BloomSage Machine Learning & MLOps Backend Component

---

Project Structure

.
├── font/
├── notebooks/
│   ├── images/
│   ├── Step1.EDA.ipynb
│   ├── Step2.DataPrep.ipynb
│   └── Step3.Classifier-BaselineModel.ipynb
├── scraping/
│   └── scrape.py
├── classify.py
├── recommend.py
├── requirements.txt
├── .gitignore
├── project-statement.md
├── README.md
└── LICENSE

1. font/: This folder contains the fonts used in our client script's GUI mode.
2. notebooks/: This folder contains all Jupyter Notebooks for this project and their exported plots in notebooks/images/.
3. scrape/: This folder contains a scraping script to get more images from the internet for our dataset. All downloaded images will also be in this folder.
4. classify.py: Client script for classifying flower images using trained models.
5. recommend.py: Client script for recommending flower images using trained models.
6. requirements.txt: Text file for pip installation of necessary packages for development environment.
7. .gitignore: This file contains ignore VCS ignore rules.
8. README.md: A text file containing useful reference information about this project, including how to run the algorithm.
9. LICENSE: MIT

Additionally, these folders will be created during dataset fetching and model training:

1. data/: This folder contains out datasets.
2. log/: This folder contains training logs exported from training our models.
3. models/: This folder contains trained models exported after training.

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Getting Started 🚀

Clone this repository:

git clone https://github.com/rmit-denominator/bloomsage-ml.git

Development Environment

To set up the necessary packages for this project, run:

pip install -r requirements.txt

Refer to requirements.txt for package dependencies and their versions.

NOTE: It is recommended that you use a Python virtual environment to avoid conflict with your global packages, and to keep your global Python installation clean. This is because we require specific versions of Numpy, Tensorflow and Keras in our code to maintain backward compatibility and compatibility between trained models and client code.

Download Dataset

The dataset for this project is available on Kaggle. Follow these steps to download and set it up for training and testing:

1. Navigate to project's root directory.

2. Clean all existing files in the data/ folders (if exists) before downloading or updating this dataset:

   rm -r ./data/*

3. Download and extract contents of the .zip from Kaggle into data/raw folder.

   Alternatively, use the Kaggle CLI:

   kaggle datasets download -d miketvo/rmit-flowers -p ./data/raw/ --unzip

   The resulting folder structure should look like this:

   .
   ├── data/
   │   └── raw/
   │       ├── Baby/
   │       ├── Calimerio/
   │       ├── Chrysanthemum/
   │       ...
   │       └── Tana/
   │
   ...
   

   where each folder corresponds to a flower class, and contains images of only that class.

4. Setup for training and testing: Run notebooks/Step2.DataPrep.ipynb and Step5.Recommender.ipynb. They will clean, process, and split the raw dataset and the resulting train and test set into data/train/ and data/test/, respectively. They will also generate a database for our image recommendation system in data/recommender-database/, along with data/recommender-database.csv that contains the feature vectors for all images in the recommender database, in addition to exporting two helper models models/fe-cnn and models/clu-kmeans.model for the recommendation system. Note: Clean these folders and files before you run these two notebook:

   rmdir -r ./data/train
   rmdir -r ./data/test
   rmdir -r ./data/recommender-database
   rm ./data/recommender-database.csv

   Important: Clean and rerun this step every time you modify the raw dataset to get the most updated train dataset, test dataset, and recommender database.

Training

Skip this step if you just want to use on of the pre-trained model packages available from Releases.

• Run each Jupyter Notebook in notebooks/ in their prefixed order starting Step1., Step2., Step3., and so on, one file at a time.
• Skip Step2.DataPrep.ipynb if you have already run it after downloading the raw dataset in the step above.
• Skip Step5.Recommender.ipynb if you have already run it after downloading the raw dataset in the step above.
• The resulting models are exported into models/ folder. Their training logs are stored in log/ folder.

Note: Beware: any existing model with conflicting name in models/ will be replaced with newly trained models.

Using Trained Models

If you are using one of our pre-trained model packages, download your desired version from Releases (.zip archives) and extract its contents into this project's root directory using your preferred zip program. Make sure to check and clean models/ folder (if exists) to avoid naming conflict with existing trained model before the extraction.

These trained models can then be loaded into your code with:

import tensorflow as tf

model = tf.keras.models.load_model('path/to/model')

Additionally, two Python files, classify.py and recommend.py, are provided as simple front-ends to our trained model. You can either run them as standalone script in the terminal or import them as Python module in your own Python script or Jupyter Notebook to programmatically classify multiple images and recommend similar images for each of them.

To use them as standalone script, see instruction below:

On your terminal, make sure that you have the environment activated for the client script to have access to all required packages:

• Python Virtualenv:

  ./venv/Scripts/activate

• Conda:

  conda activate ./envs

Classifying Flower Images

Use the classify.py client script. Its syntax is as follows:

usage: classify.py [-h] -f FILE [-c CLASSIFIER] [-g] [-v {0,1,2}]

options:
  -h, --help                                show this help message and exit
  -f FILE, --file FILE                      the image to be classified
  -c CLASSIFIER, --classifier CLASSIFIER    the machine learning model used for classification, defaults: models/clf-cnn
  -g, --gui                                 show classification result using GUI
  -v {0,1,2}, --verbose-level {0,1,2}       verbose level, default: 0

Example use:

$ python ./classify.py -f path/to/your/your/image.png -c ./models/clf -v=1
Image image.png is classified as "Chrysanthemum" (model: "clf")

It also has a rudimentary GUI mode using Matplotlib, which will display the image with a caption of what flower type it is classified as:

python ./classify.py --gui -f path/to/your/your/image.png -m ./models/clf

Note: Alternatively, you can import its classify.classify() function into your own script or notebook to programmatically classify multiple images (see its docstring for instruction on how to use).

Recommending Flower Images

Use the recommend.py client script. Its syntax is as follows:

usage: recommend.py [-h] -f FILE [-d DATABASE] [-c CLASSIFIER] [-e FEATURE_EXTRACTOR] [-k CLUSTERING_MODEL] [-n NUM]

options:
  -h, --help                                                        show this help message and exit
  -f FILE, --file FILE                                              reference image
  -d DATABASE, --database DATABASE                                  the database containing the images to be recommended, default: data/recommender-database
  -c CLASSIFIER, --classifier CLASSIFIER                            the machine learning model used for image classification, default: models/clf-cnn
  -e FEATURE_EXTRACTOR, --feature-extractor FEATURE_EXTRACTOR       the machine learning model used for image feature extraction, default: models/fe-cnn
  -k CLUSTERING_MODEL, --clustering-model CLUSTERING_MODEL          the machine learning model used for image clustering, default: models/clu-kmeans.model
  -n NUM, --num NUM                                                 number of recommendations, default: 10

Example:

python ./recommend.py -f path/to/your/your/image.png

When executed, the code above will display 10 similar flower images (GUI mode) of the same type, taken from the recommender database in data/recommender-database/, based on your reference image, using the default classifier, feature extractor, and clustering model