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

File Description

File Description Train various models. Predict one picture what you want.
dataest_parser/ Parse cityscape dataset and make h5py file.
dataest_parser/ 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/ --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 --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 --model fcn
Option Description
--model Model to test. ['fcn', 'unet', 'pspnet']
--img_path The image path you want to test


Contact us!

Anthony Kim:

TaeKang Woo: