UNet-Tensorflow

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About

Reference paper: U-Net: Convolutional Networks for Biomedical Image Segmentation

A brief UNet tensorflow implementation. It can work well on our dataset, see images below. If data augmentation and more strategies are added, the performance will be better.

• You just need to config the config.py to fit your own datast, see Dataset. When the configuration is finished, you can just run and test the model.
• The code will be updated with namescope, tfrecord, and more summaries.

Environment

• Anaconda(python 2.7)
• Tensorflow 1.10

Dataset

The dataset can be organized as follows:

|-- data_path
        |-- img_dir_name
        |-- annotation_dir_name
        |-- train_list_file
        |-- trainval_list_file

Train

python train.py

Test

python predict.py