• Keras - U-Net
• • Part 1. Introduction
  • • Models for this repository
  • Part 2. Quick Start
  • Part 3. Train your own dataset
  • Part 4. Reference and other implement

Keras - U-Net

Part 1. Introduction

U-Net is pixel level classification, and the output is the category of each pixel, and different categories of pixels will display different colors. U-Net is often used in biomedical images, and the image data in this task is often small. Therefore, Ciresan et al. trained a convolutional neural network to predict the class tag of each pixel using the sliding window to provide the surrounding area (patch) of the pixel as input.

Models for this repository

I didn't find any pretrain weights. It's also different to other network like FCN, SegNet, doesn't have familiar backbone. U-Net Paper said it used isbi dataset, but I didn't complete reproduce U-Net which has more suitable for medical image padding manipulation. You can think of it as a simple U-Net. So I just random initialize weights to train in VOC dataset.

I still implement ISBIdataset.py. It has elastic_transform function which is a data augmentation for cell membranes described in this paper.

Dataset	MIoU	Pixel accuracy
VOC train dataset	0.9141	0.9837
VOC validation dataset	0.4472	0.8939

Part 2. Quick Start

1. Pull this repository.

git clone https://github.com/verages/UNet-keras.git

2. You need to install some dependency package.

cd U-Net-keras
pip installl -r requirements.txt

3. Download the VOC dataset(VOC SegmetationClassAug if you need).
4. Getting U-Net weights.

wget 

5. Run predict.py, you'll see the result of U-Net.

python predict.py

Input image:

Output image（resize to 320 x 320）:

Part 3. Train your own dataset

1. You should rewrite your data pipeline, Dateset where in dataset.py is the base class, such as VOCdataset.py.

class VOCDataset(Dataset):
    def __init__(self, annotation_path, batch_size=4, target_size=(320, 320), num_classes=21, aug=False):
        super().__init__(target_size, num_classes)
        self.batch_size = batch_size
        self.target_size = target_size
        self.num_classes = num_classes
        self.annotation_path = annotation_path
        self.aug = aug
        self.read_voc_annotation()
        self.set_image_info()

2. Start training.

python train.py

3. Running evaluate.py to get mean iou and pixel accuracy.

python evaluate.py
--------------------------------------------------------------------------------
Total MIOU: 0.4472
Object MIOU: 0.4243
pixel acc: 0.8939
IOU:  [0.90583348 0.61631588 0.46176812 0.32607745 0.20159544 0.33146206
 0.73923587 0.61883991 0.61589186 0.22619494 0.26843258 0.28243826
 0.47725354 0.40103127 0.50151519 0.6797911  0.20051836 0.33062457
 0.28617971 0.49376667 0.42613842]

4. If you can't connect isbi website, I also provide isbi dataset.

wget 

Part 4. Reference and other implement

• U-Net: Convolutional Networks for Biomedical Image Segmentation

• zhixuhao/unet