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Image Classification Pipeline

Introduction to Modules :

  • projects : contains project folders
  • main.py : triggers training, testing and prediction
  • prepare_dataset : contains code to read .csv, .json, .txt files and finally convert it into train & test dataset
  • preprocessing.py : contains code to preprocess the dataset
  • config.py : contains all the project configurations and stays inside every independent project directory
  • model.py : contains various models for training, save and returns compiled model to train.py
  • train.py : fit the selected model
  • test.py : test the trained model
  • predict.py : make predictions on explicitly provided inputs
  • utils.py : contains common utilities & functions which are in common use
  • validation.py : valdiate requirements before training the model

Projects :

  • This is the home directory for all the image classification projects.

projects/{project_name}/

  • This is dedicated directory for a project
  • The name of the project must be from pool of alpha-numeric characters and symbols ('_', '-').

Sub-directories & files needed for a project :

config.py : projects/{project name}/config.py

  • stores model configurations of the particular project

dataset directory : projects/{project name}/dataset

  • contains dataset in following sub-directories :

    • train : contains training dataset ----> projects/{project name}/dataset/test

    • test : contains testing dataset ----> projects/{project name}/dataset/train

  • there must be sub-directories for each class of data containing the corresponding images. The names of the sub-directories must be the names of their respective classes

  • we will be using ImageDataGenerator, available in keras to train our model on the available data, this way the process becomes much simpler in terms of code.

model directory : projects/{project name}/model

  • contains saved models, weights and tensorboard logs in following sub-directories :

    • saved models : contain saved models ----> projects/{project name}/model/saved models

    • weights : contain weight files ----> projects/{project name}/model/weights

    • tblogs : contain tblogs ----> projects/{project name}/model/tblogs

  • these directories must be created before starting training process

  • all the data, config, logs, etc should be inside this folder only for this particular project

  • this whole directory should be handle with care, all learning is stored inside this folder

predict directory : projects/{project name}/predict

  • contains images for prediction purpose in the following sub-directories :

    • input : contains input images ----> projects/{project name}/predict/input

    • output : - contains output images generated by prediction phase code ----> projects/{project name}/predict/output

Project Execution Command :

For Training :

  • python main.py train {project_name} (Initial model training)
  • python main.py train {project_name} resume (Resume last training model)
  • python main.py train {project_name} example_model.h5 (Resume training of explicitly called model)

~ All these commands perform model training

For Testing :

  • python main.py test {project_name} ~ This command performs testing on provided testing dataset

For Prediction :

  • python main.py predict {project_name} ~ This command make predictions on the provided data inputs in predict/input directory

To View Tensorboard :

  • tensorboard --logdir projects/{project_name}/model/tblogs

Requirements :

  • python3
  • keras
  • fire
  • cv2
  • PIL
  • numpy
  • pandas
  • matplotlib
  • importlib
  • tqdm
  • uuid
  • shutil
  • split_folders