Image Classification for a City Dog Show

Project Description

Using a pre-trained image classifier to identify dog breeds. Project was completed through Udacity as part of Cognizant Gen AI Externship.

Program Instructions

Run program check_images.py in a terminal with up to 3 arguments. If the user fails to provide some or all of the 3 arguments, then the default values are used for the missing arguments.

Command Line Arguments:

  1. Image Folder as --dir with default value 'pet_images'

  2. CNN Model Architecture as --arch with default value 'vgg' (options: 'vgg', 'alexnet', 'resnet')

  3. Text File with Dog Names as --dogfile with default value 'dognames.txt'

If you want to run all 3 models, you can use the bash files in the terminal.

• run_models_batch.sh: Runs all 3 models against pictures on drive. Outputs to a .txt file.
• run_models_batch_uploaded.sh: Runs all 3 models against uploaded. Outputs to a .txt file.

Other Python

• adjust_results4_isadog.py: Script that adjusts the dictionary to determin if the classifier correctly classified images 'as a dog' or 'not a dog'.
• calculates_results_stats.py: Calculates statistics of the results using classifier's model architecture to classifying pet images.
• classifier.py: Script that was provided along with the project.
• classify_images.py: Creates classifier labels with classifier function, compares pet labels to the classifier labels, and adds the classifier label and the comparison of the labels to the results dictionary.
• get_input_args.py: Retrieves and parses the 3 command line arguments provided by the user when they run the program from a terminal window.
• get_pet_labels.py: Creates a dictionary of pet labels (results_dic) based upon the filenames of the image files.
• print_functions_for_lab_checks.py: Script that was provided along with the project.
• print_results.py: Prints summary results on the classification and then prints incorrectly classified dogs and incorrectly classified dog breeds if user indicates they want those printouts
• test_classifier.py: Script that was provided along with the project.

Program Outline

1. Time your program

   • Use the Time module to compute program runtime.

2. Get program Inputs from the user

   • Use command line arguments to get user inputs.

3. Create Pet Images Labels

   • Use the pet images filenames to create labels.
   • Store the pet image labels in a data structure (e.g., dictionary).

4. Create Classifier Labels and Compare Labels

   • Use the Classifier function to classify the images and create the classifier labels.
   • Compare Classifier Labels to Pet Image Labels.
   • Store Pet Labels, Classifier Labels, and their comparison in a complex data structure (e.g., dictionary of lists).

5. Classifying Labels as "Dogs" or "Not Dogs"

   • Classify all Labels as "Dogs" or "Not Dogs" using dognames.txt file.
   • Store new classifications in the complex data structure (e.g., dictionary of lists).

6. Calculate the Results

   • Use Labels and their classifications to determine how well the algorithm worked on classifying images.
   • Print the Results.