Tutorial 2: 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.
For users in China, we also recommend you get the dsdl dataset from our opensource platform OpenDataLab, for better download and use experience，here is an example: DSDLReadme， welcome to try.
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
│ ├── synapse
│ │ ├── img_dir
│ │ │ ├── train
│ │ │ ├── val
│ │ ├── ann_dir
│ │ │ ├── train
│ │ │ ├── val
│ ├── REFUGE
│ │ ├── images
│ │ │ ├── training
│ │ │ ├── validation
│ │ │ ├── test
│ │ ├── annotations
│ │ │ ├── training
│ │ │ ├── validation
│ │ │ ├── test
│ ├── mapillary
│ │ ├── training
│ │ │ ├── images
│ │ │ ├── v1.2
| │ │ │ ├── instances
| │ │ │ ├── labels
| │ │ │ └── panoptic
│ │ │ ├── v2.0
| │ │ │ ├── instances
| │ │ │ ├── labels
| │ │ │ ├── panoptic
| │ │ │ └── polygons
│ │ ├── validation
│ │ │ ├── images
| │ │ ├── v1.2
| │ │ │ ├── instances
| │ │ │ ├── labels
| │ │ │ └── panoptic
│ │ │ ├── v2.0
| │ │ │ ├── instances
| │ │ │ ├── labels
| │ │ │ ├── panoptic
| │ │ │ └── polygons
│ ├── bdd100k
│ │ ├── images
│ │ │ └── 10k
| │ │ │ ├── test
| │ │ │ ├── train
| │ │ │ └── val
│ │ └── labels
│ │ │ └── sem_seg
| │ │ │ ├── colormaps
| │ │ │ │ ├──train
| │ │ │ │ └──val
| │ │ │ ├── masks
| │ │ │ │ ├──train
| │ │ │ │ └──val
| │ │ │ ├── polygons
| │ │ │ │ ├──sem_seg_train.json
| │ │ │ │ └──sem_seg_val.json
| │ │ │ └── rles
| │ │ │ │ ├──sem_seg_train.json
| │ │ │ │ └──sem_seg_val.json
│ ├── nyu
│ │ ├── images
│ │ │ ├── train
│ │ │ ├── test
│ │ ├── annotations
│ │ │ ├── train
│ │ │ ├── test
│ ├── HSIDrive20
│ │ ├── images
│ │ │ ├── train
│ │ │ ├── validation
│ │ │ ├── test
│ │ ├── annotations
│ │ │ ├── train
│ │ │ ├── validation
│ │ │ ├── test
Download dataset via MIM¶
By using OpenXLab, you can obtain free formatted datasets in various fields. Through the search function of the platform, you may address the dataset they look for quickly and easily. Using the formatted datasets from the platform, you can efficiently conduct tasks across datasets.
If you use MIM to download, make sure that the version is greater than v0.3.8. You can use the following command to update, install, login and download the dataset:
# upgrade your MIM
pip install -U openmim
# install OpenXLab CLI tools
pip install -U openxlab
# log in OpenXLab
openxlab login
# download ADE20K by MIM
mim download mmsegmentation --dataset ade20k
Cityscapes¶
The data could be found here after registration.
By convention, **labelTrainIds.png are used for cityscapes training.
We provided a script based on cityscapesscriptsto generate **labelTrainIds.png.
# --nproc means 8 process for conversion, which could be omitted as well.
python tools/dataset_converters/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/dataset_converters/voc_aug.py data/VOCdevkit data/VOCdevkit/VOCaug --nproc 8
Please refer to concat dataset and voc_aug config example 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/dataset_converters/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/dataset_converters/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/dataset_converters/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/dataset_converters/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/dataset_converters/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/dataset_converters/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/dataset_converters/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 re-organize the dataset.
python tools/dataset_converters/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. Or download on BaiduNetdisk，password：mseg, Google Drive and OpenDataLab. The ‘2_Ortho_RGB.zip’ and ‘5_Labels_all_noBoundary.zip’ are required.
For Potsdam dataset, please run the following command to re-organize the dataset.
python tools/dataset_converters/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. Or BaiduNetdisk，password：mseg, Google Drive. 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 re-organize the dataset.
python tools/dataset_converters/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 semantic segmentation) in Aerial Images.
You may need to follow the following structure for dataset preparation after downloading iSAID dataset.
├── data
│ ├── 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/dataset_converters/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.
LIP(Look Into Person) dataset¶
This dataset could be download from this page.
Please run the following commands to unzip dataset.
unzip LIP.zip
cd LIP
unzip TrainVal_images.zip
unzip TrainVal_parsing_annotations.zip
cd TrainVal_parsing_annotations
unzip TrainVal_parsing_annotations.zip
mv train_segmentations ../
mv val_segmentations ../
cd ..
The contents of LIP datasets include:
├── data
│ ├── LIP
│ │ ├── train_images
│ │ │ ├── 1000_1234574.jpg
│ │ │ ├── ...
│ │ ├── train_segmentations
│ │ │ ├── 1000_1234574.png
│ │ │ ├── ...
│ │ ├── val_images
│ │ │ ├── 100034_483681.jpg
│ │ │ ├── ...
│ │ ├── val_segmentations
│ │ │ ├── 100034_483681.png
│ │ │ ├── ...
Synapse dataset¶
This dataset could be download from this page.
To follow the data preparation setting of TransUNet, which splits original training set (30 scans) into new training (18 scans) and validation set (12 scans). Please run the following command to prepare the dataset.
unzip RawData.zip
cd ./RawData/Training
Then create train.txt and val.txt to split dataset.
According to TransUnet, the following is the data set division.
train.txt
img0005.nii.gz
img0006.nii.gz
img0007.nii.gz
img0009.nii.gz
img0010.nii.gz
img0021.nii.gz
img0023.nii.gz
img0024.nii.gz
img0026.nii.gz
img0027.nii.gz
img0028.nii.gz
img0030.nii.gz
img0031.nii.gz
img0033.nii.gz
img0034.nii.gz
img0037.nii.gz
img0039.nii.gz
img0040.nii.gz
val.txt
img0008.nii.gz
img0022.nii.gz
img0038.nii.gz
img0036.nii.gz
img0032.nii.gz
img0002.nii.gz
img0029.nii.gz
img0003.nii.gz
img0001.nii.gz
img0004.nii.gz
img0025.nii.gz
img0035.nii.gz
The contents of synapse datasets include:
├── Training
│ ├── img
│ │ ├── img0001.nii.gz
│ │ ├── img0002.nii.gz
│ │ ├── ...
│ ├── label
│ │ ├── label0001.nii.gz
│ │ ├── label0002.nii.gz
│ │ ├── ...
│ ├── train.txt
│ ├── val.txt
Then, use this command to convert synapse dataset.
python tools/dataset_converters/synapse.py --dataset-path /path/to/synapse
Noted that MMSegmentation default evaluation metric (such as mean dice value) is calculated on 2D slice image, which is not comparable to results of 3D scan in some paper such as TransUNet.
REFUGE¶
Register in REFUGE Challenge and download REFUGE dataset.
Then, unzip REFUGE2.zip and the contents of original datasets include:
├── REFUGE2
│ ├── REFUGE2
│ │ ├── Annotation-Training400.zip
│ │ ├── REFUGE-Test400.zip
│ │ ├── REFUGE-Test-GT.zip
│ │ ├── REFUGE-Training400.zip
│ │ ├── REFUGE-Validation400.zip
│ │ ├── REFUGE-Validation400-GT.zip
│ ├── __MACOSX
Please run the following command to convert REFUGE dataset:
python tools/convert_datasets/refuge.py --raw_data_root=/path/to/refuge/REFUGE2/REFUGE2
The script will make directory structure below:
│ ├── REFUGE
│ │ ├── images
│ │ │ ├── training
│ │ │ ├── validation
│ │ │ ├── test
│ │ ├── annotations
│ │ │ ├── training
│ │ │ ├── validation
│ │ │ ├── test
It includes 400 images for training, 400 images for validation and 400 images for testing which is the same as REFUGE 2018 dataset.
Mapillary Vistas Datasets¶
The dataset could be download here after registration.
Mapillary Vistas Dataset use 8-bit with color-palette to store labels. No conversion operation is required.
Assumption you have put the dataset zip file in
mmsegmentation/data/mapillaryPlease run the following commands to unzip dataset.
cd data/mapillary unzip An-ZjB1Zm61yAZG0ozTymz8I8NqI4x0MrYrh26dq7kPgfu8vf9ImrdaOAVOFYbJ2pNAgUnVGBmbue9lTgdBOb5BbKXIpFs0fpYWqACbrQDChAA2fdX0zS9PcHu7fY8c-FOvyBVxPNYNFQuM.zip
After unzip, you will get Mapillary Vistas Dataset like this structure. Semantic segmentation mask labels in
labelsfolder.mmsegmentation ├── mmseg ├── tools ├── configs ├── data │ ├── mapillary │ │ ├── training │ │ │ ├── images │ │ │ ├── v1.2 | │ │ │ ├── instances | │ │ │ ├── labels | │ │ │ └── panoptic │ │ │ ├── v2.0 | │ │ │ ├── instances | │ │ │ ├── labels | │ │ │ ├── panoptic | │ │ │ └── polygons │ │ ├── validation │ │ │ ├── images | │ │ ├── v1.2 | │ │ │ ├── instances | │ │ │ ├── labels | │ │ │ └── panoptic │ │ │ ├── v2.0 | │ │ │ ├── instances | │ │ │ ├── labels | │ │ │ ├── panoptic | │ │ │ └── polygons
You could set Datasets version with
MapillaryDataset_v1andMapillaryDataset_v2in your configs. View the Mapillary Vistas Datasets config file here V1.2 and V2.0
LEVIR-CD¶
LEVIR-CD Large-scale Remote Sensing Change Detection Dataset for Building.
Download the dataset from here.
The supplement version of the dataset can be requested on the homepage
Please download the supplement version of the dataset, then unzip LEVIR-CD+.zip, the contents of original datasets include:
│ ├── LEVIR-CD+
│ │ ├── train
│ │ │ ├── A
│ │ │ ├── B
│ │ │ ├── label
│ │ ├── test
│ │ │ ├── A
│ │ │ ├── B
│ │ │ ├── label
For LEVIR-CD dataset, please run the following command to crop images without overlap:
python tools/dataset_converters/levircd.py --dataset-path /path/to/LEVIR-CD+ --out_dir /path/to/LEVIR-CD
The size of cropped image is 256x256, which is consistent with the original paper.
BDD100K¶
You could download BDD100k datasets from here after registration.
You can download images and masks by clicking
10K Imagesbutton andSegmentationbutton.After download, unzip by the following instructions:
unzip ~/bdd100k_images_10k.zip -d ~/mmsegmentation/data/ unzip ~/bdd100k_sem_seg_labels_trainval.zip -d ~/mmsegmentation/data/
And get
mmsegmentation
├── mmseg
├── tools
├── configs
├── data
│ ├── bdd100k
│ │ ├── images
│ │ │ └── 10k
| │ │ │ ├── test
| │ │ │ ├── train
| │ │ │ └── val
│ │ └── labels
│ │ │ └── sem_seg
| │ │ │ ├── colormaps
| │ │ │ │ ├──train
| │ │ │ │ └──val
| │ │ │ ├── masks
| │ │ │ │ ├──train
| │ │ │ │ └──val
| │ │ │ ├── polygons
| │ │ │ │ ├──sem_seg_train.json
| │ │ │ │ └──sem_seg_val.json
| │ │ │ └── rles
| │ │ │ │ ├──sem_seg_train.json
| │ │ │ │ └──sem_seg_val.json
NYU¶
To access the NYU dataset, you can download it from this link
Once the download is complete, you can utilize the tools/dataset_converters/nyu.py script to extract and organize the data into the required format. Run the following command in your terminal:
python tools/dataset_converters/nyu.py nyu.zip
HSI Drive 2.0¶
You could download HSI Drive 2.0 dataset from here after just sending an email to gded@ehu.eus with the subject “download HSI-Drive”. You will receive a password to uncompress the files.
After download, unzip by the following instructions:
7z x -p"password" ./HSI_Drive_v2_0_Phyton.zip mv ./HSIDrive20 path_to_mmsegmentation/data mv ./HSI_Drive_v2_0_release_notes_Python_version.md path_to_mmsegmentation/data mv ./image_numbering.pdf path_to_mmsegmentation/data
After unzip, you get
mmsegmentation
├── mmseg
├── tools
├── configs
├── data
│ ├── HSIDrive20
│ │ ├── images
│ │ │ ├── training
│ │ │ ├── validation
│ │ │ ├── test
│ │ ├── annotations
│ │ │ ├── training
│ │ │ ├── validation
│ │ │ ├── test
│ │ ├── images_MF
│ │ │ ├── training
│ │ │ ├── validation
│ │ │ ├── test
│ │ ├── RGB
│ │ ├── training_filenames.txt
│ │ ├── validation_filenames.txt
│ │ ├── test_filenames.txt
│ ├── HSI_Drive_v2_0_release_notes_Python_version.md
│ ├── image_numbering.pdf