readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-221116-104421
    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-221116-104421
  
  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: 0.3736
  - Mae: 0.3079
  - Rmse: 0.4321
  - Abs Rel: 0.3666
  - Log Mae: 0.1288
  - Log Rmse: 0.1794
  - Delta1: 0.4929
  - Delta2: 0.7934
  - Delta3: 0.9234
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
  | 1.3644        | 1.0   | 72   | 1.1149          | 2.9299 | 2.9878 | 4.3237  | 0.6329  | 0.6803   | 0.0001 | 0.0241 | 0.1147 |
  | 0.7701        | 2.0   | 144  | 0.5115          | 0.4977 | 0.6435 | 0.6806  | 0.1967  | 0.2543   | 0.3113 | 0.5462 | 0.7732 |
  | 0.4351        | 3.0   | 216  | 0.4129          | 0.3591 | 0.4868 | 0.4587  | 0.1495  | 0.2034   | 0.4214 | 0.7194 | 0.8869 |
  | 0.4001        | 4.0   | 288  | 0.4003          | 0.3421 | 0.4711 | 0.4248  | 0.1418  | 0.1953   | 0.4509 | 0.7446 | 0.8999 |
  | 0.3923        | 5.0   | 360  | 0.3928          | 0.3334 | 0.4573 | 0.4139  | 0.1388  | 0.1906   | 0.4562 | 0.7570 | 0.9098 |
  | 0.363         | 6.0   | 432  | 0.3806          | 0.3176 | 0.4419 | 0.3873  | 0.1328  | 0.1840   | 0.4757 | 0.7786 | 0.9188 |
  | 0.3516        | 7.0   | 504  | 0.3760          | 0.3091 | 0.4346 | 0.3697  | 0.1291  | 0.1804   | 0.4933 | 0.7927 | 0.9224 |
  | 0.303         | 8.0   | 576  | 0.3798          | 0.3131 | 0.4401 | 0.3811  | 0.1307  | 0.1833   | 0.4913 | 0.7886 | 0.9189 |
  | 0.3191        | 9.0   | 648  | 0.3766          | 0.3104 | 0.4356 | 0.3738  | 0.1298  | 0.1811   | 0.4907 | 0.7901 | 0.9214 |
  | 0.3102        | 10.0  | 720  | 0.3736          | 0.3079 | 0.4321 | 0.3666  | 0.1288  | 0.1794   | 0.4929 | 0.7934 | 0.9234 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Tokenizers 0.13.2
  