sayakpaul/glpn-nyu-finetuned-diode-221122-014502百度ai智能云
glpn百度ai智能云-nyu-finetuned-diode-221122-014502
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: 0.3476
- Mae: 0.2763
- Rmse: 0.4088
- Abs Rel: 0.3308
- Log Mae: 0.1161
- Log Rmse: 0.1700
- Delta1: 0.5682
- Delta2: 0.8301
- Delta3: 0.9279
Model description
More information needed猫箱下载安装
Intended uses & limitations
More information neededgrok中文版下载
Training and evaluation data
More information needed做al视频怎么赚钱
Training procedure
Training hyperparameters
The following hyperparameters were used during training:ai软件哪个比较好
- 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: 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.7598 | 1.0 | 72 | 0.5809 | 0.7606 | 0.9281 | 0.9834 | 0.2597 | 0.3064 | 0.1320 | 0.3250 | 0.6234 |
| 0.4481 | 2.0 | 144 | 0.4013 | 0.3507 | 0.4879 | 0.4181 | 0.1415 | 0.1950 | 0.4427 | 0.7602 | 0.9021 |
| 0.4066 | 3.0 | 216 | 0.3706 | 0.3081 | 0.4484 | 0.3675 | 0.1269 | 0.1823 | 0.5187 | 0.7977 | 0.9148 |
| 0.3965 | 4.0 | 288 | 0.3641 | 0.2987 | 0.4336 | 0.3607 | 0.1239 | 0.1787 | 0.5294 | 0.8072 | 0.9205 |
| 0.3942 | 5.0 | 360 | 0.3582 | 0.2903 | 0.4251 | 0.3490 | 0.1207 | 0.1753 | 0.5466 | 0.8165 | 0.9232 |
| 0.3575 | 6.0 | 432 | 0.3568 | 0.2898 | 0.4184 | 0.3569 | 0.1211 | 0.1753 | 0.5390 | 0.8171 | 0.9265 |
| 0.3418 | 7.0 | 504 | 0.3490 | 0.2771 | 0.4178 | 0.3248 | 0.1156 | 0.1707 | 0.5783 | 0.8312 | 0.9259 |
| 0.2916 | 8.0 | 576 | 0.3512 | 0.2819 | 0.4172 | 0.3373 | 0.1178 | 0.1725 | 0.5620 | 0.8253 | 0.9262 |
| 0.3055 | 9.0 | 648 | 0.3506 | 0.2808 | 0.4091 | 0.3422 | 0.1180 | 0.1718 | 0.5537 | 0.8248 | 0.9292 |
| 0.2932 | 10.0 | 720 | 0.3518 | 0.2809 | 0.4110 | 0.3441 | 0.1182 | 0.1724 | 0.5548 | 0.8239 | 0.9290 |
| 0.2518 | 11.0 | 792 | 0.3476 | 0.2756 | 0.4115 | 0.3265 | 0.1155 | 0.1700 | 0.5741 | 0.8326 | 0.9264 |
| 0.3177 | 12.0 | 864 | 0.3491 | 0.2784 | 0.4104 | 0.3333 | 0.1169 | 0.1706 | 0.5620 | 0.8290 | 0.9283 |
| 0.3038 | 13.0 | 936 | 0.3503 | 0.2795 | 0.4094 | 0.3410 | 0.1175 | 0.1717 | 0.5596 | 0.8275 | 0.9283 |
| 0.3299 | 14.0 | 1008 | 0.3460 | 0.2750 | 0.4098 | 0.3257 | 0.1154 | 0.1693 | 0.5721 | 0.8325 | 0.9283 |
| 0.3325 | 15.0 | 1080 | 0.3476 | 0.2763 | 0.4088 | 0.3308 | 0.1161 | 0.1700 | 0.5682 | 0.8301 | 0.9279 |
Framework versions
数据统计
数据评估
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