DATASETS.md

January 28, 2022 · View on GitHub

Supported Datasets

Dataset	Type	Categories	Train ^Images	Val ^Images	Test ^Images	Image Size ^(HxW)
COCO-Stuff	General Scene Parsing	171	118,000	5,000	20,000	-
ADE20K	General Scene Parsing	150	20,210	2,000	3,352	-
PASCALContext	General Scene Parsing	59	4,996	5,104	9,637	-

SUN RGB-D	Indoor Scene Parsing	37	2,666	2,619	5,050^+labels	-

Mapillary Vistas	Street Scene Parsing	65	18,000	2,000	5,000	1080x1920
CityScapes	Street Scene Parsing	19	2,975	500	1,525^+labels	1024x2048
CamVid	Street Scene Parsing	11	367	101	233^+labels	720x960

MHPv2	Multi-Human Parsing	59	15,403	5,000	5,000	-
MHPv1	Multi-Human Parsing	19	3,000	1,000	980^+labels	-
LIP	Multi-Human Parsing	20	30,462	10,000	-	-
CCIHP	Multi-Human Parsing	22	28,280	5,000	5,000	-
CIHP	Multi-Human Parsing	20	28,280	5,000	5,000	-
ATR	Single-Human Parsing	18	16,000	700	1,000^+labels	-

HELEN	Face Parsing	11	2,000	230	100^+labels	-
LaPa	Face Parsing	11	18,176	2,000	2,000^+labels	-
iBugMask	Face Parsing	11	21,866	-	1,000^+labels	-
CelebAMaskHQ	Face Parsing	19	24,183	2,993	2,824^+labels	512x512
FaceSynthetics	Face Parsing (Synthetic)	19	100,000	1,000	100^+labels	512x512

SUIM	Underwater Imagery	8	1,525	-	110^+labels	-

Check DATASETS to find more segmentation datasets.

Datasets Structure (click to expand)

Datasets should have the following structure:

data
|__ ADEChallenge
    |__ ADEChallengeData2016
        |__ images
            |__ training
            |__ validation
        |__ annotations
            |__ training
            |__ validation

|__ CityScapes
    |__ leftImg8bit
        |__ train
        |__ val
        |__ test
    |__ gtFine
        |__ train
        |__ val
        |__ test

|__ CamVid
    |__ train
    |__ val
    |__ test
    |__ train_labels
    |__ val_labels
    |__ test_labels
    
|__ VOCdevkit
    |__ VOC2010
        |__ JPEGImages
        |__ SegmentationClassContext
        |__ ImageSets
            |__ SegmentationContext
                |__ train.txt
                |__ val.txt
    
|__ COCO
    |__ images
        |__ train2017
        |__ val2017
    |__ labels
        |__ train2017
        |__ val2017

|__ MHPv1
    |__ images
    |__ annotations
    |__ train_list.txt
    |__ test_list.txt

|__ MHPv2
    |__ train
        |__ images
        |__ parsing_annos
    |__ val
        |__ images
        |__ parsing_annos

|__ LIP
    |__ LIP
        |__ TrainVal_images
            |__ train_images
            |__ val_images
        |__ TrainVal_parsing_annotations
            |__ train_segmentations
            |__ val_segmentations

    |__ CIHP/CCIHP
        |__ instance-leve_human_parsing
            |__ Training
                |__ Images
                |__ Category_ids
            |__ Validation
                |__ Images
                |__ Category_ids

    |__ ATR
        |__ humanparsing
            |__ JPEGImages
            |__ SegmentationClassAug

|__ SUIM
    |__ train_val
        |__ images
        |__ masks
    |__ TEST
        |__ images
        |__ masks

|__ SunRGBD
    |__ SUNRGBD
        |__ kv1/kv2/realsense/xtion
    |__ SUNRGBDtoolbox
        |__ traintestSUNRGBD
            |__ allsplit.mat

|__ Mapillary
    |__ training
        |__ images
        |__ labels
    |__ validation
        |__ images
        |__ labels

|__ SmithCVPR2013_dataset_resized (HELEN)
    |__ images
    |__ labels
    |__ exemplars.txt
    |__ testing.txt
    |__ tuning.txt

|__ CelebAMask-HQ
    |__ CelebA-HQ-img
    |__ CelebAMask-HQ-mask-anno
    |__ CelebA-HQ-to-CelebA-mapping.txt

|__ LaPa
    |__ train
        |__ images
        |__ labels
    |__ val
        |__ images
        |__ labels
    |__ test
        |__ images
        |__ labels

|__ ibugmask_release
    |__ train
    |__ test

|__ FaceSynthetics
    |__ dataset_100000
    |__ dataset_1000
    |__ dataset_100

Note: For PASCALContext, download the annotations from here and put it in VOC2010.

Note: For CelebAMask-HQ, run the preprocess script. python3 scripts/preprocess_celebamaskhq.py --root <DATASET-ROOT-DIR>.