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
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