Skip to content
/ FRRN Public
forked from TobyPDE/FRRN

Full Resolution Residual Networks for Semantic Image Segmentation

License

Notifications You must be signed in to change notification settings

ilaripih/FRRN

 
 

Repository files navigation

Full-Resolution Residual Networks (FRRN)

This repository contains code to train and qualitatively evaluate Full-Resolution Residual Networks (FRRNs) as described in

Tobias Pohlen, Alexander Hermans, Markus Mathias, Bastian Leibe: Full Resolution Residual Networks for Semantic Segmentation in Street Scenes. CVPR 2017.

A pre-print of the paper can be found on arXiv: arXiv:1611.08323.

Please cite the work as follows:

@inproceedings{pohlen2017FRRN,
  title={Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes},
  author={Pohlen, Tobias and Hermans, Alexander and Mathias, Markus and Leibe, Bastian},
  booktitle={Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on},
  year={2017}
}

Demo Video

Click here to watch our video.

Installation

Install the following software packages:

  • Python 2.7 or 3.4
  • Numpy
  • Scipy
  • Scikit-Learn
  • OpenCV
  • Theano
    • Scipy
    • Scikit-Learn
  • Lasagne

You may optionally install the following library for better performance.

You can check if all dependencies are installed correctly by running the check_dependencies.py script:

$ python check_dependencies.py --cs_folder=[Your CS folder]
2017-07-26 22:17:34,945 INFO Found supported Python version 3.4.
2017-07-26 22:17:35,122 INFO Successfully imported numpy.
2017-07-26 22:17:35,184 INFO Successfully imported cv2.
2017-07-26 22:17:35,666 INFO Successfully imported sklearn.
2017-07-26 22:17:35,691 INFO Successfully imported sklearn.metrics.
2017-07-26 22:17:35,691 INFO Successfully imported scipy.
Using cuDNN version 6021 on context None
Mapped name None to device cuda: TITAN X (Pascal) (0000:02:00.0)
2017-07-26 22:17:38,760 INFO Successfully imported theano.
2017-07-26 22:17:38,797 INFO Successfully imported lasagne.
2017-07-26 22:17:38,797 INFO Theano float is float32.
2017-07-26 22:17:38,803 INFO cuDNN spatial softmax found.
2017-07-26 22:17:38,807 INFO Use Chianti C++ library.
2017-07-26 22:17:38,826 INFO Found CityScapes training set.
2017-07-26 22:17:38,826 INFO Found CityScapes validation set.

If you don't see any ERROR messages, the software should run on your machine.

Qualitatively evaluation a pre-trained model

Run the script predict.py.

$ python predict.py --help
usage: predict.py [-h] --architecture {frrn_a,frrn_b} --model_file MODEL_FILE
                  --cs_folder CS_FOLDER [--sample_factor SAMPLE_FACTOR]

Shows the predictions of a Full-Resolution Residual Network on the Cityscapes
validation set.

optional arguments:
  -h, --help            show this help message and exit
  --architecture {frrn_a,frrn_b}
                        The network architecture type.
  --model_file MODEL_FILE
                        The model filename. Weights are initialized to the
                        given values if the file exists. Snapshots are stored
                        using a _snapshot_[iteration] post-fix.
  --cs_folder CS_FOLDER
                        The folder that contains the Cityscapes Dataset.
  --sample_factor SAMPLE_FACTOR
                        The sampling factor.

Train a new model

Run the train.py script.

$ python train.py --help
usage: train.py [-h] --architecture {frrn_a,frrn_b,frrn_c} --model_file
                MODEL_FILE --log_file LOG_FILE --cs_folder CS_FOLDER
                [--batch_size BATCH_SIZE]
                [--validation_interval VALIDATION_INTERVAL]
                [--iterator {uniform,weighted}] [--crop_size CROP_SIZE]
                [--learning_rate LEARNING_RATE]
                [--sample_factor SAMPLE_FACTOR]

Trains a Full-Resolution Residual Network on the Cityscapes Dataset.

optional arguments:
  -h, --help            show this help message and exit
  --architecture {frrn_a,frrn_b}
                        The network architecture type.
  --model_file MODEL_FILE
                        The model filename. Weights are initialized to the
                        given values if the file exists. Snapshots are stored
                        using a _snapshot_[iteration] post-fix.
  --log_file LOG_FILE   The log filename. Use log_monitor.py in order to
                        monitor training progress in the terminal.
  --cs_folder CS_FOLDER
                        The folder that contains the Cityscapes Dataset.
  --batch_size BATCH_SIZE
                        The batch size.
  --validation_interval VALIDATION_INTERVAL
                        The validation interval.
  --iterator {uniform,weighted}
                        The dataset iterator type.
  --crop_size CROP_SIZE
                        The size of crops to extract from the full-resolution
                        images. If 0, then now crops will be extracted.
  --learning_rate LEARNING_RATE
                        The learning rate to use.
  --sample_factor SAMPLE_FACTOR
                        The sampling factor.

Monitor training

Start a new notebook server and open training_monitor.ipynb.

License

See LICENSE (MIT).

Copyright

Copyright (c) 2017 Google Inc.

Copyright (c) 2017 Toby Pohlen

About

Full Resolution Residual Networks for Semantic Image Segmentation

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 51.2%
  • Jupyter Notebook 48.8%