/Unet

Primary LanguagePython

Implementation of deep learning framework -- Unet, using Keras

The architecture was inspired by U-Net: Convolutional Networks for Biomedical Image Segmentation.

Overview

Data

This experiment is carried out for implementing semiconductor auto-mesurement. The original dataset is confidential, so It was not permitted to be provided in this repo. But for your understanding of the corresponding codes, I still kept the empty data folder.

Pre-processing

The original semiconductor image is .tiff type, for convinient using, I split the data into different .npy file, for training, validation and testing, respectively.

To do data augumentation, we used separate scripts like contrast.py, reshapre.py and some bash scripts.

Model

This deep neural network is implemented with Keras functional API, which makes it extremely easy to experiment with different interesting architectures.

Output from the network is a 400*400 which represents mask that should be learned. Sigmoid activation function makes sure that mask pixels are in [0, 1] range.

Training

The model is trained for 10 epochs.

After 10 epochs, calculated accuracy is about 0.97.

Loss function for the training is basically just a binary crossentropy

How to use

Dependencies

This tutorial depends on the following libraries:

  • Tensorflow
  • Keras >= 1.0
  • libtiff(optional)

Also, this code should be compatible with Python versions 2.7-3.5.

Define the model

  • Check out get_unet() in unet.py to modify the model, optimizer and loss function.

Train the model and generate masks for test images

  • Run python unet.py to train the model.

After this script finishes, in imgs_mask_test.npy masks for corresponding images in imgs_test.npy should be generated.