readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-230119-100058
    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-230119-100058
  
  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: 0.4305
  - Mae: 0.4203
  - Rmse: 0.6123
  - Abs Rel: 0.4280
  - Log Mae: 0.1694
  - Log Rmse: 0.2214
  - Delta1: 0.3813
  - Delta2: 0.6446
  - Delta3: 0.8152
  
  ## 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: 5e-05
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
  | 1.2807        | 1.0   | 72   | 0.9866          | 0.8312 | 1.0131 | 0.7179  | 0.5655  | 0.5924   | 0.0087 | 0.0200 | 0.0552 |
  | 0.7396        | 2.0   | 144  | 0.4976          | 0.4741 | 0.6670 | 0.5279  | 0.1989  | 0.2567   | 0.3070 | 0.5470 | 0.7943 |
  | 0.5018        | 3.0   | 216  | 0.4811          | 0.4630 | 0.6367 | 0.5198  | 0.1929  | 0.2446   | 0.3211 | 0.5440 | 0.7506 |
  | 0.482         | 4.0   | 288  | 0.4726          | 0.4556 | 0.6337 | 0.4951  | 0.1893  | 0.2410   | 0.3306 | 0.5636 | 0.7663 |
  | 0.4874        | 5.0   | 360  | 0.4813          | 0.4662 | 0.6355 | 0.5265  | 0.1941  | 0.2446   | 0.3179 | 0.5385 | 0.7278 |
  | 0.4648        | 6.0   | 432  | 0.4681          | 0.4512 | 0.6309 | 0.4783  | 0.1869  | 0.2383   | 0.3430 | 0.5757 | 0.7527 |
  | 0.4346        | 7.0   | 504  | 0.4637          | 0.4499 | 0.6292 | 0.4710  | 0.1859  | 0.2357   | 0.3453 | 0.5671 | 0.7644 |
  | 0.4018        | 8.0   | 576  | 0.4790          | 0.4638 | 0.6349 | 0.5161  | 0.1928  | 0.2436   | 0.3255 | 0.5408 | 0.7338 |
  | 0.4092        | 9.0   | 648  | 0.4559          | 0.4449 | 0.6267 | 0.4540  | 0.1827  | 0.2319   | 0.3541 | 0.5814 | 0.7692 |
  | 0.3891        | 10.0  | 720  | 0.4619          | 0.4433 | 0.6259 | 0.4748  | 0.1823  | 0.2351   | 0.3579 | 0.5870 | 0.7742 |
  | 0.3707        | 11.0  | 792  | 0.4624          | 0.4500 | 0.6269 | 0.4828  | 0.1851  | 0.2350   | 0.3421 | 0.5672 | 0.7638 |
  | 0.4129        | 12.0  | 864  | 0.4648          | 0.4468 | 0.6265 | 0.4836  | 0.1836  | 0.2358   | 0.3533 | 0.5786 | 0.7625 |
  | 0.4108        | 13.0  | 936  | 0.4474          | 0.4312 | 0.6187 | 0.4501  | 0.1752  | 0.2280   | 0.3801 | 0.6088 | 0.7887 |
  | 0.3948        | 14.0  | 1008 | 0.4619          | 0.4498 | 0.6263 | 0.4853  | 0.1844  | 0.2344   | 0.3401 | 0.5721 | 0.7645 |
  | 0.4009        | 15.0  | 1080 | 0.4619          | 0.4440 | 0.6244 | 0.4889  | 0.1820  | 0.2351   | 0.3563 | 0.5841 | 0.7751 |
  | 0.3657        | 16.0  | 1152 | 0.4636          | 0.4491 | 0.6260 | 0.4936  | 0.1846  | 0.2360   | 0.3422 | 0.5734 | 0.7644 |
  | 0.3605        | 17.0  | 1224 | 0.4353          | 0.4255 | 0.6153 | 0.4248  | 0.1715  | 0.2218   | 0.3844 | 0.6207 | 0.8008 |
  | 0.3937        | 18.0  | 1296 | 0.4756          | 0.4609 | 0.6310 | 0.5281  | 0.1909  | 0.2423   | 0.3220 | 0.5461 | 0.7538 |
  | 0.3453        | 19.0  | 1368 | 0.4698          | 0.4517 | 0.6270 | 0.5145  | 0.1863  | 0.2392   | 0.3360 | 0.5702 | 0.7689 |
  | 0.3883        | 20.0  | 1440 | 0.4349          | 0.4240 | 0.6145 | 0.4311  | 0.1712  | 0.2230   | 0.3841 | 0.6321 | 0.8030 |
  | 0.3482        | 21.0  | 1512 | 0.4339          | 0.4209 | 0.6146 | 0.4223  | 0.1694  | 0.2223   | 0.3967 | 0.6337 | 0.8036 |
  | 0.3374        | 22.0  | 1584 | 0.4400          | 0.4289 | 0.6167 | 0.4431  | 0.1737  | 0.2254   | 0.3743 | 0.6191 | 0.7971 |
  | 0.3516        | 23.0  | 1656 | 0.4395          | 0.4280 | 0.6171 | 0.4426  | 0.1737  | 0.2259   | 0.3710 | 0.6241 | 0.7998 |
  | 0.3901        | 24.0  | 1728 | 0.4444          | 0.4324 | 0.6184 | 0.4562  | 0.1758  | 0.2280   | 0.3665 | 0.6118 | 0.7991 |
  | 0.3587        | 25.0  | 1800 | 0.4326          | 0.4200 | 0.6129 | 0.4281  | 0.1690  | 0.2222   | 0.3920 | 0.6403 | 0.8073 |
  | 0.3425        | 26.0  | 1872 | 0.4371          | 0.4231 | 0.6152 | 0.4341  | 0.1709  | 0.2242   | 0.3852 | 0.6372 | 0.7974 |
  | 0.3252        | 27.0  | 1944 | 0.4381          | 0.4225 | 0.6140 | 0.4399  | 0.1705  | 0.2245   | 0.3851 | 0.6396 | 0.8065 |
  | 0.3586        | 28.0  | 2016 | 