readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-230118-181403
    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-230118-181403
  
  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: 0.4408
  - Mae: 0.4277
  - Rmse: 0.6172
  - Abs Rel: 0.4520
  - Log Mae: 0.1735
  - Log Rmse: 0.2270
  - Delta1: 0.3788
  - Delta2: 0.6217
  - Delta3: 0.8047
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
  | 1.0764        | 1.0   | 72   | 0.5030          | 0.4778 | 0.6687 | 0.5507  | 0.2006  | 0.2591   | 0.3021 | 0.5334 | 0.8002 |
  | 0.4775        | 2.0   | 144  | 0.4662          | 0.4525 | 0.6313 | 0.4929  | 0.1869  | 0.2383   | 0.3276 | 0.5766 | 0.7713 |
  | 0.4668        | 3.0   | 216  | 0.4841          | 0.4709 | 0.6371 | 0.5461  | 0.1961  | 0.2470   | 0.3112 | 0.5262 | 0.7238 |
  | 0.4391        | 4.0   | 288  | 0.4571          | 0.4354 | 0.6215 | 0.4835  | 0.1779  | 0.2335   | 0.3605 | 0.6136 | 0.7948 |
  | 0.4621        | 5.0   | 360  | 0.4870          | 0.4658 | 0.6352 | 0.5593  | 0.1935  | 0.2480   | 0.3153 | 0.5462 | 0.7390 |
  | 0.4577        | 6.0   | 432  | 0.4661          | 0.4411 | 0.6238 | 0.5039  | 0.1805  | 0.2371   | 0.3654 | 0.5885 | 0.7847 |
  | 0.4206        | 7.0   | 504  | 0.4889          | 0.4660 | 0.6351 | 0.5636  | 0.1938  | 0.2484   | 0.3212 | 0.5392 | 0.7310 |
  | 0.3938        | 8.0   | 576  | 0.4856          | 0.4638 | 0.6369 | 0.5443  | 0.1923  | 0.2474   | 0.3293 | 0.5515 | 0.7313 |
  | 0.4073        | 9.0   | 648  | 0.4733          | 0.4538 | 0.6312 | 0.5211  | 0.1867  | 0.2412   | 0.3469 | 0.5653 | 0.7483 |
  | 0.399         | 10.0  | 720  | 0.4862          | 0.4620 | 0.6345 | 0.5579  | 0.1918  | 0.2476   | 0.3277 | 0.5530 | 0.7396 |
  | 0.3621        | 11.0  | 792  | 0.4523          | 0.4307 | 0.6178 | 0.4724  | 0.1749  | 0.2298   | 0.3720 | 0.6131 | 0.8001 |
  | 0.4208        | 12.0  | 864  | 0.4790          | 0.4524 | 0.6294 | 0.5339  | 0.1860  | 0.2420   | 0.3503 | 0.5648 | 0.7454 |
  | 0.4365        | 13.0  | 936  | 0.4450          | 0.4284 | 0.6175 | 0.4516  | 0.1741  | 0.2269   | 0.3673 | 0.6182 | 0.8091 |
  | 0.4192        | 14.0  | 1008 | 0.4581          | 0.4357 | 0.6214 | 0.4790  | 0.1766  | 0.2316   | 0.3697 | 0.6067 | 0.7763 |
  | 0.4172        | 15.0  | 1080 | 0.4481          | 0.4286 | 0.6276 | 0.4498  | 0.1732  | 0.2309   | 0.3898 | 0.6298 | 0.7964 |
  | 0.3825        | 16.0  | 1152 | 0.4873          | 0.4649 | 0.6342 | 0.5704  | 0.1929  | 0.2482   | 0.3184 | 0.5443 | 0.7413 |
  | 0.4006        | 17.0  | 1224 | 0.4752          | 0.4545 | 0.6298 | 0.5398  | 0.1871  | 0.2423   | 0.3383 | 0.5683 | 0.7496 |
  | 0.4171        | 18.0  | 1296 | 0.4979          | 0.4742 | 0.6410 | 0.5991  | 0.1974  | 0.2544   | 0.3148 | 0.5296 | 0.7145 |
  | 0.366         | 19.0  | 1368 | 0.4919          | 0.4659 | 0.6381 | 0.5903  | 0.1936  | 0.2519   | 0.3212 | 0.5474 | 0.7497 |
  | 0.4029        | 20.0  | 1440 | 0.4403          | 0.4257 | 0.6208 | 0.4245  | 0.1712  | 0.2248   | 0.3915 | 0.6344 | 0.8015 |
  | 0.3569        | 21.0  | 1512 | 0.4423          | 0.4251 | 0.6257 | 0.4244  | 0.1709  | 0.2281   | 0.4043 | 0.6405 | 0.7928 |
  | 0.3614        | 22.0  | 1584 | 0.4645          | 0.4402 | 0.6251 | 0.5081  | 0.1800  | 0.2378   | 0.3631 | 0.6089 | 0.7816 |
  | 0.3875        | 23.0  | 1656 | 0.4603          | 0.4384 | 0.6268 | 0.4957  | 0.1789  | 0.2369   | 0.3665 | 0.6138 | 0.7876 |
  | 0.4125        | 24.0  | 1728 | 0.4857          | 0.4628 | 0.6341 | 0.5720  | 0.1918  | 0.2484   | 0.3239 | 0.5471 | 0.7591 |
  | 0.3937        | 25.0  | 1800 | 0.4785          | 0.4555 | 0.6300 | 0.5504  | 0.1882  | 0.2446   | 0.3310 | 0.5617 | 0.7685 |
  | 0.3677        | 26.0  | 1872 | 0.4370          | 0.4225 | 0.6156 | 0.4300  | 0.1702  | 0.2237   | 0.3872 | 0.6348 | 0.8056 |
  | 0.3467        | 27.0  | 1944 | 0.4550          | 0.4321 | 0.6193 | 0.4884  | 0.1758  | 0.2330   | 0.3697 | 0.6155 | 0.8055 |
  | 0.3769        | 28.0  | 2016 