Using fully convolutional networks for semantic segmentation with caffe for the cityscapes dataset

Using fully convolutional networks for semantic segmentation (Shelhamer et al.) with caffe for the cityscapes dataset

How to get started

• Download the cityscapes dataset and the vgg-16-layer net
• Modify the images in the dataset with cut_images.py or downscale_images.py for less resource demanding training and evaluation
• Create the 32 pixel stride net with net_32.py
• Modify the paths in train.txt and val.txt (first line: path to training/validation images, second line: path to annotations)
• Start training with solve_start.py
• Run evaluate_models.py to evaluate your model or create_eval_images.py to create images with pixel label ids

Sources

Fully Convolutional Models for Semantic Segmentation:

Shelhamer, Evan, Jonathon Long, and Trevor Darrell. "Fully Convolutional Networks for Semantic Segmentation." PAMI, 2016, URL http://fcn.berkeleyvision.org

Cityscapes Dataset (Semantic Understanding of Urban Street Scenes):

Cordts, Marius, et al. "The cityscapes dataset." CVPR Workshop on The Future of Datasets in Vision. 2015, URL https://www.cityscapes-dataset.com

Caffe Deep Learning Framework:

Jia, Yangqing, et al. "Caffe: Convolutional architecture for fast feature embedding." Proceedings of the 22nd ACM international conference on Multimedia. ACM, 2014, URL http://caffe.berkeleyvision.org