readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-230124-035129
    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-230124-035129
  
  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: 0.4346
  - Mae: 0.4251
  - Rmse: 0.6137
  - Abs Rel: 0.4412
  - Log Mae: 0.1720
  - Log Rmse: 0.2234
  - Delta1: 0.3728
  - Delta2: 0.6287
  - Delta3: 0.8112
  
  ## 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: 0.0003
  - 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: 100
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Mae    | Rmse   | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
  | 1.0761        | 1.0   | 72   | 0.5029          | 0.4778 | 0.6689 | 0.5501  | 0.2005  | 0.2591   | 0.3026 | 0.5337 | 0.8000 |
  | 0.4775        | 2.0   | 144  | 0.4651          | 0.4519 | 0.6313 | 0.4910  | 0.1865  | 0.2380   | 0.3286 | 0.5789 | 0.7731 |
  | 0.4669        | 3.0   | 216  | 0.4843          | 0.4710 | 0.6374 | 0.5449  | 0.1960  | 0.2468   | 0.3114 | 0.5264 | 0.7239 |
  | 0.4386        | 4.0   | 288  | 0.4544          | 0.4342 | 0.6217 | 0.4763  | 0.1771  | 0.2324   | 0.3629 | 0.6167 | 0.7950 |
  | 0.4625        | 5.0   | 360  | 0.4854          | 0.4649 | 0.6352 | 0.5522  | 0.1930  | 0.2471   | 0.3184 | 0.5485 | 0.7388 |
  | 0.4519        | 6.0   | 432  | 0.4507          | 0.4284 | 0.6178 | 0.4597  | 0.1735  | 0.2288   | 0.3860 | 0.6189 | 0.7960 |
  | 0.4145        | 7.0   | 504  | 0.4682          | 0.4523 | 0.6294 | 0.4984  | 0.1860  | 0.2374   | 0.3391 | 0.5647 | 0.7637 |
  | 0.3833        | 8.0   | 576  | 0.4391          | 0.4221 | 0.6350 | 0.4043  | 0.1700  | 0.2297   | 0.4133 | 0.6641 | 0.7936 |
  | 0.3944        | 9.0   | 648  | 0.4482          | 0.4346 | 0.6323 | 0.4269  | 0.1766  | 0.2314   | 0.3877 | 0.6193 | 0.7755 |
  | 0.3819        | 10.0  | 720  | 0.4483          | 0.4301 | 0.6208 | 0.4636  | 0.1748  | 0.2293   | 0.3600 | 0.6339 | 0.8145 |
  | 0.3642        | 11.0  | 792  | 0.4370          | 0.4224 | 0.6170 | 0.4214  | 0.1701  | 0.2226   | 0.3825 | 0.6312 | 0.8171 |
  | 0.4094        | 12.0  | 864  | 0.4779          | 0.4589 | 0.6330 | 0.5249  | 0.1895  | 0.2422   | 0.3379 | 0.5536 | 0.7308 |
  | 0.4376        | 13.0  | 936  | 0.4539          | 0.4334 | 0.6202 | 0.4624  | 0.1762  | 0.2291   | 0.3655 | 0.6114 | 0.7897 |
  | 0.4048        | 14.0  | 1008 | 0.4728          | 0.4522 | 0.6275 | 0.5082  | 0.1854  | 0.2376   | 0.3485 | 0.5644 | 0.7391 |
  | 0.4478        | 15.0  | 1080 | 0.4573          | 0.4336 | 0.6215 | 0.4590  | 0.1761  | 0.2301   | 0.3697 | 0.6090 | 0.7884 |
  | 0.4059        | 16.0  | 1152 | 0.4721          | 0.4424 | 0.6251 | 0.5171  | 0.1815  | 0.2391   | 0.3541 | 0.5930 | 0.7832 |
  | 0.3803        | 17.0  | 1224 | 0.4518          | 0.4242 | 0.6216 | 0.4496  | 0.1709  | 0.2294   | 0.3963 | 0.6389 | 0.8061 |
  | 0.4208        | 18.0  | 1296 | 0.4755          | 0.4583 | 0.6307 | 0.5242  | 0.1893  | 0.2417   | 0.3320 | 0.5575 | 0.7438 |
  | 0.3695        | 19.0  | 1368 | 0.4758          | 0.4462 | 0.6272 | 0.5255  | 0.1836  | 0.2413   | 0.3534 | 0.5852 | 0.7696 |
  | 0.4121        | 20.0  | 1440 | 0.4353          | 0.4227 | 0.6271 | 0.4017  | 0.1703  | 0.2245   | 0.3893 | 0.6499 | 0.8077 |
  | 0.3739        | 21.0  | 1512 | 0.4378          | 0.4156 | 0.6159 | 0.4169  | 0.1666  | 0.2233   | 0.4172 | 0.6515 | 0.7963 |
  | 0.3729        | 22.0  | 1584 | 0.4375          | 0.4218 | 0.6158 | 0.4229  | 0.1696  | 0.2222   | 0.3906 | 0.6356 | 0.8061 |
  | 0.3921        | 23.0  | 1656 | 0.4353          | 0.4167 | 0.6141 | 0.4192  | 0.1670  | 0.2218   | 0.4059 | 0.6504 | 0.8023 |
  | 0.4527        | 24.0  | 1728 | 0.4850          | 0.4528 | 0.6307 | 0.5521  | 0.1873  | 0.2459   | 0.3459 | 0.5707 | 0.7571 |
  | 0.4064        | 25.0  | 1800 | 0.4446          | 0.4276 | 0.6172 | 0.4488  | 0.1731  | 0.2263   | 0.3764 | 0.6246 | 0.8056 |
  | 0.3827        | 26.0  | 1872 | 0.4476          | 0.4299 | 0.6199 | 0.4586  | 0.1746  | 0.2293   | 0.3694 | 0.6303 | 0.7972 |
  | 0.3416        | 27.0  | 1944 | 0.4428          | 0.4298 | 0.6228 | 0.4408  | 0.1743  | 0.2272   | 0.3724 | 0.6274 | 0.8103 |
  | 0.3774        | 28.0  | 2016 