readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-221116-110652
    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-221116-110652
  
  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: 0.4018
  - Mae: 0.3272
  - Rmse: 0.4546
  - Abs Rel: 0.3934
  - Log Mae: 0.1380
  - Log Rmse: 0.1907
  - Delta1: 0.4598
  - Delta2: 0.7659
  - Delta3: 0.9082
  
  ## 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: 1e-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: 0.1
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Mae    | Rmse   | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
  | 1.3984        | 1.0   | 72   | 1.1606          | 3.2154 | 3.2710 | 4.6927  | 0.6627  | 0.7082   | 0.0    | 0.0053 | 0.0893 |
  | 0.8305        | 2.0   | 144  | 0.5445          | 0.6035 | 0.8404 | 0.8013  | 0.2102  | 0.2726   | 0.2747 | 0.5358 | 0.7609 |
  | 0.4601        | 3.0   | 216  | 0.4484          | 0.4041 | 0.5376 | 0.5417  | 0.1617  | 0.2188   | 0.3771 | 0.6932 | 0.8692 |
  | 0.4211        | 4.0   | 288  | 0.4251          | 0.3634 | 0.4914 | 0.4800  | 0.1499  | 0.2069   | 0.4136 | 0.7270 | 0.8931 |
  | 0.4162        | 5.0   | 360  | 0.4170          | 0.3537 | 0.4833 | 0.4483  | 0.1455  | 0.2005   | 0.4303 | 0.7444 | 0.8992 |
  | 0.3776        | 6.0   | 432  | 0.4115          | 0.3491 | 0.4692 | 0.4558  | 0.1449  | 0.1999   | 0.4281 | 0.7471 | 0.9018 |
  | 0.3729        | 7.0   | 504  | 0.4058          | 0.3337 | 0.4590 | 0.4135  | 0.1396  | 0.1935   | 0.4517 | 0.7652 | 0.9072 |
  | 0.3235        | 8.0   | 576  | 0.4035          | 0.3304 | 0.4602 | 0.4043  | 0.1383  | 0.1929   | 0.4613 | 0.7679 | 0.9073 |
  | 0.3382        | 9.0   | 648  | 0.3990          | 0.3254 | 0.4546 | 0.3937  | 0.1365  | 0.1900   | 0.4671 | 0.7717 | 0.9102 |
  | 0.3265        | 10.0  | 720  | 0.4018          | 0.3272 | 0.4546 | 0.3934  | 0.1380  | 0.1907   | 0.4598 | 0.7659 | 0.9082 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu116
  - Tokenizers 0.13.2
  