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Prepare datasets

It is recommended that you symlink the dataset to JSeg/dataset.

ADE20K

The ADE20K training and validation set can be downloaded from here link. You can download the test set from here.

├── ADEChallengeData2016
│   ├── annotations
│   │   ├── training
│   │   ├── validation
│   ├── images
│   │   ├── training
│   │   ├── validation

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.

python tools/convert_datasets/cityscapes.py dataset/cityscapes
├── cityscapes
│   ├── leftImg8bit
│   │   ├── train
│   │   ├── val
│   ├── gtFine
│   │   ├── train
│   │   ├── val

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.

python tools/convert_datasets/voc_aug.py data/VOCdevkit data/VOCdevkit/VOCaug
├── VOCdevkit
│   ├── VOC2012
│   │   ├── JPEGImages
│   │   ├── SegmentationClass
│   │   ├── ImageSets
│   │   │   ├── Segmentation
│   ├── VOC2010
│   │   ├── JPEGImages
│   │   ├── SegmentationClassContext
│   │   ├── ImageSets
│   │   │   ├── SegmentationContext
│   │   │   │   ├── train.txt
│   │   │   │   ├── val.txt
│   │   ├── trainval_merged.json
│   ├── VOCaug
│   │   ├── dataset
│   │   │   ├── cls

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.

Original file structure

├── 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

After decompression

├── iSAID
│   ├── train
│   │   ├── images
│   │   │   ├── *.png
│   │   ├── Semantic_masks
│   │   │   ├── *_instance_color_RGB.png
│   ├── val
│   │   ├── images
│   │   │   ├── *.png
│   │   ├── Semantic_masks
│   │   │   ├── *_instance_color_RGB.png
│   ├── test
│   │   ├── images
│   │   │   ├── *.png

Processed file structure

├── iSAID_Patches
│   ├── train
│   │   ├── images
│   │   │   ├── *_sub_img.png
│   │   ├── Semantic_masks
│   │   │   ├── *_sub_img_instance_color_RGB.png
│   ├── val
│   │   ├── images
│   │   │   ├── *_sub_img.png
│   │   ├── Semantic_masks
│   │   │   ├── *_sub_img_instance_color_RGB.png
│   ├── test
│   │   ├── images
│   │   │   ├── *_sub_img.png

python tools/convert_datasets/isaid.py --src=./datasets/iSAID --target=./datasets/iSAID_Patches

In our default setting (patch_width=800, patch_height=800,　overlap_area=200), it will generate 28029 images for training and 9512 images for validation.

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

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.

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.

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.