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glpn-nyu-finetuned
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: 1.5286
- Mae: 3.1196
- Rmse: 3.5796
- Abs Rel: 5.9353
- Log Mae: 0.6899
- Log Rmse: 0.8145
- Delta1: 0.3012
- Delta2: 0.3076
- Delta3: 0.3093
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 | 1.5476 | 3.2112 | 3.7133 | 6.1586 | 0.6980 | 0.8267 | 0.2998 | 0.3073 | 0.3091 |
| No log | 2.0 | 2 | 1.5441 | 3.1939 | 3.6889 | 6.1181 | 0.6965 | 0.8245 | 0.3001 | 0.3073 | 0.3091 |
| No log | 3.0 | 3 | 1.5410 | 3.1783 | 3.6668 | 6.0811 | 0.6951 | 0.8225 | 0.3003 | 0.3074 | 0.3092 |
| No log | 4.0 | 4 | 1.5381 | 3.1643 | 3.6465 | 6.0474 | 0.6939 | 0.8207 | 0.3005 | 0.3074 | 0.3092 |
| No log | 5.0 | 5 | 1.5355 | 3.1520 | 3.6285 | 6.0172 | 0.6928 | 0.8190 | 0.3007 | 0.3075 | 0.3092 |
| No log | 6.0 | 6 | 1.5333 | 3.1415 | 3.6128 | 5.9909 | 0.6918 | 0.8176 | 0.3009 | 0.3075 | 0.3092 |
| No log | 7.0 | 7 | 1.5315 | 3.1329 | 3.5999 | 5.9693 | 0.6911 | 0.8164 | 0.3010 | 0.3075 | 0.3093 |
| No log | 8.0 | 8 | 1.5301 | 3.1264 | 3.5901 | 5.9529 | 0.6905 | 0.8155 | 0.3011 | 0.3075 | 0.3093 |
| No log | 9.0 | 9 | 1.5291 | 3.1219 | 3.5832 | 5.9413 | 0.6901 | 0.8149 | 0.3012 | 0.3076 | 0.3093 |
| No log | 10.0 | 10 | 1.5286 | 3.1196 | 3.5796 | 5.9353 | 0.6899 | 0.8145 | 0.3012 | 0.3076 | 0.3093 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
