readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-230103-091356
    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-230103-091356
  
  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: 0.4360
  - Mae: 0.4251
  - Rmse: 0.6169
  - Abs Rel: 0.4500
  - Log Mae: 0.1721
  - Log Rmse: 0.2269
  - Delta1: 0.3828
  - Delta2: 0.6326
  - Delta3: 0.8051
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
  | 1.0762        | 1.0   | 72   | 0.5031          | 0.4779 | 0.6690 | 0.5503  | 0.2006  | 0.2591   | 0.3020 | 0.5337 | 0.8000 |
  | 0.478         | 2.0   | 144  | 0.4653          | 0.4509 | 0.6307 | 0.4891  | 0.1861  | 0.2377   | 0.3300 | 0.5805 | 0.7734 |
  | 0.4668        | 3.0   | 216  | 0.4845          | 0.4712 | 0.6373 | 0.5469  | 0.1963  | 0.2471   | 0.3110 | 0.5254 | 0.7235 |
  | 0.4389        | 4.0   | 288  | 0.4587          | 0.4368 | 0.6219 | 0.4887  | 0.1787  | 0.2344   | 0.3578 | 0.6099 | 0.7926 |
  | 0.4626        | 5.0   | 360  | 0.4879          | 0.4662 | 0.6351 | 0.5617  | 0.1937  | 0.2482   | 0.3135 | 0.5462 | 0.7395 |
  | 0.4534        | 6.0   | 432  | 0.4638          | 0.4422 | 0.6236 | 0.4951  | 0.1810  | 0.2358   | 0.3606 | 0.5844 | 0.7831 |
  | 0.4108        | 7.0   | 504  | 0.4688          | 0.4508 | 0.6279 | 0.5050  | 0.1856  | 0.2385   | 0.3426 | 0.5701 | 0.7623 |
  | 0.3832        | 8.0   | 576  | 0.4759          | 0.4533 | 0.6284 | 0.5257  | 0.1869  | 0.2411   | 0.3331 | 0.5701 | 0.7617 |
  | 0.4097        | 9.0   | 648  | 0.4771          | 0.4501 | 0.6303 | 0.5361  | 0.1855  | 0.2433   | 0.3454 | 0.5838 | 0.7609 |
  | 0.3799        | 10.0  | 720  | 0.4575          | 0.4375 | 0.6240 | 0.4874  | 0.1790  | 0.2349   | 0.3669 | 0.6032 | 0.7916 |
  | 0.3659        | 11.0  | 792  | 0.4718          | 0.4590 | 0.6298 | 0.5176  | 0.1893  | 0.2396   | 0.3283 | 0.5502 | 0.7368 |
  | 0.4145        | 12.0  | 864  | 0.4776          | 0.4561 | 0.6298 | 0.5325  | 0.1883  | 0.2421   | 0.3333 | 0.5611 | 0.7540 |
  | 0.4224        | 13.0  | 936  | 0.4320          | 0.4138 | 0.6202 | 0.4013  | 0.1655  | 0.2232   | 0.4217 | 0.6641 | 0.8004 |
  | 0.4142        | 14.0  | 1008 | 0.4597          | 0.4440 | 0.6234 | 0.4842  | 0.1813  | 0.2330   | 0.3520 | 0.5895 | 0.7617 |
  | 0.4393        | 15.0  | 1080 | 0.4333          | 0.4251 | 0.6197 | 0.4182  | 0.1712  | 0.2225   | 0.3787 | 0.6303 | 0.8100 |
  | 0.4045        | 16.0  | 1152 | 0.4603          | 0.4356 | 0.6197 | 0.4819  | 0.1776  | 0.2322   | 0.3635 | 0.6050 | 0.7858 |
  | 0.3708        | 17.0  | 1224 | 0.4738          | 0.4567 | 0.6292 | 0.5264  | 0.1886  | 0.2411   | 0.3283 | 0.5557 | 0.7596 |
  | 0.4042        | 18.0  | 1296 | 0.5004          | 0.4802 | 0.6423 | 0.6101  | 0.2008  | 0.2560   | 0.3022 | 0.5165 | 0.6931 |
  | 0.3763        | 19.0  | 1368 | 0.4501          | 0.4361 | 0.6213 | 0.4723  | 0.1772  | 0.2303   | 0.3634 | 0.6034 | 0.7889 |
  | 0.4084        | 20.0  | 1440 | 0.4272          | 0.4133 | 0.6208 | 0.3958  | 0.1649  | 0.2226   | 0.4284 | 0.6684 | 0.8009 |
  | 0.3637        | 21.0  | 1512 | 0.4307          | 0.4145 | 0.6199 | 0.4134  | 0.1665  | 0.2241   | 0.3957 | 0.6847 | 0.8137 |
  | 0.3655        | 22.0  | 1584 | 0.4591          | 0.4374 | 0.6370 | 0.4594  | 0.1791  | 0.2384   | 0.3816 | 0.6264 | 0.7826 |
  | 0.3844        | 23.0  | 1656 | 0.4692          | 0.4444 | 0.6273 | 0.5241  | 0.1824  | 0.2407   | 0.3540 | 0.5990 | 0.7756 |
  | 0.428         | 24.0  | 1728 | 0.4982          | 0.4753 | 0.6403 | 0.6084  | 0.1984  | 0.2552   | 0.3099 | 0.5233 | 0.7204 |
  | 0.4051        | 25.0  | 1800 | 0.4824          | 0.4618 | 0.6329 | 0.5533  | 0.1915  | 0.2461   | 0.3248 | 0.5495 | 0.7415 |
  | 0.3584        | 26.0  | 1872 | 0.4434          | 0.4207 | 0.6177 | 0.4468  | 0.1694  | 0.2277   | 0.3975 | 0.6442 | 0.8038 |
  | 0.3443        | 27.0  | 1944 | 0.4602          | 0.4434 | 0.6241 | 0.4912  | 0.1822  | 0.2351   | 0.3431 | 0.5877 | 0.7893 |
  | 0.3714        | 28.0  | 2016 