🚀 无人机航拍图像分割模型
该模型用于无人机航拍图像分割,是基于 nvidia/mit-b0 微调得到的模型。在评估集上取得了不错的效果,能有效对航拍图像进行分割处理,为相关领域的图像分析提供支持。
📚 详细文档
模型描述
此模型是 nvidia/mit-b0 的微调版本,在评估集上取得了以下结果:
- 损失值:0.8852
- 平均交并比(Mean Iou):0.2994
- 平均准确率(Mean Accuracy):0.3923
- 整体准确率(Overall Accuracy):0.7774
训练超参数
训练过程中使用了以下超参数:
属性 |
详情 |
学习率 |
5e - 05 |
训练批次大小 |
24 |
评估批次大小 |
8 |
随机种子 |
42 |
优化器 |
Adam(β1 = 0.9,β2 = 0.999,ε = 1e - 08) |
学习率调度器类型 |
线性 |
训练轮数 |
50 |
评估结果
{'mean_iou': 0.27989828118195953,
'mean_accuracy': 0.3712316062110093,
'overall_accuracy': 0.7671712239583334,
'per_category_iou': array([ nan, 0.8560476 , 0.32234631, 0.76880948, 0.57517691,
0.43877125, 0.00114888, 0.14091442, 0.51807365, 0.76964765,
0.27391949, 0. , 0. , 0. , 0. ,
0.05778175, 0. , 0.45566807, 0. , 0.25864545,
0.48767764, 0. , 0.23313364, nan]),
'per_category_accuracy': array([ nan, 0.96170675, 0.43993514, 0.86977593, 0.8149788 ,
0.49739671, 0.00114987, 0.14445379, 0.80978302, 0.88661108,
0.46787116, 0. , 0. , 0. , 0. ,
0.05947339, 0. , 0.55639324, 0. , 0.38358184,
0.761303 , 0. , 0.51268161, nan])}
训练结果

训练损失 |
轮数 |
步数 |
验证损失 |
平均交并比 |
平均准确率 |
整体准确率 |
2.7923 |
1.25 |
20 |
2.8338 |
0.0954 |
0.1626 |
0.5529 |
2.219 |
2.5 |
40 |
2.1391 |
0.1036 |
0.1666 |
0.5929 |
1.9451 |
3.75 |
60 |
1.7919 |
0.1154 |
0.1782 |
0.6129 |
1.7558 |
5.0 |
80 |
1.6767 |
0.1300 |
0.1961 |
0.6396 |
1.6381 |
6.25 |
100 |
1.5817 |
0.1383 |
0.2055 |
0.6550 |
1.5338 |
7.5 |
120 |
1.4816 |
0.1464 |
0.2140 |
0.6729 |
1.4478 |
8.75 |
140 |
1.4231 |
0.1529 |
0.2219 |
0.6823 |
1.361 |
10.0 |
160 |
1.3300 |
0.1637 |
0.2315 |
0.6975 |
1.306 |
11.25 |
180 |
1.3034 |
0.1737 |
0.2419 |
0.7060 |
1.2611 |
12.5 |
200 |
1.2692 |
0.1755 |
0.2450 |
0.7093 |
1.2317 |
13.75 |
220 |
1.2190 |
0.1821 |
0.2501 |
0.7145 |
1.1868 |
15.0 |
240 |
1.2063 |
0.1862 |
0.2539 |
0.7188 |
1.1628 |
16.25 |
260 |
1.1832 |
0.1909 |
0.2612 |
0.7234 |
1.1149 |
17.5 |
280 |
1.1368 |
0.2048 |
0.2739 |
0.7317 |
1.1009 |
18.75 |
300 |
1.1117 |
0.2232 |
0.2938 |
0.7387 |
1.0532 |
20.0 |
320 |
1.0923 |
0.2315 |
0.2997 |
0.7414 |
1.0464 |
21.25 |
340 |
1.0821 |
0.2408 |
0.3147 |
0.7480 |
1.0278 |
22.5 |
360 |
1.0541 |
0.2517 |
0.3277 |
0.7530 |
0.9945 |
23.75 |
380 |
1.0352 |
0.2612 |
0.3398 |
0.7573 |
0.9729 |
25.0 |
400 |
1.0207 |
0.2671 |
0.3511 |
0.7609 |
0.9527 |
26.25 |
420 |
1.0067 |
0.2684 |
0.3547 |
0.7609 |
0.9494 |
27.5 |
440 |
0.9870 |
0.2713 |
0.3548 |
0.7627 |
0.9287 |
28.75 |
460 |
0.9729 |
0.2745 |
0.3619 |
0.7640 |
0.9089 |
30.0 |
480 |
0.9561 |
0.2791 |
0.3640 |
0.7680 |
0.9064 |
31.25 |
500 |
0.9500 |
0.2799 |
0.3712 |
0.7672 |
0.8681 |
32.5 |
520 |
0.9397 |
0.2845 |
0.3749 |
0.7696 |
0.8677 |
33.75 |
540 |
0.9340 |
0.2835 |
0.3737 |
0.7692 |
0.8663 |
35.0 |
560 |
0.9243 |
0.2862 |
0.3755 |
0.7716 |
0.8629 |
36.25 |
580 |
0.9173 |
0.2869 |
0.3766 |
0.7719 |
0.8542 |
37.5 |
600 |
0.9112 |
0.2908 |
0.3810 |
0.7740 |
0.8391 |
38.75 |
620 |
0.9050 |
0.2904 |
0.3812 |
0.7734 |
0.8392 |
40.0 |
640 |
0.9027 |
0.2917 |
0.3818 |
0.7734 |
0.8306 |
41.25 |
660 |
0.8949 |
0.2941 |
0.3841 |
0.7755 |
0.8213 |
42.5 |
680 |
0.8936 |
0.2958 |
0.3875 |
0.7760 |
0.8406 |
43.75 |
700 |
0.8910 |
0.2964 |
0.3879 |
0.7763 |
0.8254 |
45.0 |
720 |
0.8889 |
0.2981 |
0.3897 |
0.7764 |
0.8202 |
46.25 |
740 |
0.8880 |
0.2985 |
0.3917 |
0.7767 |
0.8013 |
47.5 |
760 |
0.8891 |
0.2989 |
0.3923 |
0.7767 |
0.8188 |
48.75 |
780 |
0.8861 |
0.2994 |
0.3926 |
0.7772 |
0.8089 |
50.0 |
800 |
0.8852 |
0.2994 |
0.3923 |
0.7774 |
框架版本
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
📄 许可证
该模型使用其他许可证。