glpn-nyu-finetuned-diode-230530-204740
This model is a fine-tuned version of vinvino02/glpn-nyu on the diode-subset dataset. It achieves the following results on the evaluation set:
- Loss: 1.5139
- Mae: 3.0509
- Rmse: 3.4756
- Abs Rel: 5.7613
- Log Mae: 0.6836
- Log Rmse: 0.8048
- Delta1: 0.3028
- Delta2: 0.3079
- Delta3: 0.3096
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: 1e-05
- 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: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 1 | 1.5335 | 3.1427 | 3.6089 | 5.9847 | 0.6920 | 0.8173 | 0.3016 | 0.3077 | 0.3094 |
| No log | 2.0 | 2 | 1.5297 | 3.1246 | 3.5833 | 5.9419 | 0.6903 | 0.8149 | 0.3018 | 0.3077 | 0.3094 |
| No log | 3.0 | 3 | 1.5263 | 3.1085 | 3.5602 | 5.9033 | 0.6889 | 0.8128 | 0.3020 | 0.3078 | 0.3095 |
| No log | 4.0 | 4 | 1.5234 | 3.0947 | 3.5400 | 5.8694 | 0.6876 | 0.8109 | 0.3022 | 0.3078 | 0.3095 |
| No log | 5.0 | 5 | 1.5208 | 3.0825 | 3.5222 | 5.8395 | 0.6865 | 0.8092 | 0.3024 | 0.3079 | 0.3095 |
| No log | 6.0 | 6 | 1.5185 | 3.0723 | 3.5072 | 5.8144 | 0.6856 | 0.8078 | 0.3025 | 0.3079 | 0.3095 |
| No log | 7.0 | 7 | 1.5167 | 3.0639 | 3.4949 | 5.7937 | 0.6848 | 0.8067 | 0.3026 | 0.3079 | 0.3096 |
| No log | 8.0 | 8 | 1.5153 | 3.0574 | 3.4852 | 5.7775 | 0.6842 | 0.8057 | 0.3027 | 0.3079 | 0.3096 |
| No log | 9.0 | 9 | 1.5143 | 3.0531 | 3.4788 | 5.7667 | 0.6838 | 0.8051 | 0.3028 | 0.3079 | 0.3096 |
| No log | 10.0 | 10 | 1.5139 | 3.0509 | 3.4756 | 5.7613 | 0.6836 | 0.8048 | 0.3028 | 0.3079 | 0.3096 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
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