This project allows you to create a multiclass classifier for images with deep learning. The TensorFlow framework is used for the computations.
The necessary Python packages are listed on requirements.txt. Training a deep neural network is done with the train.py file.
This project reads data from directories organized as follow:
data_train/class_1/[jpeg images from class_1]
/class_2/[jpeg images from class_2]
.....
/class_n/[jpeg images from class_n]
To create a classifier by training a deep neural network:
train.py -paths "data_train" -nc 3 -reg 0.0001 -dp .4 -s "training1" -bp "OUTPUT_PATH" -bs 64
In which -paths contains the training set, -nc controls the number of classes, -reg controls the L2 regularization factor, -dp controls the dropout value.
Note: the number of classes set with the -nc argument and the number of classes on the training set must be strictly identical.
A new directory is created on the -bp directory named with the training parameter plus the -s string. This directory contains the trained models and some metrics for tensorboard.
It's possible to test the trained models on a test set with the -tp option. The test set's directory should be organized like the train set directory.
- load_images.py
- Initialization of the queues to load images and perform data augmentation
- train.py
- Train models for binary classification
- eval.py
- Evaluation of trained models
- inference.py
- Simple class to perform inference
- initializers.py
- Helper functions to initialize networks
- models.py
- Implementation of the DL model