readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-230124-104649
    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-230124-104649
  
  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: 0.4340
  - Mae: 0.4201
  - Rmse: 0.6110
  - Abs Rel: 0.4400
  - Log Mae: 0.1698
  - Log Rmse: 0.2229
  - Delta1: 0.3745
  - Delta2: 0.6423
  - Delta3: 0.8241
  
  ## 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   | 0.5035          | 0.4784 | 0.6697 | 0.5506  | 0.2007  | 0.2592   | 0.3019 | 0.5331 | 0.7997 |
  | 0.4776        | 2.0   | 144  | 0.4640          | 0.4494 | 0.6305 | 0.4846  | 0.1853  | 0.2370   | 0.3321 | 0.5850 | 0.7752 |
  | 0.4667        | 3.0   | 216  | 0.4852          | 0.4716 | 0.6377 | 0.5477  | 0.1965  | 0.2473   | 0.3105 | 0.5246 | 0.7218 |
  | 0.4387        | 4.0   | 288  | 0.4587          | 0.4378 | 0.6223 | 0.4874  | 0.1790  | 0.2343   | 0.3577 | 0.6064 | 0.7906 |
  | 0.4612        | 5.0   | 360  | 0.4843          | 0.4610 | 0.6341 | 0.5444  | 0.1906  | 0.2458   | 0.3269 | 0.5602 | 0.7445 |
  | 0.4564        | 6.0   | 432  | 0.4605          | 0.4330 | 0.6197 | 0.4901  | 0.1764  | 0.2339   | 0.3775 | 0.6049 | 0.8022 |
  | 0.4166        | 7.0   | 504  | 0.4576          | 0.4421 | 0.6256 | 0.4625  | 0.1809  | 0.2322   | 0.3613 | 0.5882 | 0.7685 |
  | 0.3922        | 8.0   | 576  | 0.4805          | 0.4537 | 0.6296 | 0.5422  | 0.1875  | 0.2439   | 0.3381 | 0.5612 | 0.7874 |
  | 0.3944        | 9.0   | 648  | 0.4601          | 0.4430 | 0.6254 | 0.4762  | 0.1812  | 0.2332   | 0.3545 | 0.5877 | 0.7662 |
  | 0.3748        | 10.0  | 720  | 0.4606          | 0.4377 | 0.6221 | 0.4960  | 0.1796  | 0.2354   | 0.3573 | 0.5960 | 0.8031 |
  | 0.3749        | 11.0  | 792  | 0.4513          | 0.4377 | 0.6300 | 0.4403  | 0.1786  | 0.2311   | 0.3621 | 0.6083 | 0.7901 |
  | 0.4259        | 12.0  | 864  | 0.4834          | 0.4519 | 0.6328 | 0.5462  | 0.1862  | 0.2457   | 0.3521 | 0.5777 | 0.7614 |
  | 0.4337        | 13.0  | 936  | 0.4338          | 0.4153 | 0.6214 | 0.4096  | 0.1664  | 0.2248   | 0.4137 | 0.6651 | 0.8037 |
  | 0.4032        | 14.0  | 1008 | 0.4640          | 0.4544 | 0.6279 | 0.4922  | 0.1868  | 0.2351   | 0.3286 | 0.5574 | 0.7557 |
  | 0.4286        | 15.0  | 1080 | 0.4835          | 0.4651 | 0.6338 | 0.5567  | 0.1929  | 0.2465   | 0.3197 | 0.5449 | 0.7304 |
  | 0.397         | 16.0  | 1152 | 0.4713          | 0.4547 | 0.6279 | 0.5121  | 0.1872  | 0.2383   | 0.3312 | 0.5644 | 0.7456 |
  | 0.3713        | 17.0  | 1224 | 0.4664          | 0.4375 | 0.6290 | 0.4766  | 0.1780  | 0.2361   | 0.3821 | 0.6036 | 0.7668 |
  | 0.4155        | 18.0  | 1296 | 0.4881          | 0.4722 | 0.6367 | 0.5705  | 0.1967  | 0.2494   | 0.3061 | 0.5293 | 0.7220 |
  | 0.3822        | 19.0  | 1368 | 0.4819          | 0.4592 | 0.6322 | 0.5526  | 0.1898  | 0.2456   | 0.3357 | 0.5531 | 0.7365 |
  | 0.408         | 20.0  | 1440 | 0.4367          | 0.4201 | 0.6141 | 0.4322  | 0.1691  | 0.2232   | 0.3902 | 0.6418 | 0.8083 |
  | 0.3698        | 21.0  | 1512 | 0.4461          | 0.4263 | 0.6171 | 0.4454  | 0.1724  | 0.2263   | 0.3850 | 0.6232 | 0.7980 |
  | 0.3628        | 22.0  | 1584 | 0.4461          | 0.4254 | 0.6226 | 0.4520  | 0.1724  | 0.2307   | 0.3953 | 0.6324 | 0.7926 |
  | 0.3827        | 23.0  | 1656 | 0.4753          | 0.4529 | 0.6364 | 0.5288  | 0.1867  | 0.2450   | 0.3415 | 0.5893 | 0.7678 |
  | 0.4378        | 24.0  | 1728 | 0.4779          | 0.4608 | 0.6308 | 0.5422  | 0.1907  | 0.2433   | 0.3247 | 0.5435 | 0.7434 |
  | 0.3766        | 25.0  | 1800 | 0.4533          | 0.4415 | 0.6231 | 0.4750  | 0.1802  | 0.2309   | 0.3544 | 0.5796 | 0.7917 |
  | 0.3642        | 26.0  | 1872 | 0.4520          | 0.4276 | 0.6224 | 0.4686  | 0.1736  | 0.2322   | 0.3901 | 0.6242 | 0.8048 |
  | 0.3503        | 27.0  | 1944 | 0.4451          | 0.4262 | 0.6163 | 0.4574  | 0.1730  | 0.2278   | 0.3721 | 0.6306 | 0.8116 |
  | 0.3723        | 28.0  | 2016 