许可证：Apache-2.0
库名称：transformers
标签：
- lam
- newspapers
数据集：
- biglam/loc_beyond_words
流水线标签：object-detection
基础模型：facebook/detr-resnet-50
模型索引：
- 名称：detr-resnet-50_fine_tuned_loc-2023
结果：[]
detr-resnet-50_fine_tuned_loc-2023
此模型是基于facebook/detr-resnet-50在loc_beyond_words数据集上微调的版本。
在评估集上取得了以下结果：
模型描述
需要更多信息
预期用途与限制
需要更多信息
训练与评估数据
需要更多信息
训练过程
训练超参数
训练期间使用了以下超参数：
- 学习率：0.0001
- 训练批次大小：8
- 评估批次大小：8
- 随机种子：42
- 优化器：Adam，参数为betas=(0.9,0.999)，epsilon=1e-08
- 学习率调度器类型：linear
- 训练轮数：100
训练结果
| 训练损失 |
轮次 |
步数 |
验证损失 |
| 3.731 |
0.16 |
50 |
2.6356 |
| 2.4875 |
0.31 |
100 |
2.2348 |
| 2.1786 |
0.47 |
150 |
2.1148 |
| 1.9845 |
0.62 |
200 |
1.8847 |
| 1.8507 |
0.78 |
250 |
1.8331 |
| 1.6813 |
0.94 |
300 |
1.5620 |
| 1.5613 |
1.09 |
350 |
1.5898 |
| 1.4966 |
1.25 |
400 |
1.4161 |
| 1.4831 |
1.41 |
450 |
1.4831 |
| 1.4587 |
1.56 |
500 |
1.3218 |
| 1.433 |
1.72 |
550 |
1.3529 |
| 1.33 |
1.88 |
600 |
1.2453 |
| 1.2842 |
2.03 |
650 |
1.2956 |
| 1.2807 |
2.19 |
700 |
1.1993 |
| 1.1767 |
2.34 |
750 |
1.1557 |
| 1.2134 |
2.5 |
800 |
1.1393 |
| 1.1897 |
2.66 |
850 |
1.2016 |
| 1.1784 |
2.81 |
900 |
1.1235 |
| 1.2016 |
2.97 |
950 |
1.1378 |
| 1.06 |
3.12 |
1000 |
1.0803 |
| 1.1124 |
3.28 |
1050 |
1.1145 |
| 1.1191 |
3.44 |
1100 |
1.0523 |
| 1.0819 |
3.59 |
1150 |
1.0165 |
| 1.1196 |
3.75 |
1200 |
1.0349 |
| 1.0534 |
3.91 |
1250 |
1.0441 |
| 1.0365 |
4.06 |
1300 |
1.1177 |
| 0.9853 |
4.22 |
1350 |
1.0721 |
| 0.9984 |
4.38 |
1400 |
0.9923 |
| 0.9802 |
4.53 |
1450 |
1.0079 |
| 1.04 |
4.69 |
1500 |
1.0198 |
| 1.098 |
4.84 |
1550 |
0.9788 |
| 1.079 |
5.0 |
1600 |
1.0291 |
| 1.0664 |
5.16 |
1650 |
0.9691 |
| 0.9715 |
5.31 |
1700 |
0.9380 |
| 0.9723 |
5.47 |
1750 |
1.0164 |
| 1.0019 |
5.62 |
1800 |
1.0064 |
| 0.9895 |
5.78 |
1850 |
1.0364 |
| 0.9835 |
5.94 |
1900 |
0.9848 |
| 0.994 |
6.09 |
1950 |
0.9353 |
| 0.9693 |
6.25 |
2000 |
0.9425 |
| 0.9413 |
6.41 |
2050 |
0.9173 |
| 0.9375 |
6.56 |
2100 |
0.9663 |
| 0.952 |
6.72 |
2150 |
0.8951 |
| 0.8927 |
6.88 |
2200 |
0.9099 |
| 0.8777 |
7.03 |
2250 |
0.9238 |
| 0.8976 |
7.19 |
2300 |
0.9715 |
| 0.9451 |
7.34 |
2350 |
0.9373 |
| 0.8972 |
7.5 |
2400 |
0.8959 |
| 0.9393 |
7.66 |
2450 |
1.0062 |
| 0.9 |
7.81 |
2500 |
0.8920 |
| 0.915 |
7.97 |
2550 |
0.8833 |
| 0.9018 |
8.12 |
2600 |
0.8671 |
| 0.8272 |
8.28 |
2650 |
0.9304 |
| 0.943 |
8.44 |
2700 |
0.8593 |
| 0.8667 |
8.59 |
2750 |
0.8875 |
| 0.871 |
8.75 |
2800 |
0.8457 |
| 0.9023 |
8.91 |
2850 |
0.8448 |
| 0.8733 |
9.06 |
2900 |
0.8261 |
| 0.8686 |
9.22 |
2950 |
0.8489 |
| 0.8412 |
9.38 |
3000 |
0.8244 |
| 0.8385 |
9.53 |
3050 |
0.8830 |
| 0.891 |
9.69 |
3100 |
0.8349 |
| 0.8692 |
9.84 |
3150 |
0.8672 |
| 0.8247 |
10.0 |
3200 |
0.8811 |
| 0.799 |
10.16 |
3250 |
0.8784 |
框架版本
- Transformers 4.27.4
- PyTorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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