Code for Soft labelling for semantic segmentation: Bringing coherence to label down-sampling, currently under review.

Installation

• The code is tested with pytorch 1.3 and python 3.6
• You can use ./Dockerfile to build an image.

Weights

• Create a directory where you can keep large files. Ideally, not in this directory.

  > mkdir <large_asset_dir>

• Update __C.ASSETS_PATH in config.py to point at that directory

  __C.ASSETS_PATH=<large_asset_dir>

• Download pretrained weights from Supplementary Material and put into <large_asset_dir>/seg_weights

Download/Prepare Data

If using Cityscapes, download Cityscapes data, then update config.py to set the path:

__C.DATASET.CITYSCAPES_DIR=<path_to_cityscapes>

If using Mapillary, download Mapillary data, then update config.py to set the path:

__C.DATASET.MAPILLARY_DIR=<path_to_mapillary>

Running the code

The instructions below make use of a tool called runx, which we find useful to help automate experiment running and summarization. For more information about this tool, please see runx. In general, you can either use the runx-style commandlines shown below. Or you can call python train.py <args ...> directly if you like.

Run inference on Cityscapes

> python -m runx.runx scripts/eval_cityscapes.yml -i

Dump images for Cityscapes

> python -m runx.runx scripts/dump_cityscapes.yml -i

This will dump network output and composited images from running evaluation with the Cityscapes validation set.

Run inference and dump images on a folder of images

Modify the scripts/dump_folder.yml to point to the image folder to run inference:

eval_folder:<path_to_image_folder>

Run:

> python -m runx.runx scripts/dump_folder.yml -i

This will dump network output, composited images and attention maps from running evaluation with the Cityscapes validation set.

Train a model

Train cityscapes

> python -m runx.runx scripts/train_cityscapes.yml -i

The first time this command is run, a centroid file has to be built for the dataset. It'll take about 10 minutes. The centroid file is used during training to know how to sample from the dataset in a class-uniform way.

This training run should deliver a model that achieves ~84.4 IOU.

Code based on

Baseline code comes from NVIDIA semantic segmentation framework