How to train an object classifier using our own images

1. Info:

I prepared two python scripts (retrain.py, predict.py) for this task.

• retrain.py: used to train the classifier.
• predict.py: used to load the trained model and test on new images.

2. Prepare training and testing data:

• Training Data: Let’s assume that we have two classes, namely “cat” and “dog”. We just need to make sure that there are two sub-folders in “training_images” folder. Each sub-folder only consists of its own images.

  |- training_images
      |- cat
          |- image_0.jpg
          |- image_1.jpg
          ...
      |- dog
          |- image_2.jpg
          |- image_3.jpg
          ...
  

• Testing Data: All testing images are put in one folder, e.g. “testing_images”.

  |- testing_images
      |- image_0.jpg
      |- image_1.jpg
      |- image_2.jpg
      |- image_3.jpg
      ...
  

3. Train a classifier

Assume that both “training_images” and “testing_images” are in this folder:

> ls
models/ predict.py  retrain.py  testing_images/  training_images/

Then, we can type the following command to starting training process:

> python retrain.py \
--bottleneck_dir=./bottlenecks \
--how_many_training_steps=50000 \ 
--model_dir=./inception \
--output_graph=./models/retrained_graph.pb \
--output_labels=./models/retrained_labels.txt \
--summaries_dir=./retrain_logs \
--validation_batch_size=5000 \
--image_dir=training_images # this is the folder of training data

After this training process finished, it saves the trained model in “./models”.

> ls models
retrained_graph.pb    retrained_labels.txt

4. Predict new images using trained model

Type the following command in a terminal to run the testing code:

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