readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode
    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
  
  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: 0.4359
  - Rmse: 0.4276
  
  ## 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: 2e-05
  - train_batch_size: 24
  - eval_batch_size: 24
  - seed: 42
  - 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 | Rmse   |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | No log        | 1.0   | 13   | 1.2498          | 0.9726 |
  | 0.8469        | 2.0   | 26   | 1.0661          | nan    |
  | 0.8469        | 3.0   | 39   | 0.9959          | 0.5287 |
  | 0.6946        | 4.0   | 52   | 0.8550          | 0.4084 |
  | 0.586         | 5.0   | 65   | 0.7679          | 0.3603 |
  | 0.586         | 6.0   | 78   | 0.6650          | 0.3119 |
  | 0.5195        | 7.0   | 91   | 0.6837          | 0.3370 |
  | 0.4737        | 8.0   | 104  | 0.6638          | 0.3366 |
  | 0.4737        | 9.0   | 117  | 0.6522          | 0.3250 |
  | 0.4663        | 10.0  | 130  | 0.6461          | 0.3153 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Tokenizers 0.13.2
  