README.md

Setting Up Datasets

This file describes how to perform training on other datasets.

Only Pascal VOC dataset can be loaded from its original format and be outputted to Pascal style results currently.

We expect the annotations from other datasets be converted to COCO json format, and the output will be in COCO-style. (i.e. AP, AP50, AP75, APs, APm, APl for bbox and segm)

Creating Symlinks for PASCAL VOC

We assume that your symlinked datasets/voc/VOC<year> directory has the following structure:

VOC<year>
|_ JPEGImages
|  |_ <im-1-name>.jpg
|  |_ ...
|  |_ <im-N-name>.jpg
|_ Annotations
|  |_ pascal_train<year>.json (optional)
|  |_ pascal_val<year>.json (optional)
|  |_ pascal_test<year>.json (optional)
|  |_ <im-1-name>.xml
|  |_ ...
|  |_ <im-N-name>.xml
|_ VOCdevkit<year>

Create symlinks for voc/VOC<year>:

cd ~/github/maskrcnn-benchmark
mkdir -p datasets/voc/VOC<year>
ln -s /path/to/VOC<year> /datasets/voc/VOC<year>

Example configuration files for PASCAL VOC could be found here.

PASCAL VOC Annotations in COCO Format

To output COCO-style evaluation result, PASCAL VOC annotations in COCO json format is required and could be downloaded from here via http://cocodataset.org/#external.

Creating Symlinks for Cityscapes:

We assume that your symlinked datasets/cityscapes directory has the following structure:

cityscapes
|_ images
|  |_ <im-1-name>.jpg
|  |_ ...
|  |_ <im-N-name>.jpg
|_ annotations
|  |_ instanceonly_gtFile_train.json
|  |_ ...
|_ raw
   |_ gtFine
   |_ ...
   |_ README.md

Create symlinks for cityscapes:

cd ~/github/maskrcnn-benchmark
mkdir -p datasets/cityscapes
ln -s /path/to/cityscapes datasets/data/cityscapes

Steps to convert Cityscapes Annotations to COCO Format

1. Download gtFine_trainvaltest.zip from https://www.cityscapes-dataset.com/downloads/ (login required)
2. Extract it to /path/to/gtFine_trainvaltest

cityscapes
|_ gtFine_trainvaltest.zip
|_ gtFine_trainvaltest
   |_ gtFine

3. Run the below commands to convert the annotations

cd ~/github
git clone https://github.com/mcordts/cityscapesScripts.git
cd cityscapesScripts
cp ~/github/maskrcnn-benchmark/tools/cityscapes/instances2dict_with_polygons.py cityscapesscripts/evaluation
python setup.py install
cd ~/github/maskrcnn-benchmark
python tools/cityscapes/convert_cityscapes_to_coco.py --datadir /path/to/cityscapes --outdir /path/to/cityscapes/annotations

Example configuration files for Cityscapes could be found here.