Developing a model to detect rotting meat in supermarkets
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── flask_demo <- A web demo for this project created using flask
│ ├── models <- Contains resnet 50-fe model binary
│ ├── screenshots <- Screenshots of demo web pages
│ ├── static <- css and static resources
│ └── Procfile <- Process file to run flask server
│ └── README.md <- Description of flask demo
│ └── app.yaml <- Config file for environment setup
│ └── main.py <- Main flask file for demo
│ └── requirements.txt <- Package installation
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks used for modelling, predictions and experiments
│ └── 1.0-EDA-and-preprocessing.ipynb
│ └── 2.0-image-segmentation.ipynb
│ └── 3.0-resnet_model_evaluation.ipynb
│ └── 4.0-resnet-LIME-Explanation.ipynb
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ ├── evaluation <- Scripts for model evaluation
│ │ └── misclassification_cost.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ └── train_resnet.py
│ │ └── predict_resnet.py
│ │ └── resnet_pipe.sh
│ │ └── resnet_predict.sh
│ │
│ └── utils <- Scripts for utilitity functions
│ │ └── manage_constants.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Team 09 Sagiraju Bhargav: A0262798J | Nathaniel Nartea Casanova: A0262708B | Manan Lohia:A0262838U | Lam Ivan Chuen Chun:A0262765W | Toshinori Yoshiyasu:A0262738W
All Rights Reserved.
Project based on the cookiecutter data science project template. #cookiecutterdatascience