readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-230113-130735
    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-230113-130735
  
  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: 0.4320
  - Mae: 0.4213
  - Rmse: 0.6133
  - Abs Rel: 0.4298
  - Log Mae: 0.1697
  - Log Rmse: 0.2216
  - Delta1: 0.3800
  - Delta2: 0.6396
  - Delta3: 0.8189
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
  | 1.0073        | 1.0   | 72   | 0.4927          | 0.4684 | 0.6425 | 0.5680  | 0.1955  | 0.2515   | 0.3154 | 0.5289 | 0.7834 |
  | 0.4694        | 2.0   | 144  | 0.4560          | 0.4425 | 0.6285 | 0.4674  | 0.1818  | 0.2341   | 0.3395 | 0.6061 | 0.7873 |
  | 0.4632        | 3.0   | 216  | 0.4817          | 0.4646 | 0.6341 | 0.5412  | 0.1930  | 0.2453   | 0.3181 | 0.5368 | 0.7491 |
  | 0.4363        | 4.0   | 288  | 0.4589          | 0.4379 | 0.6228 | 0.4880  | 0.1793  | 0.2348   | 0.3588 | 0.6025 | 0.7952 |
  | 0.4636        | 5.0   | 360  | 0.4767          | 0.4545 | 0.6301 | 0.5367  | 0.1878  | 0.2430   | 0.3279 | 0.5716 | 0.7705 |
  | 0.4642        | 6.0   | 432  | 0.4437          | 0.4185 | 0.6200 | 0.4405  | 0.1689  | 0.2283   | 0.4071 | 0.6531 | 0.8091 |
  | 0.409         | 7.0   | 504  | 0.4787          | 0.4542 | 0.6291 | 0.5399  | 0.1873  | 0.2430   | 0.3345 | 0.5679 | 0.7648 |
  | 0.4081        | 8.0   | 576  | 0.4545          | 0.4359 | 0.6258 | 0.4554  | 0.1779  | 0.2311   | 0.3717 | 0.6035 | 0.7952 |
  | 0.4146        | 9.0   | 648  | 0.4726          | 0.4523 | 0.6293 | 0.5108  | 0.1870  | 0.2403   | 0.3394 | 0.5692 | 0.7571 |
  | 0.392         | 10.0  | 720  | 0.4643          | 0.4453 | 0.6249 | 0.5081  | 0.1831  | 0.2372   | 0.3380 | 0.5881 | 0.7917 |
  | 0.3722        | 11.0  | 792  | 0.4670          | 0.4475 | 0.6245 | 0.4957  | 0.1838  | 0.2355   | 0.3413 | 0.5739 | 0.7689 |
  | 0.4397        | 12.0  | 864  | 0.4548          | 0.4367 | 0.6262 | 0.4604  | 0.1780  | 0.2319   | 0.3664 | 0.6081 | 0.7903 |
  | 0.43          | 13.0  | 936  | 0.4281          | 0.4223 | 0.6230 | 0.3974  | 0.1691  | 0.2207   | 0.3975 | 0.6426 | 0.7943 |
  | 0.3976        | 14.0  | 1008 | 0.4592          | 0.4470 | 0.6249 | 0.4759  | 0.1827  | 0.2321   | 0.3482 | 0.5784 | 0.7507 |
  | 0.4251        | 15.0  | 1080 | 0.4515          | 0.4366 | 0.6205 | 0.4589  | 0.1773  | 0.2285   | 0.3689 | 0.5990 | 0.7785 |
  | 0.4007        | 16.0  | 1152 | 0.4859          | 0.4668 | 0.6347 | 0.5570  | 0.1939  | 0.2467   | 0.3156 | 0.5378 | 0.7265 |
  | 0.376         | 17.0  | 1224 | 0.4529          | 0.4331 | 0.6195 | 0.4421  | 0.1752  | 0.2260   | 0.3795 | 0.6016 | 0.7702 |
  | 0.4028        | 18.0  | 1296 | 0.5027          | 0.4775 | 0.6420 | 0.6169  | 0.1993  | 0.2569   | 0.3098 | 0.5228 | 0.7035 |
  | 0.3816        | 19.0  | 1368 | 0.4869          | 0.4634 | 0.6342 | 0.5565  | 0.1924  | 0.2473   | 0.3276 | 0.5448 | 0.7370 |
  | 0.4092        | 20.0  | 1440 | 0.4317          | 0.4155 | 0.6164 | 0.4083  | 0.1661  | 0.2218   | 0.4003 | 0.6569 | 0.8123 |
  | 0.3673        | 21.0  | 1512 | 0.4433          | 0.4326 | 0.6208 | 0.4295  | 0.1750  | 0.2244   | 0.3751 | 0.6068 | 0.7879 |
  | 0.3698        | 22.0  | 1584 | 0.4607          | 0.4322 | 0.6216 | 0.4981  | 0.1758  | 0.2354   | 0.3831 | 0.6163 | 0.7906 |
  | 0.3771        | 23.0  | 1656 | 0.4668          | 0.4478 | 0.6255 | 0.5075  | 0.1841  | 0.2373   | 0.3390 | 0.5819 | 0.7697 |
  | 0.4343        | 24.0  | 1728 | 0.4532          | 0.4331 | 0.6203 | 0.4722  | 0.1767  | 0.2312   | 0.3587 | 0.6166 | 0.8087 |
  | 0.4011        | 25.0  | 1800 | 0.4499          | 0.4327 | 0.6213 | 0.4519  | 0.1755  | 0.2279   | 0.3716 | 0.6152 | 0.7844 |
  | 0.3714        | 26.0  | 1872 | 0.4460          | 0.4254 | 0.6188 | 0.4495  | 0.1716  | 0.2278   | 0.3932 | 0.6352 | 0.7916 |
  | 0.3436        | 27.0  | 1944 | 0.4360          | 0.4182 | 0.6165 | 0.4192  | 0.1682  | 0.2224   | 0.3894 | 0.6524 | 0.8145 |
  | 0.3698        | 28.0  | 2016 |