readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-221122-030603
    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-221122-030603
  
  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: 0.3597
  - Mae: 0.3054
  - Rmse: 0.4481
  - Abs Rel: 0.3462
  - Log Mae: 0.1256
  - Log Rmse: 0.1798
  - Delta1: 0.5278
  - Delta2: 0.8055
  - Delta3: 0.9191
  
  ## 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: 5e-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: 0.2
  - num_epochs: 15
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Mae    | Rmse   | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
  | 1.3722        | 1.0   | 72   | 0.9950          | 2.2271 | 2.3100 | 3.3726  | 0.5488  | 0.6025   | 0.0176 | 0.0929 | 0.1687 |
  | 0.4936        | 2.0   | 144  | 0.4493          | 0.3961 | 0.5488 | 0.4918  | 0.1648  | 0.2234   | 0.3827 | 0.6877 | 0.8553 |
  | 0.4087        | 3.0   | 216  | 0.3971          | 0.3434 | 0.4737 | 0.4250  | 0.1437  | 0.1980   | 0.4393 | 0.7431 | 0.8979 |
  | 0.3696        | 4.0   | 288  | 0.3895          | 0.3355 | 0.4652 | 0.4334  | 0.1405  | 0.1968   | 0.4412 | 0.7566 | 0.9174 |
  | 0.3621        | 5.0   | 360  | 0.3926          | 0.3432 | 0.4766 | 0.4198  | 0.1416  | 0.1967   | 0.4372 | 0.7591 | 0.9095 |
  | 0.3104        | 6.0   | 432  | 0.3615          | 0.3104 | 0.4447 | 0.3630  | 0.1281  | 0.1813   | 0.5032 | 0.7923 | 0.9237 |
  | 0.2835        | 7.0   | 504  | 0.3744          | 0.3140 | 0.4506 | 0.3429  | 0.1326  | 0.1824   | 0.4801 | 0.7837 | 0.9135 |
  | 0.2308        | 8.0   | 576  | 0.3615          | 0.3013 | 0.4458 | 0.3459  | 0.1243  | 0.1803   | 0.5334 | 0.8139 | 0.9173 |
  | 0.2433        | 9.0   | 648  | 0.3609          | 0.3042 | 0.4374 | 0.3570  | 0.1258  | 0.1798   | 0.5223 | 0.8053 | 0.9229 |
  | 0.2097        | 10.0  | 720  | 0.3628          | 0.3066 | 0.4312 | 0.3792  | 0.1276  | 0.1815   | 0.4973 | 0.8075 | 0.9254 |
  | 0.1761        | 11.0  | 792  | 0.3637          | 0.3111 | 0.4413 | 0.3726  | 0.1286  | 0.1817   | 0.4982 | 0.7979 | 0.9223 |
  | 0.22          | 12.0  | 864  | 0.3584          | 0.3077 | 0.4619 | 0.3345  | 0.1258  | 0.1808   | 0.5360 | 0.8007 | 0.9119 |
  | 0.2087        | 13.0  | 936  | 0.3614          | 0.3078 | 0.4502 | 0.3513  | 0.1265  | 0.1808   | 0.5211 | 0.8055 | 0.9177 |
  | 0.2036        | 14.0  | 1008 | 0.3601          | 0.3086 | 0.4486 | 0.3530  | 0.1267  | 0.1801   | 0.5184 | 0.8023 | 0.9189 |
  | 0.2123        | 15.0  | 1080 | 0.3597          | 0.3054 | 0.4481 | 0.3462  | 0.1256  | 0.1798   | 0.5278 | 0.8055 | 0.9191 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu116
  - Tokenizers 0.13.2
  