🚀 mobilevit-small-10k-steps
这个模型是 apple/deeplabv3-mobilevit-small 在 Efferbach/lane_master2 数据集上的微调版本。它在评估集上取得了以下结果：
- 损失值：0.0821
- 平均交并比（Mean Iou）：0.0
- 平均准确率：0.0
- 总体准确率：0.0
- 背景准确率：nan
- 左侧准确率：0.0
- 右侧准确率：0.0
- 背景交并比：0.0
- 左侧交并比：0.0
- 右侧交并比：0.0
📚 详细文档
训练过程
训练超参数
训练过程中使用了以下超参数：
- 学习率：6e-05
- 训练批次大小：8
- 评估批次大小：8
- 随机种子：1337
- 优化器：Adam（β1=0.9，β2=0.999，ε=1e-08）
- 学习率调度器类型：多项式
- 训练步数：10000
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 |
总体准确率 |
背景准确率 |
左侧准确率 |
右侧准确率 |
背景交并比 |
左侧交并比 |
右侧交并比 |
| 0.5041 |
1.0 |
385 |
0.3382 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.1553 |
2.0 |
770 |
0.1387 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.1019 |
3.0 |
1155 |
0.1037 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0882 |
4.0 |
1540 |
0.0883 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0828 |
5.0 |
1925 |
0.0823 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0807 |
6.0 |
2310 |
0.0820 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0795 |
7.0 |
2695 |
0.0804 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0786 |
8.0 |
3080 |
0.0784 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0777 |
9.0 |
3465 |
0.0786 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0771 |
10.0 |
3850 |
0.0774 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0773 |
11.0 |
4235 |
0.0775 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0765 |
12.0 |
4620 |
0.0782 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0757 |
13.0 |
5005 |
0.0775 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0756 |
14.0 |
5390 |
0.0774 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0754 |
15.0 |
5775 |
0.0775 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0746 |
16.0 |
6160 |
0.0775 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.074 |
17.0 |
6545 |
0.0779 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0736 |
18.0 |
6930 |
0.0792 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0737 |
19.0 |
7315 |
0.0801 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.073 |
20.0 |
7700 |
0.0804 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0729 |
21.0 |
8085 |
0.0805 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0734 |
22.0 |
8470 |
0.0804 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0726 |
23.0 |
8855 |
0.0811 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0726 |
24.0 |
9240 |
0.0816 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0721 |
25.0 |
9625 |
0.0822 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
| 0.0727 |
25.97 |
10000 |
0.0821 |
0.0 |
0.0 |
0.0 |
nan |
0.0 |
0.0 |
0.0 |
0.0 |
0.0 |
框架版本
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
📄 许可证
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