Retrain-Inception

Retraining the final layer of Google's Inception (TensorFlow)

Step1: Download the pre-trained model and the required scripts.

git clone https://github.com/akashsonth/Retrain-Inception

cd Retrain-Inception

Step2: Setup the image folder

This step involves setting up the folder structure so that tensorflow can pick up the classes easily. Let’s assume that you want to train 5 new flower types, say “roses”, “tulips”, “dandelions”, “mayflower”, and “marigold”. To create the folder structure,

1. Create one folder for each flower type. The name of the folder will be the name of the class ( in this case, that particular flower).
2. Add all the images of the flowers into its respective folders. Eg; all images of roses go into the “roses” folder.
3. Add all the folders into another parent folder, say, “flowers”. At the end of this exercise, you will have the following structure: ~/flowers

~/flowers/roses/img1.jpg

~/flowers/roses/img2.jpg

...

~/flowers/tulips/tulips_img1.jpg

~/flowers/tulips/tulips_img2.jpg

~/flowers/tulips/tulips_img3.jpg

...

Note: All the images must be in jpg format

Step 3: Running the re-training script

python retrain.py --model_dir ./inception --image_dir ~/flowers --output_graph ./output --how_many_training_steps 500

Step 4: Getting the labels and output file

1. Rename the file named 'output' which is formed after Step 3 in 'Retrain-Inception' directory to 'output.pb'
2. Copy the file 'output_lables.txt' from 'tmp' folder in root directory to 'Retrain-Inception' directory
3. Rename the copied file in 'Retrain-Inception' to 'labels.txt'

Step 5: Testing the retrained model

1. Make sure the retrain_model_classifier.py is in the same folder as the retrained model and labels file.
2. Run the following command

python retrain_model_classifier.py <image_path>

Following is an example of classifying an image present in Pictures directory

python retrain_model_classifier.py /home/akshay/Pictures/test_image_flower.jpg