readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-230115-063851
    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-230115-063851
  
  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: 0.4360
  - Mae: 0.4211
  - Rmse: 0.6143
  - Abs Rel: 0.4394
  - Log Mae: 0.1700
  - Log Rmse: 0.2243
  - Delta1: 0.3835
  - Delta2: 0.6419
  - Delta3: 0.8181
  
  ## 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: 75
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Mae    | Rmse   | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
  | 1.0074        | 1.0   | 72   | 0.4929          | 0.4684 | 0.6424 | 0.5682  | 0.1955  | 0.2515   | 0.3151 | 0.5288 | 0.7834 |
  | 0.4702        | 2.0   | 144  | 0.4561          | 0.4431 | 0.6292 | 0.4667  | 0.1822  | 0.2344   | 0.3377 | 0.6052 | 0.7878 |
  | 0.4618        | 3.0   | 216  | 0.4827          | 0.4664 | 0.6351 | 0.5445  | 0.1939  | 0.2461   | 0.3163 | 0.5336 | 0.7408 |
  | 0.4388        | 4.0   | 288  | 0.4669          | 0.4450 | 0.6251 | 0.5068  | 0.1831  | 0.2379   | 0.3465 | 0.5870 | 0.7811 |
  | 0.463         | 5.0   | 360  | 0.4960          | 0.4715 | 0.6382 | 0.5942  | 0.1963  | 0.2528   | 0.3115 | 0.5361 | 0.7222 |
  | 0.4478        | 6.0   | 432  | 0.4808          | 0.4542 | 0.6301 | 0.5439  | 0.1872  | 0.2440   | 0.3452 | 0.5647 | 0.7519 |
  | 0.42          | 7.0   | 504  | 0.4645          | 0.4445 | 0.6268 | 0.4876  | 0.1822  | 0.2361   | 0.3578 | 0.5889 | 0.7741 |
  | 0.3977        | 8.0   | 576  | 0.4767          | 0.4503 | 0.6299 | 0.5162  | 0.1857  | 0.2410   | 0.3452 | 0.5705 | 0.7726 |
  | 0.4045        | 9.0   | 648  | 0.4747          | 0.4568 | 0.6303 | 0.5208  | 0.1885  | 0.2406   | 0.3323 | 0.5572 | 0.7497 |
  | 0.392         | 10.0  | 720  | 0.4860          | 0.4571 | 0.6331 | 0.5645  | 0.1889  | 0.2475   | 0.3370 | 0.5627 | 0.7589 |
  | 0.3749        | 11.0  | 792  | 0.4785          | 0.4502 | 0.6308 | 0.5449  | 0.1860  | 0.2446   | 0.3423 | 0.5719 | 0.7940 |
  | 0.4292        | 12.0  | 864  | 0.4905          | 0.4574 | 0.6346 | 0.5616  | 0.1891  | 0.2483   | 0.3402 | 0.5694 | 0.7499 |
  | 0.432         | 13.0  | 936  | 0.4648          | 0.4408 | 0.6229 | 0.4877  | 0.1804  | 0.2345   | 0.3607 | 0.5947 | 0.7771 |
  | 0.4097        | 14.0  | 1008 | 0.4464          | 0.4303 | 0.6221 | 0.4398  | 0.1742  | 0.2285   | 0.3879 | 0.6179 | 0.7821 |
  | 0.4212        | 15.0  | 1080 | 0.4773          | 0.4550 | 0.6298 | 0.5327  | 0.1874  | 0.2425   | 0.3375 | 0.5666 | 0.7588 |
  | 0.3862        | 16.0  | 1152 | 0.4682          | 0.4440 | 0.6248 | 0.5171  | 0.1824  | 0.2392   | 0.3516 | 0.5906 | 0.7793 |
  | 0.3726        | 17.0  | 1224 | 0.4702          | 0.4425 | 0.6243 | 0.5190  | 0.1807  | 0.2385   | 0.3591 | 0.5904 | 0.7824 |
  | 0.4016        | 18.0  | 1296 | 0.5012          | 0.4789 | 0.6418 | 0.6093  | 0.2002  | 0.2561   | 0.3035 | 0.5188 | 0.7003 |
  | 0.3772        | 19.0  | 1368 | 0.4935          | 0.4676 | 0.6371 | 0.5940  | 0.1947  | 0.2525   | 0.3195 | 0.5398 | 0.7340 |
  | 0.3987        | 20.0  | 1440 | 0.4630          | 0.4399 | 0.6312 | 0.4934  | 0.1801  | 0.2388   | 0.3711 | 0.6044 | 0.7865 |
  | 0.378         | 21.0  | 1512 | 0.4424          | 0.4180 | 0.6211 | 0.4329  | 0.1683  | 0.2280   | 0.4210 | 0.6415 | 0.8022 |
  | 0.3674        | 22.0  | 1584 | 0.4591          | 0.4346 | 0.6272 | 0.5022  | 0.1764  | 0.2374   | 0.3819 | 0.6184 | 0.7881 |
  | 0.3803        | 23.0  | 1656 | 0.4708          | 0.4483 | 0.6276 | 0.5228  | 0.1841  | 0.2404   | 0.3484 | 0.5854 | 0.7597 |
  | 0.4082        | 24.0  | 1728 | 0.4753          | 0.4506 | 0.6286 | 0.5436  | 0.1854  | 0.2436   | 0.3512 | 0.5780 | 0.7593 |
  | 0.3662        | 25.0  | 1800 | 0.4455          | 0.4221 | 0.6160 | 0.4622  | 0.1709  | 0.2288   | 0.3897 | 0.6406 | 0.8124 |
  | 0.3735        | 26.0  | 1872 | 0.4405          | 0.4194 | 0.6219 | 0.4487  | 0.1691  | 0.2304   | 0.4091 | 0.6492 | 0.8080 |
  | 0.3387        | 27.0  | 1944 | 0.4449          | 0.4235 | 0.6176 | 0.4538  | 0.1716  | 0.2282   | 0.3807 | 0.6471 | 0.8122 |
  | 0.3826        | 28.0  | 2016 |