readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-221221-102136
    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-221221-102136
  
  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: 0.4222
  - Mae: 0.4110
  - Rmse: 0.6292
  - Abs Rel: 0.3778
  - Log Mae: 0.1636
  - Log Rmse: 0.2240
  - Delta1: 0.4320
  - Delta2: 0.6806
  - Delta3: 0.8068
  
  ## 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: 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 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
  | 0.4953        | 1.0   | 72   | 0.4281          | 0.4216 | 0.6448 | 0.3539  | 0.1696  | 0.2312   | 0.4427 | 0.6625 | 0.7765 |
  | 0.3855        | 2.0   | 144  | 0.4749          | 0.4444 | 0.6498 | 0.4156  | 0.1846  | 0.2408   | 0.3612 | 0.6027 | 0.7728 |
  | 0.4158        | 3.0   | 216  | 0.5042          | 0.5122 | 0.7196 | 0.4385  | 0.2264  | 0.2834   | 0.2797 | 0.4837 | 0.6699 |
  | 0.388         | 4.0   | 288  | 0.4418          | 0.4304 | 0.6473 | 0.4030  | 0.1745  | 0.2378   | 0.4027 | 0.6497 | 0.7900 |
  | 0.4595        | 5.0   | 360  | 0.4394          | 0.4154 | 0.6292 | 0.4012  | 0.1664  | 0.2285   | 0.4262 | 0.6613 | 0.8021 |
  | 0.393         | 6.0   | 432  | 0.4252          | 0.4060 | 0.6153 | 0.3944  | 0.1617  | 0.2215   | 0.4318 | 0.6747 | 0.8128 |
  | 0.3468        | 7.0   | 504  | 0.4413          | 0.4366 | 0.6479 | 0.3835  | 0.1818  | 0.2385   | 0.3778 | 0.6248 | 0.7770 |
  | 0.316         | 8.0   | 576  | 0.4218          | 0.4048 | 0.6192 | 0.3844  | 0.1606  | 0.2215   | 0.4374 | 0.6896 | 0.8119 |
  | 0.3123        | 9.0   | 648  | 0.4263          | 0.4168 | 0.6295 | 0.3765  | 0.1689  | 0.2267   | 0.4139 | 0.6612 | 0.7976 |
  | 0.2973        | 10.0  | 720  | 0.4222          | 0.4110 | 0.6292 | 0.3778  | 0.1636  | 0.2240   | 0.4320 | 0.6806 | 0.8068 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  