## Prepare datasets It is recommended to symlink the dataset root to `$MMSEGMENTATION/data`. If your folder structure is different, you may need to change the corresponding paths in config files. ```none mmsegmentation ├── mmseg ├── tools ├── configs ├── data │ ├── cityscapes │ │ ├── leftImg8bit │ │ │ ├── train │ │ │ ├── val │ │ ├── gtFine │ │ │ ├── train │ │ │ ├── val │ ├── VOCdevkit │ │ ├── VOC2012 │ │ │ ├── JPEGImages │ │ │ ├── SegmentationClass │ │ │ ├── ImageSets │ │ │ │ ├── Segmentation │ │ ├── VOC2010 │ │ │ ├── JPEGImages │ │ │ ├── SegmentationClassContext │ │ │ ├── ImageSets │ │ │ │ ├── SegmentationContext │ │ │ │ │ ├── train.txt │ │ │ │ │ ├── val.txt │ │ │ ├── trainval_merged.json │ │ ├── VOCaug │ │ │ ├── dataset │ │ │ │ ├── cls │ ├── ade │ │ ├── ADEChallengeData2016 │ │ │ ├── annotations │ │ │ │ ├── training │ │ │ │ ├── validation │ │ │ ├── images │ │ │ │ ├── training │ │ │ │ ├── validation │ ├── CHASE_DB1 │ │ ├── images │ │ │ ├── training │ │ │ ├── validation │ │ ├── annotations │ │ │ ├── training │ │ │ ├── validation │ ├── DRIVE │ │ ├── images │ │ │ ├── training │ │ │ ├── validation │ │ ├── annotations │ │ │ ├── training │ │ │ ├── validation │ ├── HRF │ │ ├── images │ │ │ ├── training │ │ │ ├── validation │ │ ├── annotations │ │ │ ├── training │ │ │ ├── validation │ ├── STARE │ │ ├── images │ │ │ ├── training │ │ │ ├── validation │ │ ├── annotations │ │ │ ├── training │ │ │ ├── validation ``` ### Cityscapes The data could be found [here](https://www.cityscapes-dataset.com/downloads/) after registration. By convention, `**labelTrainIds.png` are used for cityscapes training. We provided a [scripts](https://github.com/open-mmlab/mmsegmentation/blob/master/tools/convert_datasets/cityscapes.py) based on [cityscapesscripts](https://github.com/mcordts/cityscapesScripts) to generate `**labelTrainIds.png`. ```shell # --nproc means 8 process for conversion, which could be omitted as well. python tools/convert_datasets/cityscapes.py data/cityscapes --nproc 8 ``` ### Pascal VOC Pascal VOC 2012 could be downloaded from [here](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar). Beside, most recent works on Pascal VOC dataset usually exploit extra augmentation data, which could be found [here](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz). If you would like to use augmented VOC dataset, please run following command to convert augmentation annotations into proper format. ```shell # --nproc means 8 process for conversion, which could be omitted as well. python tools/convert_datasets/voc_aug.py data/VOCdevkit data/VOCdevkit/VOCaug --nproc 8 ``` Please refer to [concat dataset](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/tutorials/new_dataset.md#concatenate-dataset) for details about how to concatenate them and train them together. ### ADE20K The training and validation set of ADE20K could be download from this [link](http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip). We may also download test set from [here](http://data.csail.mit.edu/places/ADEchallenge/release_test.zip). ### Pascal Context The training and validation set of Pascal Context could be download from [here](http://host.robots.ox.ac.uk/pascal/VOC/voc2010/VOCtrainval_03-May-2010.tar). You may also download test set from [here](http://host.robots.ox.ac.uk:8080/eval/downloads/VOC2010test.tar) after registration. To split the training and validation set from original dataset, you may download trainval_merged.json from [here](https://codalabuser.blob.core.windows.net/public/trainval_merged.json). If you would like to use Pascal Context dataset, please install [Detail](https://github.com/zhanghang1989/detail-api) and then run the following command to convert annotations into proper format. ```shell python tools/convert_datasets/pascal_context.py data/VOCdevkit data/VOCdevkit/VOC2010/trainval_merged.json ``` ### CHASE DB1 The training and validation set of CHASE DB1 could be download from [here](https://staffnet.kingston.ac.uk/~ku15565/CHASE_DB1/assets/CHASEDB1.zip). To convert CHASE DB1 dataset to MMSegmentation format, you should run the following command: ```shell python tools/convert_datasets/chase_db1.py /path/to/CHASEDB1.zip ``` The script will make directory structure automatically. ### DRIVE The training and validation set of DRIVE could be download from [here](https://drive.grand-challenge.org/). Before that, you should register an account. Currently '1st_manual' is not provided officially. To convert DRIVE dataset to MMSegmentation format, you should run the following command: ```shell python tools/convert_datasets/drive.py /path/to/training.zip /path/to/test.zip ``` The script will make directory structure automatically. ### HRF First, download [healthy.zip](https://www5.cs.fau.de/fileadmin/research/datasets/fundus-images/healthy.zip), [glaucoma.zip](https://www5.cs.fau.de/fileadmin/research/datasets/fundus-images/glaucoma.zip), [diabetic_retinopathy.zip](https://www5.cs.fau.de/fileadmin/research/datasets/fundus-images/diabetic_retinopathy.zip), [healthy_manualsegm.zip](https://www5.cs.fau.de/fileadmin/research/datasets/fundus-images/healthy_manualsegm.zip), [glaucoma_manualsegm.zip](https://www5.cs.fau.de/fileadmin/research/datasets/fundus-images/glaucoma_manualsegm.zip) and [diabetic_retinopathy_manualsegm.zip](https://www5.cs.fau.de/fileadmin/research/datasets/fundus-images/diabetic_retinopathy_manualsegm.zip). To convert HRF dataset to MMSegmentation format, you should run the following command: ```shell python tools/convert_datasets/hrf.py /path/to/healthy.zip /path/to/healthy_manualsegm.zip /path/to/glaucoma.zip /path/to/glaucoma_manualsegm.zip /path/to/diabetic_retinopathy.zip /path/to/diabetic_retinopathy_manualsegm.zip ``` The script will make directory structure automatically. ### STARE First, download [stare-images.tar](http://cecas.clemson.edu/~ahoover/stare/probing/stare-images.tar), [labels-ah.tar](http://cecas.clemson.edu/~ahoover/stare/probing/labels-ah.tar) and [labels-vk.tar](http://cecas.clemson.edu/~ahoover/stare/probing/labels-vk.tar). To convert STARE dataset to MMSegmentation format, you should run the following command: ```shell python tools/convert_datasets/stare.py /path/to/stare-images.tar /path/to/labels-ah.tar /path/to/labels-vk.tar ``` The script will make directory structure automatically.