/Pneumonia-Classification

Pneumonia-Classification Model (Pytorch)

Primary LanguagePython

Pneumonia Classifier

used data : https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

test samples:

Quick Start

  1. dependencies: matplotlib, torch, torchvision, sklearn, seaborn, numpy, imgaug, PIL, cv2
  2. download the code and create a project.
  3. download data to "./data/train" , "./data/val" , "./data/test"
  4. label your data like "./data/train/class1", "./data/train/class2..."....
  5. run 'train.py'
  6. you can edit the parameters in 'parameters.py'

User Setting Variables

train.py

  1. mode
    • 'new': create an archive on a new branch.
    • 'overlay': training continues in the target existing branch.
    • 'load': training continues on a new branch.
  2. netend
    • Variable defining terminal linear classifier for transfer-learning (you can disable)
    • See networks/nets.py
  3. network
    • Variable defining pre-trained Network
  4. loss_f, optimizer
  5. load_branch, load_num: if you use 'overlay' or 'load' mode.
  6. transform_set

parameters.py

  1. model_name
  2. params: parameters used in training and validation
  3. test_params: parameters used in test
  4. user_setting, permission

test.py

  1. branch_num, epoch_num: same in load_branch and load_num in 'train.py'
  2. netend, model: same in netend and network in 'train.py'

ensemble.py

code for model ensemble. you have to pay attention to the order and fill the lists: <'model_list', 'branch_nums', 'epoch_nums', 'models'>

  1. model_list: names of the model in the weight files
  2. branch_nums: branch address of target weight file
  3. epoch_num: epoch number of target weight file
  4. models: model definition list
  5. transform_set