In this chapter, I list all materials related to the trained classifier which is used to classify an image into 5 different types of boats:

• iceboat
• shrimper
• patrol boat
• fishing boat
• weather ship

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0. Trained model

• The trained model consists of two files:
  retrained_graph.pb
  retrained_labels.txt

1. Downloaded Dataset

• Training dataset (475.6 MB) Download

  Class	Number of images
  iceboat	1692
  shrimper	2380
  patrol_boat	1820
  fishing_boat	1320
  weather_ship	1101
  In total	8313

• Testing dataset (34.7 MB, 1532 images) Download

2. Test trained model

After we downloaded the trained model and Testing dataset, can use this code to test this model by type the following command in a terminal:

> python predict.py \
--models_folder='./models' \
--test_image_folder='./testing_images' \
--display_image=False

(More details about How to train an object classifier using our own images)

3. Use trained model on Android

This android demo loads our trained boat classifier and classify the camera video frame.

• Android apk file (103.4 MB): Xin_Boat_Demo.apk (I only tested this app on our ASUS NEXUS 7 Tablet.)
• Source Code (392.1 MB): tensorflow-classifier-android.zip (already contained trained model)

This figure is the screenshot:

(More details about How to use trained model in Android)

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If you do not want to know more details about how did I collect the data, you can skip the following part of this chapter.

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Image Collection

1. All training images are downloaded from ImageNet. In order to make it easier, I wrote a python script (download_image.py) to download images automatically. (PS: some time the it takes a long time to access ImageNet, which causes a time out error of the script. We need to login the ImageNet (easy to register an account) in our browser first.)
2. There are some links not accessable any more, which results in some downloaded files are not images. Thus, I wrote another python script (remove.py) to remove them. In addition, some images are error message and we need to manually remove them.
3. I used this tool to augment the training images.
4. Then, I use the method we discussed in How to train an object classifier using our own images to train the classifier.

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