Keras-ICNet

[paper]

Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images. Training in progress!

Requisites

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

Issue ./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

Training

conv6_cls_categorical_accuracy conv6_cls_loss loss

Validation

val_conv6_cls_categorical_accuracy val_conv6_cls_loss val_loss

Test

Issue ./test --help for options to start a testing session, default arguments should work out-of-the-box.

Output examples

TODO

Perform class weighting