readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-221229-103851
    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-221229-103851
  
  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: 0.4367
  - Mae: 0.4201
  - Rmse: 0.6202
  - Abs Rel: 0.4454
  - Log Mae: 0.1684
  - Log Rmse: 0.2265
  - Delta1: 0.3989
  - Delta2: 0.6516
  - Delta3: 0.8162
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
  | 1.0076        | 1.0   | 72   | 0.4910          | 0.4686 | 0.6460 | 0.5610  | 0.1948  | 0.2509   | 0.3172 | 0.5344 | 0.7863 |
  | 0.4695        | 2.0   | 144  | 0.4518          | 0.4410 | 0.6304 | 0.4547  | 0.1805  | 0.2328   | 0.3427 | 0.6159 | 0.7933 |
  | 0.4629        | 3.0   | 216  | 0.4766          | 0.4636 | 0.6340 | 0.5289  | 0.1921  | 0.2433   | 0.3189 | 0.5391 | 0.7521 |
  | 0.4366        | 4.0   | 288  | 0.4581          | 0.4417 | 0.6300 | 0.4828  | 0.1801  | 0.2349   | 0.3591 | 0.6006 | 0.7918 |
  | 0.4643        | 5.0   | 360  | 0.4687          | 0.4514 | 0.6290 | 0.5164  | 0.1856  | 0.2394   | 0.3340 | 0.5755 | 0.7834 |
  | 0.4594        | 6.0   | 432  | 0.4515          | 0.4269 | 0.6214 | 0.4603  | 0.1727  | 0.2302   | 0.3894 | 0.6253 | 0.8033 |
  | 0.4169        | 7.0   | 504  | 0.4669          | 0.4451 | 0.6271 | 0.4934  | 0.1830  | 0.2374   | 0.3538 | 0.5812 | 0.7772 |
  | 0.4023        | 8.0   | 576  | 0.4734          | 0.4533 | 0.6353 | 0.5114  | 0.1869  | 0.2416   | 0.3403 | 0.5711 | 0.7772 |
  | 0.4094        | 9.0   | 648  | 0.4752          | 0.4491 | 0.6329 | 0.5141  | 0.1843  | 0.2420   | 0.3621 | 0.5831 | 0.7587 |
  | 0.3848        | 10.0  | 720  | 0.4515          | 0.4309 | 0.6246 | 0.4686  | 0.1750  | 0.2323   | 0.3837 | 0.6285 | 0.7958 |
  | 0.365         | 11.0  | 792  | 0.4613          | 0.4541 | 0.6318 | 0.4734  | 0.1865  | 0.2348   | 0.3399 | 0.5601 | 0.7451 |
  | 0.4244        | 12.0  | 864  | 0.4875          | 0.4616 | 0.6342 | 0.5606  | 0.1908  | 0.2475   | 0.3382 | 0.5512 | 0.7328 |
  | 0.4397        | 13.0  | 936  | 0.4286          | 0.4183 | 0.6224 | 0.3908  | 0.1672  | 0.2211   | 0.4020 | 0.6556 | 0.8030 |
  | 0.408         | 14.0  | 1008 | 0.4501          | 0.4377 | 0.6278 | 0.4376  | 0.1774  | 0.2289   | 0.3785 | 0.5970 | 0.7752 |
  | 0.4233        | 15.0  | 1080 | 0.4330          | 0.4207 | 0.6252 | 0.3906  | 0.1680  | 0.2223   | 0.4184 | 0.6421 | 0.7951 |
  | 0.3984        | 16.0  | 1152 | 0.4731          | 0.4550 | 0.6295 | 0.5166  | 0.1870  | 0.2394   | 0.3386 | 0.5589 | 0.7554 |
  | 0.3706        | 17.0  | 1224 | 0.4291          | 0.4145 | 0.6170 | 0.3908  | 0.1652  | 0.2189   | 0.4184 | 0.6484 | 0.8062 |
  | 0.424         | 18.0  | 1296 | 0.4910          | 0.4692 | 0.6388 | 0.5791  | 0.1944  | 0.2506   | 0.3195 | 0.5378 | 0.7355 |
  | 0.3814        | 19.0  | 1368 | 0.4982          | 0.4770 | 0.6415 | 0.6067  | 0.1988  | 0.2552   | 0.3065 | 0.5249 | 0.7135 |
  | 0.4224        | 20.0  | 1440 | 0.4222          | 0.4093 | 0.6149 | 0.3911  | 0.1621  | 0.2182   | 0.4257 | 0.6726 | 0.8145 |
  | 0.3773        | 21.0  | 1512 | 0.4464          | 0.4213 | 0.6223 | 0.4333  | 0.1696  | 0.2283   | 0.4087 | 0.6412 | 0.7998 |
  | 0.3529        | 22.0  | 1584 | 0.4541          | 0.4341 | 0.6237 | 0.4770  | 0.1756  | 0.2320   | 0.3749 | 0.6176 | 0.7872 |
  | 0.3726        | 23.0  | 1656 | 0.4439          | 0.4229 | 0.6216 | 0.4461  | 0.1702  | 0.2285   | 0.3984 | 0.6456 | 0.8022 |
  | 0.4254        | 24.0  | 1728 | 0.4898          | 0.4680 | 0.6389 | 0.5684  | 0.1936  | 0.2490   | 0.3196 | 0.5408 | 0.7341 |
  | 0.3814        | 25.0  | 1800 | 0.4462          | 0.4292 | 0.6276 | 0.4380  | 0.1732  | 0.2294   | 0.3901 | 0.6324 | 0.7938 |
  | 0.352         | 26.0  | 1872 | 0.4534          | 0.4316 | 0.6257 | 0.4686  | 0.1747  | 0.2336   | 0.3875 | 0.6286 | 0.7918 |
  | 0.3372        | 27.0  | 1944 | 0.4472          | 0.4275 | 0.6198 | 0.4542  | 0.1733  | 0.2286   | 0.3788 | 0.6311 | 0.8053 |
  | 0.372         | 28.0  | 2016 |