glpn-nyu-finetuned-diode-221122-082237
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: 0.3421
- Mae: 0.2700
- Rmse: 0.4042
- Abs Rel: 0.3279
- Log Mae: 0.1132
- Log Rmse: 0.1688
- Delta1: 0.5839
- Delta2: 0.8408
- Delta3: 0.9309
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: 2e-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: 0.2
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.7577 | 1.0 | 72 | 0.5814 | 0.7579 | 0.9267 | 0.9816 | 0.2592 | 0.3062 | 0.1341 | 0.3276 | 0.6248 |
| 0.4447 | 2.0 | 144 | 0.3947 | 0.3412 | 0.4785 | 0.4051 | 0.1381 | 0.1912 | 0.4585 | 0.7697 | 0.9072 |
| 0.4034 | 3.0 | 216 | 0.3657 | 0.2988 | 0.4357 | 0.3629 | 0.1242 | 0.1802 | 0.5321 | 0.8054 | 0.9202 |
| 0.3726 | 4.0 | 288 | 0.3576 | 0.2896 | 0.4176 | 0.3647 | 0.1215 | 0.1769 | 0.5376 | 0.8178 | 0.9270 |
| 0.3656 | 5.0 | 360 | 0.3547 | 0.2818 | 0.4098 | 0.3504 | 0.1186 | 0.1732 | 0.5551 | 0.8225 | 0.9283 |
| 0.3211 | 6.0 | 432 | 0.3495 | 0.2743 | 0.4160 | 0.3250 | 0.1155 | 0.1708 | 0.5773 | 0.8317 | 0.9258 |
| 0.3027 | 7.0 | 504 | 0.3471 | 0.2724 | 0.4123 | 0.3226 | 0.1146 | 0.1695 | 0.5801 | 0.8345 | 0.9283 |
| 0.2438 | 8.0 | 576 | 0.3494 | 0.2735 | 0.4097 | 0.3304 | 0.1157 | 0.1708 | 0.5741 | 0.8328 | 0.9267 |
| 0.2512 | 9.0 | 648 | 0.3448 | 0.2700 | 0.4121 | 0.3160 | 0.1134 | 0.1683 | 0.5880 | 0.8395 | 0.9266 |
| 0.2416 | 10.0 | 720 | 0.3439 | 0.2688 | 0.4017 | 0.3255 | 0.1135 | 0.1682 | 0.5781 | 0.8397 | 0.9324 |
| 0.1971 | 11.0 | 792 | 0.3456 | 0.2730 | 0.4059 | 0.3348 | 0.1148 | 0.1703 | 0.5730 | 0.8370 | 0.9305 |
| 0.2382 | 12.0 | 864 | 0.3446 | 0.2708 | 0.4069 | 0.3283 | 0.1139 | 0.1696 | 0.5818 | 0.8394 | 0.9295 |
| 0.237 | 13.0 | 936 | 0.3417 | 0.2674 | 0.4038 | 0.3218 | 0.1123 | 0.1680 | 0.5901 | 0.8424 | 0.9307 |
| 0.2378 | 14.0 | 1008 | 0.3421 | 0.2686 | 0.4030 | 0.3241 | 0.1130 | 0.1683 | 0.5852 | 0.8408 | 0.9311 |
| 0.2409 | 15.0 | 1080 | 0.3421 | 0.2700 | 0.4042 | 0.3279 | 0.1132 | 0.1688 | 0.5839 | 0.8408 | 0.9309 |
数据统计
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