[STRUCTURE] There are four parts in the project:

1. DataSet related files

   • 'ImageCollection' and 'Preprocess Dataset' folder are stores scripts used in the project.

   [Notes] These scripts are separated and each of them is used to solve one one single task.

   • 'Dataset' folder stores the final dataset used in the project.

   [Notes] Since the whole dataset is larger than storage limitation of Github, the original dataset is not stored.

2. Classification related files

   • In this part, AlexNet related files are stored. However, we won't use it in the project.

3. Object detection related files

   • 'Final Model files' folder stores the final model files which could be used in the notebook and an Android app.

4. Web related files

   • 'Web' folder stores all the files used to show the static website, which is a basic description of the project.

5. Mobile app related files

   • 'Mobile' folder stores all the files used to implement an Android app, and the outcome apk file is downloaded by: https://www.dropbox.com/sh/6izojq9hlovbky2/AADh4vxs2wzi3fomAN9tEr6oa?dl=0

[GUIDE]

1. Install Tensorflow
2. Download Tensorflow Models: https://github.com/tensorflow/models.git
3. Basic procedures:
   • Making DataSet
   • Training model using scripts in the 'Tensorflow Models'
   • Optimize model
   • Export model using scripts in the 'Tensorflow Models'
   • Use 'object_detection.ipynb' to test the result of the model

[HOW TO TEST THE MODEL]

1. Install 'Jupyter'
2. Make sure 'object_detection.ipynb' can access scripts in the 'Tensorflow Models'.
3. Execute the 'object_detection.ipynb'