HF计算机视觉
sayakpaul/glpn-nyu-finetuned-diode-221122-044810
glpn-nyu-finetuned-diode-221122-044810
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: 0.3690
- Mae: 0.2909
- Rmse: 0.4208
- Abs Rel: 0.3635
- Log Mae: 0.1224
- Log Rmse: 0.1793
- Delta1: 0.5323
- Delta2: 0.8179
- Delta3: 0.9258
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.3864 | 1.0 | 72 | 1.2016 | 3.4656 | 3.5204 | 5.1101 | 0.6881 | 0.7346 | 0.0 | 0.0011 | 0.0764 |
| 1.0082 | 2.0 | 144 | 0.4607 | 0.4107 | 0.5420 | 0.5254 | 0.1697 | 0.2234 | 0.3596 | 0.6460 | 0.8465 |
| 0.4656 | 3.0 | 216 | 0.4071 | 0.3431 | 0.4758 | 0.4359 | 0.1425 | 0.1992 | 0.4567 | 0.7481 | 0.8958 |
| 0.4093 | 4.0 | 288 | 0.3953 | 0.3261 | 0.4622 | 0.4197 | 0.1363 | 0.1947 | 0.4841 | 0.7624 | 0.9103 |
| 0.392 | 5.0 | 360 | 0.3916 | 0.3211 | 0.4463 | 0.4116 | 0.1338 | 0.1896 | 0.4810 | 0.7756 | 0.9176 |
| 0.3466 | 6.0 | 432 | 0.3807 | 0.3075 | 0.4451 | 0.3658 | 0.1293 | 0.1839 | 0.5026 | 0.7921 | 0.9180 |
| 0.3297 | 7.0 | 504 | 0.3811 | 0.3047 | 0.4448 | 0.3534 | 0.1290 | 0.1835 | 0.5066 | 0.7920 | 0.9137 |
| 0.2768 | 8.0 | 576 | 0.3779 | 0.3057 | 0.4283 | 0.3894 | 0.1280 | 0.1832 | 0.5046 | 0.7996 | 0.9256 |
| 0.2849 | 9.0 | 648 | 0.3753 | 0.2978 | 0.4341 | 0.3496 | 0.1259 | 0.1806 | 0.5149 | 0.8041 | 0.9184 |
| 0.2571 | 10.0 | 720 | 0.3825 | 0.3068 | 0.4305 | 0.3896 | 0.1289 | 0.1849 | 0.4998 | 0.7974 | 0.9206 |
| 0.2246 | 11.0 | 792 | 0.3718 | 0.2951 | 0.4235 | 0.3678 | 0.1240 | 0.1803 | 0.5249 | 0.8105 | 0.9248 |
| 0.2703 | 12.0 | 864 | 0.3716 | 0.2945 | 0.4317 | 0.3593 | 0.1235 | 0.1808 | 0.5324 | 0.8122 | 0.9215 |
| 0.2596 | 13.0 | 936 | 0.3692 | 0.2921 | 0.4185 | 0.3690 | 0.1229 | 0.1798 | 0.5294 | 0.8167 | 0.9264 |
| 0.2604 | 14.0 | 1008 | 0.3684 | 0.2893 | 0.4171 | 0.3601 | 0.1223 | 0.1785 | 0.5325 | 0.8179 | 0.9252 |
| 0.2679 | 15.0 | 1080 | 0.3690 | 0.2909 | 0.4208 | 0.3635 | 0.1224 | 0.1793 | 0.5323 | 0.8179 | 0.9258 |
Framework versions
- Transformers 4.24.0
- PyTorch 1.12.1+cu116
- Tokenizers 0.13.2
数据统计
数据评估
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