sayakpaul/glpn-nyu-finetuned-diode-221214-054706下载官方即梦a1
glpnai软件哪个比较好-nyu-finetuned-diode-221214-054706
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: 0.3340
- Mae: 0.2649
- Rmse: 0.3917
- Abs Rel: 0.3138
- Log Mae: 0.1111
- Log Rmse: 0.1640
- Delta1: 0.5843
- Delta2: 0.8459
- Delta3: 0.9413
Model description
More information neededai分析软件
Intended uses & limitations
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Training and evaluation data
More information neededai软件哪个比较好
Training procedure
Training hyperparameters
The following hyperparameters were used during training:猫箱下载安装
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.746 | 1.0 | 72 | 0.5730 | 0.7316 | 0.8964 | 0.9501 | 0.2530 | 0.3005 | 0.1427 | 0.3446 | 0.6453 |
| 0.4215 | 2.0 | 144 | 0.3764 | 0.3140 | 0.4548 | 0.3607 | 0.1286 | 0.1819 | 0.5054 | 0.7927 | 0.9161 |
| 0.3566 | 3.0 | 216 | 0.3568 | 0.2894 | 0.4226 | 0.3436 | 0.1206 | 0.1751 | 0.5475 | 0.8178 | 0.9257 |
| 0.322 | 4.0 | 288 | 0.3523 | 0.2818 | 0.4069 | 0.3541 | 0.1188 | 0.1740 | 0.5508 | 0.8219 | 0.9325 |
| 0.3066 | 5.0 | 360 | 0.3464 | 0.2777 | 0.4090 | 0.3283 | 0.1164 | 0.1695 | 0.5609 | 0.8328 | 0.9313 |
| 0.256 | 6.0 | 432 | 0.3384 | 0.2687 | 0.3927 | 0.3173 | 0.1136 | 0.1647 | 0.5665 | 0.8381 | 0.9394 |
| 0.2268 | 7.0 | 504 | 0.3407 | 0.2747 | 0.4138 | 0.3091 | 0.1143 | 0.1672 | 0.5716 | 0.8380 | 0.9322 |
| 0.1721 | 8.0 | 576 | 0.3346 | 0.2631 | 0.3850 | 0.3176 | 0.1108 | 0.1636 | 0.5804 | 0.8489 | 0.9452 |
| 0.1898 | 9.0 | 648 | 0.3362 | 0.2656 | 0.3994 | 0.3100 | 0.1112 | 0.1650 | 0.5910 | 0.8455 | 0.9368 |
| 0.1609 | 10.0 | 720 | 0.3355 | 0.2667 | 0.3866 | 0.3242 | 0.1124 | 0.1648 | 0.5693 | 0.8452 | 0.9433 |
| 0.139 | 11.0 | 792 | 0.3344 | 0.2660 | 0.3954 | 0.3115 | 0.1112 | 0.1639 | 0.5833 | 0.8453 | 0.9409 |
| 0.1772 | 12.0 | 864 | 0.3360 | 0.2667 | 0.3979 | 0.3137 | 0.1115 | 0.1650 | 0.5854 | 0.8449 | 0.9393 |
| 0.1631 | 13.0 | 936 | 0.3353 | 0.2652 | 0.3938 | 0.3134 | 0.1112 | 0.1645 | 0.5858 | 0.8451 | 0.9403 |
| 0.1736 | 14.0 | 1008 | 0.3367 | 0.2663 | 0.3954 | 0.3163 | 0.1117 | 0.1653 | 0.5840 | 0.8446 | 0.9391 |
| 0.1736 | 15.0 | 1080 | 0.3340 | 0.2649 | 0.3917 | 0.3138 | 0.1111 | 0.1640 | 0.5843 | 0.8459 | 0.9413 |
Framework versions
数据统计
数据评估
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