Skip to content

Latest commit

 

History

History
50 lines (35 loc) · 1.69 KB

README.md

File metadata and controls

50 lines (35 loc) · 1.69 KB
Raw

unet

This repo is a Keras implementation of unet.

How to use

Requirement

  • OpenCV
  • Python 3.6
  • Tensorflow-gpu-1.8.0
  • Keras-2.2.4

train and test

Before you start training, you must make sure your dataset have the right format

If you just two classes to classify, you should set flag_multi_class equal to False and num_class=2

if you have many classes to classify, you should set flag_multi_class equal to True and num_class=number of your classes

Then you should set image type , image_color_mode and label_color_mode.

change the data path and run the train.py to train you own model and test.py to predict the test images

Results

The binary classify model is trained for 30 epochs(300 step per epoch) in Kitti dataset. After 30 epochs, calculated accuracy is about 0.989, the loss is about 0.02 Loss function for the training is basically just a binary crossentropy. image/test.png image/test_predict.png

The multi classify model is trained for 30 epochs(300 step per epoch) in Camvid dataset. After 30 epochs, calculated valid accuracy is about 0.768, the loss is about 1.43 Loss function for the training is categorical_crossentropy. image/camvid.png image/camvid_predict.png

and the loss and accuracy curve in there: image/acc&loss.png

Then you also can use label_visualization.py to visual your resut like this: image/mask.png

About

Unet is More commonly used in medical areas.

Code reference

https://github.com/wuyang0329/unet

Data reference

https://www.kaggle.com/longnguyen2306/bacteria-detection-with-darkfield-microscopy