readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-230117-091155
    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-230117-091155
  
  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: 0.4508
  - Mae: 0.4313
  - Rmse: 0.6190
  - Abs Rel: 0.4812
  - Log Mae: 0.1758
  - Log Rmse: 0.2320
  - Delta1: 0.3634
  - Delta2: 0.6241
  - Delta3: 0.8095
  
  ## 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: 0.0005
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
  | 0.9462        | 1.0   | 72   | 0.4914          | 0.4683 | 0.6367 | 0.5686  | 0.1952  | 0.2498   | 0.3207 | 0.5284 | 0.7432 |
  | 0.4614        | 2.0   | 144  | 0.4642          | 0.4481 | 0.6276 | 0.4985  | 0.1851  | 0.2379   | 0.3310 | 0.5818 | 0.7870 |
  | 0.4575        | 3.0   | 216  | 0.4863          | 0.4690 | 0.6352 | 0.5562  | 0.1952  | 0.2474   | 0.3124 | 0.5296 | 0.7341 |
  | 0.4333        | 4.0   | 288  | 0.4578          | 0.4350 | 0.6234 | 0.4770  | 0.1772  | 0.2337   | 0.3717 | 0.6145 | 0.7898 |
  | 0.461         | 5.0   | 360  | 0.4652          | 0.4417 | 0.6251 | 0.4889  | 0.1802  | 0.2351   | 0.3640 | 0.5982 | 0.7674 |
  | 0.4556        | 6.0   | 432  | 0.4488          | 0.4268 | 0.6176 | 0.4490  | 0.1727  | 0.2272   | 0.3840 | 0.6282 | 0.7923 |
  | 0.4173        | 7.0   | 504  | 0.4583          | 0.4381 | 0.6261 | 0.4704  | 0.1788  | 0.2339   | 0.3806 | 0.5943 | 0.7748 |
  | 0.407         | 8.0   | 576  | 0.4761          | 0.4597 | 0.6332 | 0.5354  | 0.1911  | 0.2444   | 0.3238 | 0.5456 | 0.7837 |
  | 0.4414        | 9.0   | 648  | 0.4753          | 0.4528 | 0.6281 | 0.5253  | 0.1866  | 0.2408   | 0.3403 | 0.5715 | 0.7462 |
  | 0.4027        | 10.0  | 720  | 0.4822          | 0.4629 | 0.6333 | 0.5371  | 0.1914  | 0.2438   | 0.3318 | 0.5426 | 0.7352 |
  | 0.391         | 11.0  | 792  | 0.4488          | 0.4376 | 0.6440 | 0.4148  | 0.1784  | 0.2341   | 0.3712 | 0.6422 | 0.7924 |
  | 0.4323        | 12.0  | 864  | 0.4845          | 0.4653 | 0.6343 | 0.5571  | 0.1937  | 0.2474   | 0.3171 | 0.5352 | 0.7550 |
  | 0.4564        | 13.0  | 936  | 0.4757          | 0.4606 | 0.6315 | 0.5164  | 0.1901  | 0.2401   | 0.3285 | 0.5452 | 0.7361 |
  | 0.4551        | 14.0  | 1008 | 0.4518          | 0.4354 | 0.6210 | 0.4406  | 0.1770  | 0.2264   | 0.3644 | 0.6064 | 0.7760 |
  | 0.4634        | 15.0  | 1080 | 0.4542          | 0.4306 | 0.6205 | 0.4378  | 0.1756  | 0.2280   | 0.3770 | 0.6093 | 0.7818 |
  | 0.4395        | 16.0  | 1152 | 0.4744          | 0.4438 | 0.6266 | 0.5195  | 0.1828  | 0.2405   | 0.3520 | 0.5851 | 0.7937 |
  | 0.4018        | 17.0  | 1224 | 0.4872          | 0.4567 | 0.6323 | 0.5525  | 0.1888  | 0.2461   | 0.3405 | 0.5610 | 0.7444 |
  | 0.4684        | 18.0  | 1296 | 0.4979          | 0.4736 | 0.6403 | 0.5876  | 0.1977  | 0.2533   | 0.3116 | 0.5289 | 0.7149 |
  | 0.408         | 19.0  | 1368 | 0.4995          | 0.4795 | 0.6416 | 0.5995  | 0.2004  | 0.2546   | 0.3013 | 0.5164 | 0.6961 |
  | 0.4372        | 20.0  | 1440 | 0.4580          | 0.4346 | 0.6415 | 0.4046  | 0.1794  | 0.2366   | 0.3786 | 0.6417 | 0.7788 |
  | 0.4104        | 21.0  | 1512 | 0.4781          | 0.4531 | 0.6293 | 0.5330  | 0.1868  | 0.2426   | 0.3454 | 0.5700 | 0.7470 |
  | 0.3843        | 22.0  | 1584 | 0.4487          | 0.4322 | 0.6203 | 0.4518  | 0.1755  | 0.2280   | 0.3711 | 0.6160 | 0.7828 |
  | 0.4025        | 23.0  | 1656 | 0.4659          | 0.4448 | 0.6341 | 0.4721  | 0.1824  | 0.2376   | 0.3563 | 0.6061 | 0.7633 |
  | 0.4553        | 24.0  | 1728 | 0.4766          | 0.4557 | 0.6301 | 0.5319  | 0.1890  | 0.2429   | 0.3306 | 0.5491 | 0.7753 |
  | 0.4134        | 25.0  | 1800 | 0.4779          | 0.4530 | 0.6289 | 0.5299  | 0.1871  | 0.2424   | 0.3416 | 0.5645 | 0.7550 |
  | 0.3913        | 26.0  | 1872 | 0.4591          | 0.4392 | 0.6292 | 0.4691  | 0.1799  | 0.2351   | 0.3610 | 0.6102 | 0.7819 |
  | 0.373         | 27.0  | 1944 | 0.4634          | 0.4330 | 0.6313 | 0.4694  | 0.1767  | 0.2376   | 0.3885 | 0.6201 | 0.7928 |
  | 0.4079        | 28.0  | 2016 