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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.
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) |
We used only three classes in the cityscape dataset for a simple implementation.
Person, Car, and Road.
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 |
Anthony Kim: artit.anthony@gmail.com
TaeKang Woo: wtk1101@gmail.com
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