379 lines
15 KiB
Markdown
379 lines
15 KiB
Markdown
## Prepare datasets
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It is recommended to symlink the dataset root to `$MMSEGMENTATION/data`.
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If your folder structure is different, you may need to change the corresponding paths in config files.
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```none
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mmsegmentation
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├── mmseg
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├── tools
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├── configs
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├── data
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│ ├── cityscapes
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│ │ ├── leftImg8bit
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│ │ │ ├── train
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│ │ │ ├── val
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│ │ ├── gtFine
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│ │ │ ├── train
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│ │ │ ├── val
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│ ├── VOCdevkit
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│ │ ├── VOC2012
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│ │ │ ├── JPEGImages
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│ │ │ ├── SegmentationClass
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│ │ │ ├── ImageSets
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│ │ │ │ ├── Segmentation
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│ │ ├── VOC2010
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│ │ │ ├── JPEGImages
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│ │ │ ├── SegmentationClassContext
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│ │ │ ├── ImageSets
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│ │ │ │ ├── SegmentationContext
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│ │ │ │ │ ├── train.txt
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│ │ │ │ │ ├── val.txt
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│ │ │ ├── trainval_merged.json
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│ │ ├── VOCaug
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│ │ │ ├── dataset
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│ │ │ │ ├── cls
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│ ├── ade
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│ │ ├── ADEChallengeData2016
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│ │ │ ├── annotations
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│ │ │ │ ├── training
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│ │ │ │ ├── validation
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│ │ │ ├── images
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│ │ │ │ ├── training
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│ │ │ │ ├── validation
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│ ├── coco_stuff10k
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│ │ ├── images
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│ │ │ ├── train2014
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│ │ │ ├── test2014
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│ │ ├── annotations
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│ │ │ ├── train2014
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│ │ │ ├── test2014
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│ │ ├── imagesLists
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│ │ │ ├── train.txt
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│ │ │ ├── test.txt
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│ │ │ ├── all.txt
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│ ├── coco_stuff164k
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│ │ ├── images
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│ │ │ ├── train2017
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│ │ │ ├── val2017
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│ │ ├── annotations
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│ │ │ ├── train2017
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│ │ │ ├── val2017
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│ ├── CHASE_DB1
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│ │ ├── images
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│ │ │ ├── training
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│ │ │ ├── validation
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│ │ ├── annotations
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│ │ │ ├── training
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│ │ │ ├── validation
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│ ├── DRIVE
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│ │ ├── images
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│ │ │ ├── training
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│ │ │ ├── validation
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│ │ ├── annotations
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│ │ │ ├── training
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│ │ │ ├── validation
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│ ├── HRF
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│ │ ├── images
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│ │ │ ├── training
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│ │ │ ├── validation
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│ │ ├── annotations
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│ │ │ ├── training
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│ │ │ ├── validation
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│ ├── STARE
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│ │ ├── images
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│ │ │ ├── training
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│ │ │ ├── validation
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│ │ ├── annotations
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│ │ │ ├── training
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│ │ │ ├── validation
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| ├── dark_zurich
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| │ ├── gps
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| │ │ ├── val
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| │ │ └── val_ref
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| │ ├── gt
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| │ │ └── val
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| │ ├── LICENSE.txt
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| │ ├── lists_file_names
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| │ │ ├── val_filenames.txt
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| │ │ └── val_ref_filenames.txt
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| │ ├── README.md
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| │ └── rgb_anon
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| │ | ├── val
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| │ | └── val_ref
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| ├── NighttimeDrivingTest
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| | ├── gtCoarse_daytime_trainvaltest
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| | │ └── test
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| | │ └── night
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| | └── leftImg8bit
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| | | └── test
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| | | └── night
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│ ├── loveDA
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│ │ ├── img_dir
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│ │ │ ├── train
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│ │ │ ├── val
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│ │ │ ├── test
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│ │ ├── ann_dir
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│ │ │ ├── train
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│ │ │ ├── val
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│ ├── potsdam
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│ │ ├── img_dir
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│ │ │ ├── train
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│ │ │ ├── val
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│ │ ├── ann_dir
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│ │ │ ├── train
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│ │ │ ├── val
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│ ├── vaihingen
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│ │ ├── img_dir
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│ │ │ ├── train
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│ │ │ ├── val
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│ │ ├── ann_dir
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│ │ │ ├── train
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│ │ │ ├── val
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│ ├── iSAID
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│ │ ├── img_dir
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│ │ │ ├── train
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│ │ │ ├── val
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│ │ │ ├── test
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│ │ ├── ann_dir
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│ │ │ ├── train
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│ │ │ ├── val
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```
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### Cityscapes
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The data could be found [here](https://www.cityscapes-dataset.com/downloads/) after registration.
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By convention, `**labelTrainIds.png` are used for cityscapes training.
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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)
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to generate `**labelTrainIds.png`.
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```shell
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# --nproc means 8 process for conversion, which could be omitted as well.
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python tools/convert_datasets/cityscapes.py data/cityscapes --nproc 8
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```
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### Pascal VOC
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Pascal VOC 2012 could be downloaded from [here](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar).
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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).
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If you would like to use augmented VOC dataset, please run following command to convert augmentation annotations into proper format.
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```shell
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# --nproc means 8 process for conversion, which could be omitted as well.
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python tools/convert_datasets/voc_aug.py data/VOCdevkit data/VOCdevkit/VOCaug --nproc 8
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```
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Please refer to [concat dataset](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/tutorials/customize_datasets.md#concatenate-dataset) for details about how to concatenate them and train them together.
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### ADE20K
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The training and validation set of ADE20K could be download from this [link](http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip).
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We may also download test set from [here](http://data.csail.mit.edu/places/ADEchallenge/release_test.zip).
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### Pascal Context
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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.
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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).
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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.
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```shell
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python tools/convert_datasets/pascal_context.py data/VOCdevkit data/VOCdevkit/VOC2010/trainval_merged.json
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```
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### COCO Stuff 10k
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The data could be downloaded [here](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/cocostuff-10k-v1.1.zip) by wget.
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For COCO Stuff 10k dataset, please run the following commands to download and convert the dataset.
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```shell
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# download
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mkdir coco_stuff10k && cd coco_stuff10k
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wget http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/cocostuff-10k-v1.1.zip
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# unzip
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unzip cocostuff-10k-v1.1.zip
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# --nproc means 8 process for conversion, which could be omitted as well.
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python tools/convert_datasets/coco_stuff10k.py /path/to/coco_stuff10k --nproc 8
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```
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By convention, mask labels in `/path/to/coco_stuff164k/annotations/*2014/*_labelTrainIds.png` are used for COCO Stuff 10k training and testing.
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### COCO Stuff 164k
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For COCO Stuff 164k dataset, please run the following commands to download and convert the augmented dataset.
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```shell
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# download
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mkdir coco_stuff164k && cd coco_stuff164k
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wget http://images.cocodataset.org/zips/train2017.zip
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wget http://images.cocodataset.org/zips/val2017.zip
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wget http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip
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# unzip
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unzip train2017.zip -d images/
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unzip val2017.zip -d images/
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unzip stuffthingmaps_trainval2017.zip -d annotations/
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# --nproc means 8 process for conversion, which could be omitted as well.
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python tools/convert_datasets/coco_stuff164k.py /path/to/coco_stuff164k --nproc 8
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```
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By convention, mask labels in `/path/to/coco_stuff164k/annotations/*2017/*_labelTrainIds.png` are used for COCO Stuff 164k training and testing.
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The details of this dataset could be found at [here](https://github.com/nightrome/cocostuff#downloads).
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### CHASE DB1
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The training and validation set of CHASE DB1 could be download from [here](https://staffnet.kingston.ac.uk/~ku15565/CHASE_DB1/assets/CHASEDB1.zip).
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To convert CHASE DB1 dataset to MMSegmentation format, you should run the following command:
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```shell
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python tools/convert_datasets/chase_db1.py /path/to/CHASEDB1.zip
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```
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The script will make directory structure automatically.
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### DRIVE
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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.
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To convert DRIVE dataset to MMSegmentation format, you should run the following command:
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```shell
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python tools/convert_datasets/drive.py /path/to/training.zip /path/to/test.zip
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```
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The script will make directory structure automatically.
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### HRF
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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).
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To convert HRF dataset to MMSegmentation format, you should run the following command:
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```shell
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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
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```
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The script will make directory structure automatically.
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### STARE
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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).
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To convert STARE dataset to MMSegmentation format, you should run the following command:
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```shell
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python tools/convert_datasets/stare.py /path/to/stare-images.tar /path/to/labels-ah.tar /path/to/labels-vk.tar
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```
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The script will make directory structure automatically.
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### Dark Zurich
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Since we only support test models on this dataset, you may only download [the validation set](https://data.vision.ee.ethz.ch/csakarid/shared/GCMA_UIoU/Dark_Zurich_val_anon.zip).
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### Nighttime Driving
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Since we only support test models on this dataset, you may only download [the test set](http://data.vision.ee.ethz.ch/daid/NighttimeDriving/NighttimeDrivingTest.zip).
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### LoveDA
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The data could be downloaded from Google Drive [here](https://drive.google.com/drive/folders/1ibYV0qwn4yuuh068Rnc-w4tPi0U0c-ti?usp=sharing).
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Or it can be downloaded from [zenodo](https://zenodo.org/record/5706578#.YZvN7SYRXdF), you should run the following command:
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```shell
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# Download Train.zip
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wget https://zenodo.org/record/5706578/files/Train.zip
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# Download Val.zip
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wget https://zenodo.org/record/5706578/files/Val.zip
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# Download Test.zip
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wget https://zenodo.org/record/5706578/files/Test.zip
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```
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For LoveDA dataset, please run the following command to download and re-organize the dataset.
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```shell
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python tools/convert_datasets/loveda.py /path/to/loveDA
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```
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Using trained model to predict test set of LoveDA and submit it to server can be found [here](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/inference.md).
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More details about LoveDA can be found [here](https://github.com/Junjue-Wang/LoveDA).
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### ISPRS Potsdam
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The [Potsdam](https://www2.isprs.org/commissions/comm2/wg4/benchmark/2d-sem-label-potsdam/)
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dataset is for urban semantic segmentation used in the 2D Semantic Labeling Contest - Potsdam.
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The dataset can be requested at the challenge [homepage](https://www2.isprs.org/commissions/comm2/wg4/benchmark/data-request-form/).
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The '2_Ortho_RGB.zip' and '5_Labels_all_noBoundary.zip' are required.
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For Potsdam dataset, please run the following command to download and re-organize the dataset.
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```shell
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python tools/convert_datasets/potsdam.py /path/to/potsdam
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```
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In our default setting, it will generate 3456 images for training and 2016 images for validation.
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### ISPRS Vaihingen
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The [Vaihingen](https://www2.isprs.org/commissions/comm2/wg4/benchmark/2d-sem-label-vaihingen/)
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dataset is for urban semantic segmentation used in the 2D Semantic Labeling Contest - Vaihingen.
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The dataset can be requested at the challenge [homepage](https://www2.isprs.org/commissions/comm2/wg4/benchmark/data-request-form/).
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The 'ISPRS_semantic_labeling_Vaihingen.zip' and 'ISPRS_semantic_labeling_Vaihingen_ground_truth_eroded_COMPLETE.zip' are required.
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For Vaihingen dataset, please run the following command to download and re-organize the dataset.
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```shell
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python tools/convert_datasets/vaihingen.py /path/to/vaihingen
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```
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In our default setting (`clip_size` =512, `stride_size`=256), it will generate 344 images for training and 398 images for validation.
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### iSAID
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The data images could be download from [DOTA-v1.0](https://captain-whu.github.io/DOTA/dataset.html) (train/val/test)
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The data annotations could be download from [iSAID](https://captain-whu.github.io/iSAID/dataset.html) (train/val)
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The dataset is a Large-scale Dataset for Instance Segmentation (also have segmantic segmentation) in Aerial Images.
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You may need to follow the following structure for dataset preparation after downloading iSAID dataset.
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```
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│ ├── iSAID
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│ │ ├── train
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│ │ │ ├── images
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│ │ │ │ ├── part1.zip
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│ │ │ │ ├── part2.zip
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│ │ │ │ ├── part3.zip
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│ │ │ ├── Semantic_masks
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│ │ │ │ ├── images.zip
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│ │ ├── val
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│ │ │ ├── images
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│ │ │ │ ├── part1.zip
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│ │ │ ├── Semantic_masks
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│ │ │ │ ├── images.zip
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│ │ ├── test
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│ │ │ ├── images
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│ │ │ │ ├── part1.zip
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│ │ │ │ ├── part2.zip
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```
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```shell
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python tools/convert_datasets/isaid.py /path/to/iSAID
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```
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In our default setting (`patch_width`=896, `patch_height`=896, `overlap_area`=384), it will generate 33978 images for training and 11644 images for validation.
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