readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-221221-110911
    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-221221-110911
  
  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: 0.4188
  - Mae: 0.4087
  - Rmse: 0.6260
  - Abs Rel: 0.3672
  - Log Mae: 0.1626
  - Log Rmse: 0.2222
  - Delta1: 0.4391
  - Delta2: 0.6801
  - Delta3: 0.8037
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
  | 0.5004        | 1.0   | 72   | 0.4287          | 0.4162 | 0.6314 | 0.3823  | 0.1665  | 0.2260   | 0.4303 | 0.6746 | 0.7946 |
  | 0.3996        | 2.0   | 144  | 0.4796          | 0.4471 | 0.6517 | 0.4339  | 0.1868  | 0.2450   | 0.3620 | 0.5981 | 0.7637 |
  | 0.4145        | 3.0   | 216  | 0.4401          | 0.4195 | 0.6352 | 0.4073  | 0.1682  | 0.2302   | 0.4202 | 0.6745 | 0.8016 |
  | 0.4064        | 4.0   | 288  | 0.4573          | 0.4302 | 0.6366 | 0.4508  | 0.1742  | 0.2363   | 0.3917 | 0.6483 | 0.7958 |
  | 0.4247        | 5.0   | 360  | 0.4441          | 0.4204 | 0.6227 | 0.4272  | 0.1693  | 0.2276   | 0.3986 | 0.6512 | 0.8034 |
  | 0.4084        | 6.0   | 432  | 0.4326          | 0.4155 | 0.6287 | 0.3913  | 0.1667  | 0.2266   | 0.4183 | 0.6714 | 0.8022 |
  | 0.3569        | 7.0   | 504  | 0.4334          | 0.4189 | 0.6355 | 0.3775  | 0.1697  | 0.2285   | 0.4125 | 0.6622 | 0.7967 |
  | 0.3239        | 8.0   | 576  | 0.4246          | 0.4116 | 0.6259 | 0.3822  | 0.1640  | 0.2237   | 0.4319 | 0.6736 | 0.8043 |
  | 0.32          | 9.0   | 648  | 0.4226          | 0.4171 | 0.6359 | 0.3648  | 0.1670  | 0.2256   | 0.4241 | 0.6677 | 0.7977 |
  | 0.3087        | 10.0  | 720  | 0.4188          | 0.4087 | 0.6260 | 0.3672  | 0.1626  | 0.2222   | 0.4391 | 0.6801 | 0.8037 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  