spatialAnticipationNetwork

Requirements

Training:

• Tensorflow (tested with version 0.12)

Evaluation:

• Matlab
• MatConvNet

Quick Start

• Prepare Cityscapes dataset: Convert background label 255 to 19
• Download models and place them inside the 'spatialAnticipationNetwork'-root folder.
• Adapt the paths in train.py
• Train the model using python train.py
• Adapt the paths in eval.py
• Predict labels on the validation set python eval.py
• Compute IoU and F1-scores by using ./matlab/evaluateAllResults.m after adapting paths

Acknowledgement

The tensorflow code in this repository was written by modifying a duplicate of DrSleep's-deeplab-tensorflow project. The Matlab evaluation scripts were written by modifying Liang-Chieh Chen's deeplab-public-ver2