readme: ---
  license: apache-2.0
  tags:
  - vision
  - depth-estimation
  - generated_from_trainer
  model-index:
  - name: glpn-nyu-finetuned-diode-221223-094145
    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-221223-094145
  
  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: 0.4077
  - Mae: 0.4032
  - Rmse: 0.6201
  - Abs Rel: 0.3554
  - Log Mae: 0.1594
  - Log Rmse: 0.2173
  - Delta1: 0.4530
  - Delta2: 0.6868
  - Delta3: 0.8071
  
  ## 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: 0.0001
  - 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: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Mae    | Rmse   | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
  | 1.0433        | 1.0   | 72   | 0.5885          | 0.5648 | 0.7732 | 0.4665  | 0.2691  | 0.3222   | 0.2134 | 0.4070 | 0.5668 |
  | 0.4529        | 2.0   | 144  | 0.4284          | 0.4217 | 0.6232 | 0.3846  | 0.1702  | 0.2214   | 0.3935 | 0.6428 | 0.7958 |
  | 0.415         | 3.0   | 216  | 0.4221          | 0.4049 | 0.6164 | 0.3800  | 0.1603  | 0.2180   | 0.4499 | 0.6735 | 0.8070 |
  | 0.3643        | 4.0   | 288  | 0.4430          | 0.4172 | 0.6176 | 0.4419  | 0.1671  | 0.2265   | 0.4208 | 0.6489 | 0.8077 |
  | 0.3927        | 5.0   | 360  | 0.4186          | 0.4072 | 0.6199 | 0.3646  | 0.1623  | 0.2199   | 0.4362 | 0.6675 | 0.8077 |
  | 0.389         | 6.0   | 432  | 0.4093          | 0.4018 | 0.6168 | 0.3515  | 0.1592  | 0.2155   | 0.4499 | 0.6753 | 0.8111 |
  | 0.3521        | 7.0   | 504  | 0.4320          | 0.4112 | 0.6165 | 0.4061  | 0.1646  | 0.2226   | 0.4358 | 0.6569 | 0.8062 |
  | 0.3324        | 8.0   | 576  | 0.4056          | 0.3977 | 0.6132 | 0.3570  | 0.1566  | 0.2148   | 0.4556 | 0.7006 | 0.8157 |
  | 0.3183        | 9.0   | 648  | 0.4187          | 0.4036 | 0.6151 | 0.3667  | 0.1607  | 0.2172   | 0.4472 | 0.6664 | 0.8095 |
  | 0.3052        | 10.0  | 720  | 0.4149          | 0.4031 | 0.6171 | 0.3683  | 0.1601  | 0.2191   | 0.4469 | 0.6815 | 0.8073 |
  | 0.3071        | 11.0  | 792  | 0.4168          | 0.4111 | 0.6252 | 0.3587  | 0.1647  | 0.2218   | 0.4322 | 0.6643 | 0.8019 |
  | 0.3358        | 12.0  | 864  | 0.4161          | 0.4029 | 0.6171 | 0.3650  | 0.1600  | 0.2189   | 0.4507 | 0.6789 | 0.8092 |
  | 0.3385        | 13.0  | 936  | 0.4116          | 0.4051 | 0.6215 | 0.3565  | 0.1609  | 0.2190   | 0.4478 | 0.6770 | 0.8053 |
  | 0.316         | 14.0  | 1008 | 0.4092          | 0.3982 | 0.6138 | 0.3618  | 0.1569  | 0.2157   | 0.4577 | 0.6951 | 0.8109 |
  | 0.3301        | 15.0  | 1080 | 0.4159          | 0.4056 | 0.6199 | 0.3654  | 0.1619  | 0.2204   | 0.4462 | 0.6743 | 0.8056 |
  | 0.3076        | 16.0  | 1152 | 0.4130          | 0.4051 | 0.6200 | 0.3612  | 0.1612  | 0.2195   | 0.4470 | 0.6787 | 0.8076 |
  | 0.3001        | 17.0  | 1224 | 0.4134          | 0.4071 | 0.6244 | 0.3579  | 0.1621  | 0.2210   | 0.4487 | 0.6771 | 0.8045 |
  | 0.3293        | 18.0  | 1296 | 0.4091          | 0.4031 | 0.6182 | 0.3552  | 0.1601  | 0.2174   | 0.4501 | 0.6786 | 0.8065 |
  | 0.3023        | 19.0  | 1368 | 0.4089          | 0.3990 | 0.6143 | 0.3633  | 0.1573  | 0.2160   | 0.4518 | 0.6966 | 0.8137 |
  | 0.3288        | 20.0  | 1440 | 0.4067          | 0.4006 | 0.6166 | 0.3538  | 0.1580  | 0.2155   | 0.4529 | 0.6895 | 0.8122 |
  | 0.2988        | 21.0  | 1512 | 0.4061          | 0.4060 | 0.6221 | 0.3491  | 0.1614  | 0.2183   | 0.4480 | 0.6777 | 0.8059 |
  | 0.3037        | 22.0  | 1584 | 0.4081          | 0.4025 | 0.6204 | 0.3582  | 0.1587  | 0.2174   | 0.4523 | 0.6905 | 0.8093 |
  | 0.3284        | 23.0  | 1656 | 0.4080          | 0.4062 | 0.6209 | 0.3545  | 0.1615  | 0.2184   | 0.4409 | 0.6794 | 0.8060 |
  | 0.3261        | 24.0  | 1728 | 0.4092          | 0.4044 | 0.6208 | 0.3562  | 0.1602  | 0.2183   | 0.4512 | 0.6807 | 0.8061 |
  | 0.3039        | 25.0  | 1800 | 0.4079          | 0.4005 | 0.6159 | 0.3576  | 0.1585  | 0.2167   | 0.4611 | 0.6827 | 0.8095 |
  | 0.2843        | 26.0  | 1872 | 0.4072          | 0.4045 | 0.6212 | 0.3548  | 0.1603  | 0.2182   | 0.4502 | 0.6856 | 0.8079 |
  | 0.2828        | 27.0  | 1944 | 0.4110          | 0.4089 | 0.6248 | 0.3578  | 0.1631  | 0.2211   | 0.4419 | 0.6756 | 0.8031 |
  | 0.3212        | 28.0  | 2016 |