https://github.com/0eix/deepclassifier

DeepClassify - A Versatile Command Line Image Classifier
https://github.com/0eix/deepclassifier

classification classifier cli cnn command-line-app deep-learning numpy pandas pillow python pytorch torchvision transfer-learning

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DeepClassify - A Versatile Command Line Image Classifier

• Host: GitHub
• URL: https://github.com/0eix/deepclassifier
• Owner: 0eix
• License: mit
• Created: 2021-10-22T18:21:32.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2023-06-04T11:55:02.000Z (over 1 year ago)
• Last Synced: 2024-04-18T04:20:33.886Z (7 months ago)
• Topics: classification, classifier, cli, cnn, command-line-app, deep-learning, numpy, pandas, pillow, python, pytorch, torchvision, transfer-learning
• Language: Python
• Homepage:
• Size: 1.35 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Flower Image Classifier: An AI Programming Project

This project is part of the Udacity AI Programming with Python Nanodegree, where a deep learning model is developed for identifying 102 different species of flowers based on their images. The project is divided into two parts: 

1. Designing and training the model using Jupyter notebook.

2. Turning the trained model into a command-line application that can train on any set of images, and make predictions on new images.

The final command-line application allows customization of various aspects including CNN architecture selection, setting hyperparameters, choosing between GPU/CPU, and saving the training to a checkpoint to resume later.

## File Structure:

Here is an overview of the purpose and usage of each file in this project.

1. **cli_utils.py**: Contains helper functions for parsing command-line arguments. This aids in customizing the application's functionality like selecting the CNN architecture, setting hyperparameters, deciding whether to use GPU, and more.

2. **data_utils.py**: Responsible for data preprocessing and loading tasks. It includes functions to load image datasets from a directory, split them into training, validation and testing sets, and apply necessary transformations to prepare data for the model.

3. **device_utils.py**: Includes utility functions for device selection. It helps to identify whether a GPU is available and should be used for training and inference.

4. **model_utils.py**: Contains functions to build and load the deep learning model. This includes creating a classifier using one of the pre-trained models (AlexNet, VGG, DenseNet, etc.), and loading a checkpoint to resume training.

5. **predict.py**: This script uses the trained model to make predictions on new images. It processes the input image, performs inference using the model, and outputs the top K most likely classes along with their probabilities.

6. **train.py**: Contains the functionality required for training the model. It defines the training loop, validation loop, and handles saving the model's state to a checkpoint after each epoch.

## How to Run:

You can train a new network on a data-set with `train.py` and predict the class for an input image with `predict.py`. Run `python train.py -h` and `python predict.py -h` to see the available command-line options for each script.

## Requirements:

The application is built using Python and requires PyTorch, NumPy, and Pillow libraries.

Remember to install these dependencies before running the application.