | 0.5676 | 0.7346 |
  | 0.4383        | 15.0  | 1080 | 0.4655          | 0.4394 | 0.6283 | 0.4894  | 0.1793  | 0.2370   | 0.3762 | 0.6022 | 0.7816 |
  | 0.4009        | 16.0  | 1152 | 0.4684          | 0.4434 | 0.6294 | 0.5215  | 0.1814  | 0.2403   | 0.3601 | 0.5881 | 0.7980 |
  | 0.3889        | 17.0  | 1224 | 0.4619          | 0.4379 | 0.6357 | 0.4623  | 0.1791  | 0.2389   | 0.3946 | 0.6088 | 0.7665 |
  | 0.4114        | 18.0  | 1296 | 0.4838          | 0.4642 | 0.6358 | 0.5514  | 0.1924  | 0.2471   | 0.3310 | 0.5444 | 0.7336 |
  | 0.3656        | 19.0  | 1368 | 0.4771          | 0.4524 | 0.6317 | 0.5284  | 0.1869  | 0.2428   | 0.3379 | 0.5765 | 0.7665 |
  | 0.4117        | 20.0  | 1440 | 0.4388          | 0.4187 | 0.6257 | 0.4113  | 0.1680  | 0.2270   | 0.4162 | 0.6619 | 0.8001 |
  | 0.3641        | 21.0  | 1512 | 0.4593          | 0.4374 | 0.6238 | 0.4773  | 0.1779  | 0.2332   | 0.3705 | 0.6088 | 0.7745 |
  | 0.3559        | 22.0  | 1584 | 0.4534          | 0.4300 | 0.6242 | 0.4663  | 0.1747  | 0.2329   | 0.3854 | 0.6288 | 0.7987 |
  | 0.3897        | 23.0  | 1656 | 0.4695          | 0.4506 | 0.6292 | 0.5215  | 0.1852  | 0.2404   | 0.3432 | 0.5746 | 0.7698 |
  | 0.4281        | 24.0  | 1728 | 0.4920          | 0.4693 | 0.6380 | 0.5835  | 0.1949  | 0.2514   | 0.3239 | 0.5352 | 0.7230 |
  | 0.4113        | 25.0  | 1800 | 0.4525          | 0.4335 | 0.6405 | 0.4109  | 0.1757  | 0.2330   | 0.4046 | 0.6251 | 0.7878 |
  | 0.3734        | 26.0  | 1872 | 0.4357          | 0.4159 | 0.6203 | 0.4158  | 0.1667  | 0.2241   | 0.4234 | 0.6609 | 0.7919 |
  | 0.3408        | 27.0  | 1944 | 0.4544          | 0.4419 | 0.6257 | 0.4712  | 0.1806  | 0.2325   | 0.3525 | 0.5993 | 0.7850 |
  | 0.3816        | 28.0  | 2016 