Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images. Training in progress!
- Python 3.6.3
- Keras 2.1.1 with Tensorflow backend
- A dataset, such as Cityscapes or Mapillary (Mapillary was used in this case).
./train --help for options to start a training session, default arguments should work out-of-the-box.
You need to place the dataset following the next directory convention:
. ├── mapillary | ├── training | | ├── images # Contains the input images | | └── instances # Contains the target labels | ├── validation | | ├── images | | └── instances | └── testing | | └── images
These are the results of training for 300 epochs
./train --epochs 300
./test --help for options to start a testing session, default arguments should work out-of-the-box.
- Perform class weighting