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├── config/ <- 📂 Configuration files [.ini, .json, .yaml]
├── data/ <- 📂 Images, numpy data objects, text files
├── docs/ <- 📂 Store .md files. Used by Mkdocs for Project Documentation
├── logs/ <- 📂 Log files generated by the project during execution
├── models/ <- 📂 Model files [.h5, .pkl, .pt] - pre-trained weight files, snapshots, checkpoints
├── notebooks/ <- 📂 Jupyter Notebooks
├── references/ <- 📂 Data dictionaries, manuals, and all other explanatory materials
├── scripts/ <- 📂 Utility scripts for various project-related tasks
├── src/ <- 📂 Source code (.py files)
├── tests/ <- 📂 Unit tests for the project
├── workspaces/ <- 📂 Multi-user workspace that can be used in the case of a single machine
├── .env-template <- 🔧 Template for the .env file
├── .gitattributes <- 🔧 Standard .gitattributes file
├── .gitignore <- 📛 Standard .gitignore file
├── .pre-commit-config.yaml <- 🔧 Config file for Git Hooks
├── LICENSE <- 🪧 License file [choose your appropriate license from GitHub]
├── mkdocs.yml <- 🗞️ Base config file required for Mkdocs
├── Pipfile <- 🗃️ Most commonly used python packages
├── README.md <- 📝 Project readme
├── setup.py <- 📦️ For installing & packaging the project
└── tox.ini <- 🔧 General-purpose package configuration manager
All the packages to be installed are included in the Pipfile. For installing additional packages
* numpy <- for numerical computing and scientific computing
* scipy <- mathematical algorithms and convenience functions built on the NumPy
* pandas <- for data manipulation and analysis
* matplotlib <- plotting library
* seaborn <- data visualization library for drawing informative statistical graphics.
* scikit-learn <- machine learning library
* jupyter <- web-based interactive computing platform
* jupyter-server <- backend for Jupyter notebooks. Required when running notebooks in VS Code
* ipykernel <- interactive Python shell. Required when running notebooks in VS Code
* ipython <- provides a powerful interactive shell and a kernel for Jupyter
* isort <- sorts imports in a python file
* python-decouple <- Reads configuration/settings from .env, system environment variables
* flake8 <- Code linter (format checker)
* flake8-tabs <- Tab (and Spaces) Style Checker for flake8
* black <- Code formatter
* mypy <- Static type checker
* pre-commit <- A framework for managing and maintaining multi-language pre-commit hooks.
* pdoc3 <- Generate API documentation for Python projects
* mkdocs <- Generate Project documentation for Python projects
Due to the complexity of the problems, the models were trained using Kaggle, as it provides a more robust hardware processing environment. However, the notebooks related to these models can be found in the "notebooks" folder.
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😎 Fast-App PCO
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🤩 Streamlit App
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😎 Fast-App PCO
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🤩 Streamlit App
Open your terminal and navigate to the project directory.
Setting dolowload models
🚨 To run the application, download the required model files from this Google Drive link and place them in the models/ directory.
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├── models/ <- 📂 Model files [.h5, .pkl, .pt] - pre-trained weight files, snapshots, checkpoints
├── vgg19_001/ <- 📂 Model [.h5]
├── labels.json <- File [.json]
├── model.h5 <- File [.h5]
* Docker is required to run the application. Follow these steps:
* Install Docker if it's not already installed on your system.
* Navigate to the /app directory in the terminal.
Option 1 - 🐳 Docker build local
docker-compose up --build
🚨 If you encounter issues building the image, it likely indicates that you are missing some essential system packages for building.
Option 2 - 🐳 Pull Image from Docker
docker pull ddiazva312/object_classification
docker-compose up
http://localhost:8080/docs
http://localhost:5000/
