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Keras Image Segmentation

Semantic Segmentation easy code for keras users.


We use cityscape dataset for training various models.

Use pretrained VGG16 weight for FCN and U-net! You can download weights offered by keras.

Tested Env

  • python 2 & 3
  • tensorflow 1.5
  • keras 2.1.4
  • opencv 3.3

File Description

File Description
train.py Train various models.
test.py Predict one picture what you want.
dataest_parser/make_h5.py Parse cityscape dataset and make h5py file.
dataest_parser/generator.py Data_generator with augmentation using data.h5
model/ Folder that contains various models for semantic segmentation
segmentation_dh/ Experiment folder for Anthony Kim(useless contents for users)
segmentation_tk/ Experiment folder for TaeKang Woo(useless contents for users)
temp/ Folder that contains various scripts we used(useless contents for users)

Implement Details

We used only three classes in the cityscape dataset for a simple implementation.

Person, Car, and Road.

Simple Tutorial

First, you have to make .h5 file with data!

python3 dataset_parser/make_h5.py --path "/downloaded/leftImg8bit/path/" --gtpath "/downloaded/gtFine/path/"

After you run above command, 'data.h5' file will appear in dataset_parser folder.

Second, Train your model!

python3 train.py --model fcn
Option Description
--model Model to train. ['fcn', 'unet', 'pspnet']
--train_batch Batch size for train.
--val_batch Batch size for validation.
--lr_init Initial learning rate.
--lr_decay How much to decay the learning rate.
--vgg Pretrained vgg16 weight path.

Finally, test your model!

python3 test.py --model fcn
Option Description
--model Model to test. ['fcn', 'unet', 'pspnet']
--img_path The image path you want to test

Todo

  • FCN
  • Unet
  • PSPnet
  • DeepLab_v3
  • Mask_RCNN
  • Evauate methods(calc mIoU)

Contact us!

Anthony Kim: artit.anthony@gmail.com

TaeKang Woo: wtk1101@gmail.com

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Image segmentation with keras. FCN, Unet, DeepLab V3 plus, Mask RCNN ... etc.

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