omnigeeker / Mlnd_distracted_driver_detection
基于深度学习的驾驶员状态检测，不仅仅可以识别出疲劳驾驶，还能够识别出各种各样的状态
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驾驶员状态检测
描述
使用深度学习方法检测驾驶员的状态。
- 输入：一张彩色图片
- 输出：十种状态的概率
状态列表：
- c0: 安全驾驶
- c1: 右手打字
- c2: 右手打电话
- c3: 左手打字
- c4: 左手打电话
- c5: 调收音机
- c6: 喝饮料
- c7: 拿后面的东西
- c8: 整理头发和化妆
- c9: 和其他乘客说话
数据
此数据集可以从 kaggle 上下载。Distracted Driver Detection
如果你下载有困难，可以点这里:百度云
报告说明
- 开题报告: proposal.pdf
- 毕业项目报告: capstone.pdf
代码说明，依次执行以下步骤：
1. 拆分数据集代码
splite_valid.py
2. 基准模型代码
keras-vgg16-visual-finetune.ipynb
3. 单模型代码
keras-resnet50-visual-finetune.ipynb
keras-inceptionV3-visual-finetune.ipynb
keras-xception-visual-finetune.ipynb
4. 混合模型代码
生成混合模型的输入；write_bottleneck_with_fine_tune.py
最终模型执行代码：main-finetune.ipynb
下面是废弃的代码，共参考
不做finetune的 生成混合模型的输入：write_bottleneck.py
不做finetune的 最终混合模型代码：main-without-finetune.ipynb
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