迁移学习的介绍和案例
案例迁移学习利用数据,任务和模型之间的相似性, 都是分类问题在旧的领域学习过或训练好的模型应用于新的领域进行训练模型地址:models/research/slim at master · tensorflow/models · GitHubfine tuning, 微调调整模型参数不需要过多调整调整模型结构,微微调整其中Pre_trained:预训练模型fine tuning:微调后的模型预训练模
迁移学习

- 利用数据,任务和模型之间的相似性, 都是分类问题
- 在旧的领域学习过或训练好的模型
- 应用于新的领域进行训练

模型地址:models/research/slim at master · tensorflow/models · GitHub
fine tuning, 微调
- 调整模型参数不需要过多调整
- 调整模型结构,微微调整
- 其中Pre_trained:预训练模型
- fine tuning:微调后的模型
预训练模型:
|
Model |
TF-Slim File |
Checkpoint |
Top-1 Accuracy |
Top-5 Accuracy |
|
69.8 |
89.6 |
|||
|
73.9 |
91.8 |
|||
|
78.0 |
93.9 |
|||
|
80.2 |
95.2 |
|||
|
80.4 |
95.3 |
|||
|
75.2 |
92.2 |
|||
|
76.4 |
92.9 |
|||
|
76.8 |
93.2 |
|||
|
^ |
75.6 |
92.8 |
||
|
^ |
77.0 |
93.7 |
||
|
^ |
77.8 |
94.1 |
||
|
79.9* |
95.2* |
|||
|
71.5 |
89.8 |
|||
|
71.1 |
89.8 |
|||
|
70.9 |
89.9 |
|||
|
59.1 |
81.9 |
|||
|
41.5 |
66.3 |
|||
|
74.9 |
92.5 |
|||
|
71.9 |
91.0 |
|||
|
# |
74.0 |
91.6 |
||
|
# |
82.7 |
96.2 |
||
|
82.9 |
96.2 |
|||
|
74.2 |
91.9 |
|
Model |
Size |
Top-1 Accuracy |
Top-5 Accuracy |
Parameters |
Depth |
|
88 MB |
0.790 |
0.945 |
22,910,480 |
126 |
|
|
528 MB |
0.713 |
0.901 |
138,357,544 |
23 |
|
|
549 MB |
0.713 |
0.900 |
143,667,240 |
26 |
|
|
99 MB |
0.749 |
0.921 |
25,636,712 |
168 |
|
|
92 MB |
0.779 |
0.937 |
23,851,784 |
159 |
|
|
215 MB |
0.803 |
0.953 |
55,873,736 |
572 |
|
|
16 MB |
0.704 |
0.895 |
4,253,864 |
88 |
|
|
14 MB |
0.713 |
0.901 |
3,538,984 |
88 |
|
|
33 MB |
0.750 |
0.923 |
8,062,504 |
121 |
|
|
57 MB |
0.762 |
0.932 |
14,307,880 |
169 |
|
|
80 MB |
0.773 |
0.936 |
20,242,984 |
201 |
|
|
23 MB |
0.744 |
0.919 |
5,326,716 |
- |
|
|
343 MB |
0.825 |
0.960 |
88,949,818 |
- |
过程


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