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classify-dogbreeds-pytorch

Project Description

This repository contains coding files that will help us determine which CNN model architecture performs best at classifying a dog and its various breeds. This project was completed as part of AI Programming with Python Nanodegree program (Udacity).

Udacity GitHub repository

Files Needed to run check_images.py locally

The following files and folders need to be put in the same folder as the check_images.py python program on your local computer.

  • pet_images (folder of 40 pet image)
  • uploaded_images (a folder you will have to create to hold your uploaded images in that section of the project)
  • classifier.py (classifier function you will be using to classify the images)
  • dognames.txt (file that contains all the valid dog names from the classifier function and the pet image files)
  • imagenet1000_clsid_to_human.txt (dictionary that converts the classifier function ids to text labels)
  • adjust_results4_isadog.py (a program that contains the adjust_results4_isadog function )
  • calculates_results_stats.py (a program that contains the calculates_results_stats function)
  • classify_images.py (a program that contains the classify_images function )
  • get_input_args.py (a program that contains the get_input_args function )
  • get_pet_labels.py (a program that contains the get_pet_labels function )
  • print_results.py (a program that contains the print_results function )
  • run_models_batch.sh (a bash script that will run check_images.py sequentially for all 3 model architectures and output their results to text files - on Unix/Linux/OSX/Project Workspace from a terminal window)
  • run_models_batch.bat (a batch script that will run check_images.py sequentially for all 3 model architectures and output their results to text files - on Windows from the Anaconda Prompt window)
  • run_models_batch_uploaded.sh (a bash script that will run check_images.py sequentially for all 3 model architectures on the uploaded images folder and output their results to text files - on Unix/Linux/OSX/Project Workspace from a terminal window)
  • run_models_batch_uploaded.bat (a batch script that will run check_images.py sequentially for all 3 model architectures on the uploaded images folder and output their results to text files - on Windows from the Anaconda Prompt window)
  • test_classifier.py (an example program that demonstrates how to use the classifier function)
  • print_functions_for_lab_checks.py (a program that contains functions that will allow you to check your code)

Running the batch files locally

In anaconda prompt, execute run_models_batch or run_models_batch_uploaded file that will compute check_images.py for all CNN architectures. For windows use .bat and not .sh files

Dependencies

Each directory has a requirements.txt describing the minimal dependencies required to run the notebooks in that directory.

pip

To install these dependencies with pip, you can issue pip3 install -r requirements.txt.