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.
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
│ ├── coco_stuff10k
│ │ ├── images
│ │ │ ├── train2014
│ │ │ ├── test2014
│ │ ├── annotations
│ │ │ ├── train2014
│ │ │ ├── test2014
│ │ ├── imagesLists
│ │ │ ├── train.txt
│ │ │ ├── test.txt
│ │ │ ├── all.txt
│ ├── coco_stuff164k
│ │ ├── images
│ │ │ ├── train2017
│ │ │ ├── val2017
│ │ ├── annotations
│ │ │ ├── train2017
│ │ │ ├── val2017
│ ├── 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
| ├── dark_zurich
| │ ├── gps
| │ │ ├── val
| │ │ └── val_ref
| │ ├── gt
| │ │ └── val
| │ ├── LICENSE.txt
| │ ├── lists_file_names
| │ │ ├── val_filenames.txt
| │ │ └── val_ref_filenames.txt
| │ ├── README.md
| │ └── rgb_anon
| │ | ├── val
| │ | └── val_ref
| ├── NighttimeDrivingTest
| | ├── gtCoarse_daytime_trainvaltest
| | │ └── test
| | │ └── night
| | └── leftImg8bit
| | | └── test
| | | └── night
│ ├── loveDA
│ │ ├── img_dir
│ │ │ ├── train
│ │ │ ├── val
│ │ │ ├── test
│ │ ├── ann_dir
│ │ │ ├── train
│ │ │ ├── val
│ ├── potsdam
│ │ ├── img_dir
│ │ │ ├── train
│ │ │ ├── val
│ │ ├── ann_dir
│ │ │ ├── train
│ │ │ ├── val
│ ├── vaihingen
│ │ ├── img_dir
│ │ │ ├── train
│ │ │ ├── val
│ │ ├── ann_dir
│ │ │ ├── train
│ │ │ ├── val
│ ├── iSAID
│ │ ├── img_dir
│ │ │ ├── train
│ │ │ ├── val
│ │ │ ├── test
│ │ ├── ann_dir
│ │ │ ├── train
│ │ │ ├── val
│ ├── occlusion-aware-face-dataset
│ │ ├── train.txt
│ │ ├── NatOcc_hand_sot
│ │ │ ├── img
│ │ │ ├── mask
│ │ ├── NatOcc_object
│ │ │ ├── img
│ │ │ ├── mask
│ │ ├── RandOcc
│ │ │ ├── img
│ │ │ ├── mask
│ │ ├── RealOcc
│ │ │ ├── img
│ │ │ ├── mask
│ │ │ ├── split
│ ├── 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
Cityscapes¶
The data could be found here after registration.
By convention, **labelTrainIds.png are used for cityscapes training.
We provided a scripts based on cityscapesscripts
to generate **labelTrainIds.png.
# --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. Beside, most recent works on Pascal VOC dataset usually exploit extra augmentation data, which could be found here.
If you would like to use augmented VOC dataset, please run following command to convert augmentation annotations into proper format.
# --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 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. We may also download test set from here.
Pascal Context¶
The training and validation set of Pascal Context could be download from here. You may also download test set from here after registration.
To split the training and validation set from original dataset, you may download trainval_merged.json from here.
If you would like to use Pascal Context dataset, please install Detail and then run the following command to convert annotations into proper format.
python tools/convert_datasets/pascal_context.py data/VOCdevkit data/VOCdevkit/VOC2010/trainval_merged.json
COCO Stuff 10k¶
The data could be downloaded here by wget.
For COCO Stuff 10k dataset, please run the following commands to download and convert the dataset.
# download
mkdir coco_stuff10k && cd coco_stuff10k
wget http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/cocostuff-10k-v1.1.zip
# unzip
unzip cocostuff-10k-v1.1.zip
# --nproc means 8 process for conversion, which could be omitted as well.
python tools/convert_datasets/coco_stuff10k.py /path/to/coco_stuff10k --nproc 8
By convention, mask labels in /path/to/coco_stuff164k/annotations/*2014/*_labelTrainIds.png are used for COCO Stuff 10k training and testing.
COCO Stuff 164k¶
For COCO Stuff 164k dataset, please run the following commands to download and convert the augmented dataset.
# download
mkdir coco_stuff164k && cd coco_stuff164k
wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
wget http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip
# unzip
unzip train2017.zip -d images/
unzip val2017.zip -d images/
unzip stuffthingmaps_trainval2017.zip -d annotations/
# --nproc means 8 process for conversion, which could be omitted as well.
python tools/convert_datasets/coco_stuff164k.py /path/to/coco_stuff164k --nproc 8
By convention, mask labels in /path/to/coco_stuff164k/annotations/*2017/*_labelTrainIds.png are used for COCO Stuff 164k training and testing.
The details of this dataset could be found at here.
CHASE DB1¶
The training and validation set of CHASE DB1 could be download from here.
To convert CHASE DB1 dataset to MMSegmentation format, you should run the following command:
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. 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:
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, glaucoma.zip, diabetic_retinopathy.zip, healthy_manualsegm.zip, glaucoma_manualsegm.zip and diabetic_retinopathy_manualsegm.zip.
To convert HRF dataset to MMSegmentation format, you should run the following command:
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, labels-ah.tar and labels-vk.tar.
To convert STARE dataset to MMSegmentation format, you should run the following command:
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.
Dark Zurich¶
Since we only support test models on this dataset, you may only download the validation set.
Nighttime Driving¶
Since we only support test models on this dataset, you may only download the test set.
LoveDA¶
The data could be downloaded from Google Drive here.
Or it can be downloaded from zenodo, you should run the following command:
# Download Train.zip
wget https://zenodo.org/record/5706578/files/Train.zip
# Download Val.zip
wget https://zenodo.org/record/5706578/files/Val.zip
# Download Test.zip
wget https://zenodo.org/record/5706578/files/Test.zip
For LoveDA dataset, please run the following command to download and re-organize the dataset.
python tools/convert_datasets/loveda.py /path/to/loveDA
Using trained model to predict test set of LoveDA and submit it to server can be found here.
More details about LoveDA can be found here.
ISPRS Potsdam¶
The Potsdam dataset is for urban semantic segmentation used in the 2D Semantic Labeling Contest - Potsdam.
The dataset can be requested at the challenge homepage. The ‘2_Ortho_RGB.zip’ and ‘5_Labels_all_noBoundary.zip’ are required.
For Potsdam dataset, please run the following command to download and re-organize the dataset.
python tools/convert_datasets/potsdam.py /path/to/potsdam
In our default setting, it will generate 3456 images for training and 2016 images for validation.
ISPRS Vaihingen¶
The Vaihingen dataset is for urban semantic segmentation used in the 2D Semantic Labeling Contest - Vaihingen.
The dataset can be requested at the challenge homepage. The ‘ISPRS_semantic_labeling_Vaihingen.zip’ and ‘ISPRS_semantic_labeling_Vaihingen_ground_truth_eroded_COMPLETE.zip’ are required.
For Vaihingen dataset, please run the following command to download and re-organize the dataset.
python tools/convert_datasets/vaihingen.py /path/to/vaihingen
In our default setting (clip_size =512, stride_size=256), it will generate 344 images for training and 398 images for validation.
iSAID¶
The data images could be download from DOTA-v1.0 (train/val/test)
The data annotations could be download from iSAID (train/val)
The dataset is a Large-scale Dataset for Instance Segmentation (also have segmantic segmentation) in Aerial Images.
You may need to follow the following structure for dataset preparation after downloading iSAID dataset.
│ ├── iSAID
│ │ ├── train
│ │ │ ├── images
│ │ │ │ ├── part1.zip
│ │ │ │ ├── part2.zip
│ │ │ │ ├── part3.zip
│ │ │ ├── Semantic_masks
│ │ │ │ ├── images.zip
│ │ ├── val
│ │ │ ├── images
│ │ │ │ ├── part1.zip
│ │ │ ├── Semantic_masks
│ │ │ │ ├── images.zip
│ │ ├── test
│ │ │ ├── images
│ │ │ │ ├── part1.zip
│ │ │ │ ├── part2.zip
python tools/convert_datasets/isaid.py /path/to/iSAID
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.
Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets¶
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
Dataset Preparation¶
step 1
Create a folder for data generation materials on mmsegmentation folder.
mkdir data_materials
step 2
Please download the masks (11k-hands_mask.7z,CelebAMask-HQ-masks_corrected.7z) from this drive
Please download the images from CelebAMask-HQ, 11k Hands.zip and dtd-r1.0.1.tar.gz.
step 3
Download a upsampled COCO objects images and masks (coco_object.7z). files can be found in this drive.
Download CelebAMask-HQ and 11k Hands images split txt files. (11k_hands_sample.txt, CelebAMask-HQ-WO-train.txt) found in drive.
download file to ./data_materials
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
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:
├── 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¶
git clone https://github.com/kennyvoo/face-occlusion-generation.git
cd face_occlusion-generation
Example script to generate NatOcc hand dataset
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
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
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:
├── 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
ImageNetS¶
The ImageNet-S dataset is for Large-scale unsupervised/semi-supervised semantic segmentation.
The images and annotations are available on ImageNet-S.
│ ├── 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