Road Segmentation from Aerial Images
Computational Intelligence Lab (Road Segmentation Project) - ETH Zürich Spring 2019
Group Name: Uncalibrated
Group Members:
- Arda Duzceker
- Jonas Hein
- Juan Lopez Fernandez
- Marilou Beyeler
Dependencies
- python 3.7.x
- tensorflow 1.13.1
- opencv 3.4.x
- numpy
- scikit-learn
- pillow
- matplotlib
- tqdm
- scipy
- pandas
- imageio
- pymaxflow (for graph-cut baseline, can be installed using the provided master branch folder)
competition-data
Please download the competition data from Kaggle and place its contents into ./competition-data folder. The empty folder structure is given for guidance purposes.
additional-data
Running additional-data/collect_data.py using additional-data/input_cities.csv parameters for number of images and city bounding boxes, extra data can be gathered. The Google Drive link to already gathered Google Maps data: https://drive.google.com/open?id=1aXjASwNVKF6bc4CWXcC_TU6Q2BfUr5Lp
baseline-graph-cut
Please install PyMaxflow library following the instructions in ./baseline-graph-cut/PyMaxflow-master/README.rst
Then running ./baseline-graph-cut/run.py without any arguments will reproduce our Graph-Cut baseline predictions.
baseline-cnn
Code adapted from https://github.com/tensorflow/models/blob/master/samples/outreach/blogs/segmentation_blogpost/image_segmentation.ipynb
Simply run whole IPython notebook ./baseline-cnn/baseline-cnn.ipynb from scratch to train our U-Net baseline with competition data only and reproduce our results.
ensemble
This folder contains training codes of individual models, ImageNet pretrained weights that we used, a log directory and prediction codes for ensemble prediction after training all the models.
runs
This log folder is initially empty, but please do not remove it. When you run one the trainings, a log folder named with the run time and date is created, all tensorboard materials, current situation of code and best checkpoint is saved here for later use.
training_mobilenetv2_based_model_with_single_spatial_pyramid
The folder contains MobilenetV2 Encoder with Spatial Pyramid Pooling and Upsampling Decoder model. Running training.py will start the training procedure described in the report. The required best checkpoint of this model is saved as ./ensemble/runs/yyyymmdd-hhmmss/mobilenetv2_based_model_best_checkpoint.hdf5
training_xception_based_model_with_two_spatial_pyramids
The folder contains Xception Encoder with Spatial Pyramid Pooling and Upsampling Decoder model. Running training.py will start the training procedure described in the report. The required best checkpoint of this model is saved as ./ensemble/runs/yyyymmdd-hhmmss/xception_based_model_with_two_pyramids_best_checkpoint.hdf5
training_xception_based_model_with_many_spatial_pyramids
The folder contains Xception Encoder with Many Spatial Pyramid Pooling Blocks and Upsampling Decoder model. Running training.py will start the training procedure described in the report. The required best checkpoint of this model is saved as ./ensemble/runs/yyyymmdd-hhmmss/xception_based_model_with_many_pyramids_best_checkpoint.hdf5
prediction
The folder contains the code necessary to perform Ensemble and Equivariant Prediction section described in the report. After training all(!) the models individually, please locate their best checkpoints in their respective log folders. Then, move/copy all of three checkpoints into ./ensemble/prediction/ folder and run ./ensemble/prediction/prediction.py
For convenience reasons, we also provide our best checkpoints with which we acquired our competition score: https://drive.google.com/open?id=1aXjASwNVKF6bc4CWXcC_TU6Q2BfUr5Lp
The ./ensemble/prediction/results folder will be created automatically and it will contain predictions for test images. Also, the submission file named "mobilenet_xceptiontwo_xceptionmany_rotation_flip_ensemble_majority_sigmoid.csv" will appear in this directory.
NOTE
If something here is not clear, we are sorry, please contact us in such a situation.