630 lines
24 KiB
Markdown
630 lines
24 KiB
Markdown
<!-- #region -->
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## 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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│ ├── occlusion-aware-face-dataset
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│ │ ├── train.txt
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│ │ ├── NatOcc_hand_sot
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│ │ │ ├── img
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│ │ │ ├── mask
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│ │ ├── NatOcc_object
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│ │ │ ├── img
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│ │ │ ├── mask
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│ │ ├── RandOcc
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│ │ │ ├── img
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│ │ │ ├── mask
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│ │ ├── RealOcc
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│ │ │ ├── img
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│ │ │ ├── mask
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│ │ │ ├── split
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│ ├── ImageNetS
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│ │ ├── ImageNetS919
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│ │ │ ├── train-semi
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│ │ │ ├── train-semi-segmentation
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│ │ │ ├── validation
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│ │ │ ├── validation-segmentation
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│ │ │ ├── test
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│ │ ├── ImageNetS300
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│ │ │ ├── train-semi
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│ │ │ ├── train-semi-segmentation
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│ │ │ ├── validation
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│ │ │ ├── validation-segmentation
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│ │ │ ├── test
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│ │ ├── ImageNetS50
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│ │ │ ├── train-semi
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│ │ │ ├── train-semi-segmentation
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│ │ │ ├── validation
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│ │ │ ├── validation-segmentation
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│ │ │ ├── test
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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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### Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets
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The dataset is generated by two techniques, Naturalistic occlusion generation, Random occlusion generation. you must install face-occlusion-generation and dataset. see more guide in https://github.com/kennyvoo/face-occlusion-generation.git
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## Dataset Preparation
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step 1
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Create a folder for data generation materials on mmsegmentation folder.
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```shell
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mkdir data_materials
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```
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step 2
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Please download the masks (11k-hands_mask.7z,CelebAMask-HQ-masks_corrected.7z) from this [drive](https://drive.google.com/drive/folders/15nZETWlGMdcKY6aHbchRsWkUI42KTNs5?usp=sharing)
|
||
|
||
Please download the images from [CelebAMask-HQ](https://github.com/switchablenorms/CelebAMask-HQ), [11k Hands.zip](https://sites.google.com/view/11khands) and [dtd-r1.0.1.tar.gz](https://www.robots.ox.ac.uk/~vgg/data/dtd/).
|
||
|
||
step 3
|
||
|
||
Download a upsampled COCO objects images and masks (coco_object.7z). files can be found in this [drive](https://drive.google.com/drive/folders/15nZETWlGMdcKY6aHbchRsWkUI42KTNs5?usp=sharing).
|
||
|
||
Download CelebAMask-HQ and 11k Hands images split txt files. (11k_hands_sample.txt, CelebAMask-HQ-WO-train.txt) found in [drive](https://drive.google.com/drive/folders/15nZETWlGMdcKY6aHbchRsWkUI42KTNs5?usp=sharing).
|
||
|
||
download file to ./data_materials
|
||
|
||
```none
|
||
CelebAMask-HQ.zip
|
||
CelebAMask-HQ-masks_corrected.7z
|
||
CelebAMask-HQ-WO-train.txt
|
||
RealOcc.7z
|
||
RealOcc-Wild.7z
|
||
11k-hands_mask.7z
|
||
11k Hands.zip
|
||
11k_hands_sample.txt
|
||
coco_object.7z
|
||
dtd-r1.0.1.tar.gz
|
||
```
|
||
|
||
______________________________________________________________________
|
||
|
||
```bash
|
||
apt-get install p7zip-full
|
||
|
||
cd data_materials
|
||
|
||
#make occlusion-aware-face-dataset folder
|
||
mkdir path-to-mmsegmentaion/data/occlusion-aware-face-dataset
|
||
|
||
#extract celebAMask-HQ and split by train-set
|
||
unzip CelebAMask-HQ.zip
|
||
7za x CelebAMask-HQ-masks_corrected.7z -o./CelebAMask-HQ
|
||
#copy training data to train-image-folder
|
||
rsync -a ./CelebAMask-HQ/CelebA-HQ-img/ --files-from=./CelebAMask-HQ-WO-train.txt ./CelebAMask-HQ-WO-Train_img
|
||
#create a file-name txt file for copying mask
|
||
basename -s .jpg ./CelebAMask-HQ-WO-Train_img/* > train.txt
|
||
#add .png to file-name txt file
|
||
xargs -n 1 -i echo {}.png < train.txt > mask_train.txt
|
||
#copy training data to train-mask-folder
|
||
rsync -a ./CelebAMask-HQ/CelebAMask-HQ-masks_corrected/ --files-from=./mask_train.txt ./CelebAMask-HQ-WO-Train_mask
|
||
mv train.txt ../data/occlusion-aware-face-dataset
|
||
|
||
#extract DTD
|
||
tar -zxvf dtd-r1.0.1.tar.gz
|
||
mv dtd DTD
|
||
|
||
#extract hands dataset and split by 200 samples
|
||
7za x 11k-hands_masks.7z -o.
|
||
unzip Hands.zip
|
||
rsync -a ./Hands/ --files-from=./11k_hands_sample.txt ./11k-hands_img
|
||
|
||
#extract upscaled coco object
|
||
7za x coco_object.7z -o.
|
||
mv coco_object/* .
|
||
|
||
#extract validation set
|
||
7za x RealOcc.7z -o../data/occlusion-aware-face-dataset
|
||
|
||
```
|
||
|
||
**Dataset material Organization:**
|
||
|
||
```none
|
||
|
||
├── data_materials
|
||
│ ├── CelebAMask-HQ-WO-Train_img
|
||
│ │ ├── {image}.jpg
|
||
│ ├── CelebAMask-HQ-WO-Train_mask
|
||
│ │ ├── {mask}.png
|
||
│ ├── DTD
|
||
│ │ ├── images
|
||
│ │ │ ├── {classA}
|
||
│ │ │ │ ├── {image}.jpg
|
||
│ │ │ ├── {classB}
|
||
│ │ │ │ ├── {image}.jpg
|
||
│ ├── 11k-hands_img
|
||
│ │ ├── {image}.jpg
|
||
│ ├── 11k-hands_mask
|
||
│ │ ├── {mask}.png
|
||
│ ├── object_image_sr
|
||
│ │ ├── {image}.jpg
|
||
│ ├── object_mask_x4
|
||
│ │ ├── {mask}.png
|
||
|
||
```
|
||
|
||
## Data Generation
|
||
|
||
```bash
|
||
git clone https://github.com/kennyvoo/face-occlusion-generation.git
|
||
cd face_occlusion-generation
|
||
```
|
||
|
||
Example script to generate NatOcc hand dataset
|
||
|
||
```bash
|
||
CUDA_VISIBLE_DEVICES=0 NUM_WORKERS=4 python main.py \
|
||
--config ./configs/natocc_hand.yaml \
|
||
--opts OUTPUT_PATH "path/to/mmsegmentation/data/occlusion-aware-face-dataset/NatOcc_hand_sot"\
|
||
AUGMENTATION.SOT True \
|
||
SOURCE_DATASET.IMG_DIR "path/to/data_materials/CelebAMask-HQ-WO-Train_img" \
|
||
SOURCE_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-Train_mask" \
|
||
OCCLUDER_DATASET.IMG_DIR "path/to/mmsegmentation/data_materials/11k-hands_img" \
|
||
OCCLUDER_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/11k-hands_masks"
|
||
```
|
||
|
||
Example script to generate NatOcc object dataset
|
||
|
||
```bash
|
||
CUDA_VISIBLE_DEVICES=0 NUM_WORKERS=4 python main.py \
|
||
--config ./configs/natocc_objects.yaml \
|
||
--opts OUTPUT_PATH "path/to/mmsegmentation/data/occlusion-aware-face-dataset/NatOcc_object" \
|
||
SOURCE_DATASET.IMG_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-Train_img" \
|
||
SOURCE_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-Train_mask" \
|
||
OCCLUDER_DATASET.IMG_DIR "path/to/mmsegmentation/data_materials/object_image_sr" \
|
||
OCCLUDER_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/object_mask_x4"
|
||
```
|
||
|
||
Example script to generate RandOcc dataset
|
||
|
||
```bash
|
||
CUDA_VISIBLE_DEVICES=0 NUM_WORKERS=4 python main.py \
|
||
--config ./configs/randocc.yaml \
|
||
--opts OUTPUT_PATH "path/to/mmsegmentation/data/occlusion-aware-face-dataset/RandOcc" \
|
||
SOURCE_DATASET.IMG_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-Train_img/" \
|
||
SOURCE_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-Train_mask" \
|
||
OCCLUDER_DATASET.IMG_DIR "path/to/jw93/mmsegmentation/data_materials/DTD/images"
|
||
```
|
||
|
||
**Dataset Organization:**
|
||
|
||
```none
|
||
├── data
|
||
│ ├── occlusion-aware-face-dataset
|
||
│ │ ├── train.txt
|
||
│ │ ├── NatOcc_hand_sot
|
||
│ │ │ ├── img
|
||
│ │ │ │ ├── {image}.jpg
|
||
│ │ │ ├── mask
|
||
│ │ │ │ ├── {mask}.png
|
||
│ │ ├── NatOcc_object
|
||
│ │ │ ├── img
|
||
│ │ │ │ ├── {image}.jpg
|
||
│ │ │ ├── mask
|
||
│ │ │ │ ├── {mask}.png
|
||
│ │ ├── RandOcc
|
||
│ │ │ ├── img
|
||
│ │ │ │ ├── {image}.jpg
|
||
│ │ │ ├── mask
|
||
│ │ │ │ ├── {mask}.png
|
||
│ │ ├── RealOcc
|
||
│ │ │ ├── img
|
||
│ │ │ │ ├── {image}.jpg
|
||
│ │ │ ├── mask
|
||
│ │ │ │ ├── {mask}.png
|
||
│ │ │ ├── split
|
||
│ │ │ │ ├── val.txt
|
||
```
|
||
|
||
<!-- #endregion -->
|
||
|
||
```python
|
||
|
||
```
|
||
|
||
### ImageNetS
|
||
|
||
The ImageNet-S dataset is for [Large-scale unsupervised/semi-supervised semantic segmentation](https://arxiv.org/abs/2106.03149).
|
||
|
||
The images and annotations are available on [ImageNet-S](https://github.com/LUSSeg/ImageNet-S#imagenet-s-dataset-preparation).
|
||
|
||
```
|
||
│ ├── ImageNetS
|
||
│ │ ├── ImageNetS919
|
||
│ │ │ ├── train-semi
|
||
│ │ │ ├── train-semi-segmentation
|
||
│ │ │ ├── validation
|
||
│ │ │ ├── validation-segmentation
|
||
│ │ │ ├── test
|
||
│ │ ├── ImageNetS300
|
||
│ │ │ ├── train-semi
|
||
│ │ │ ├── train-semi-segmentation
|
||
│ │ │ ├── validation
|
||
│ │ │ ├── validation-segmentation
|
||
│ │ │ ├── test
|
||
│ │ ├── ImageNetS50
|
||
│ │ │ ├── train-semi
|
||
│ │ │ ├── train-semi-segmentation
|
||
│ │ │ ├── validation
|
||
│ │ │ ├── validation-segmentation
|
||
│ │ │ ├── test
|
||
```
|