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Project description
GeDa
GeDa is a Python package that helps you to Get the Data for your project easily.
Installation
pip install geda
Usage
Using specific data provider class
from geda.data_providers.voc import VOCSemanticSegmentationDataProvider
root = "<directory>/<to>/<store>/<data>" # e.g. "data/VOC"
dataprovider = VOCSemanticSegmentationDataProvider(root)
dataprovider.get_data()
Using get_data
shortcut
from geda import get_data
root = "<directory>/<to>/<store>/<data>" # e.g. "data/VOC"
dataprovider = get_data(name="VOC_SemanticSegmentation", root=root)
dataprovider.get_data()
The
get_data
function currently supported names:MNIST
,DUTS
,NYUDv2
,VOC_InstanceSegmentation
,VOC_SemanticSegmentation
,VOC_PersonPartSegmentation
,VOC_Main
,VOC_Action
,VOC_Layout
,MPII
,COCO_Keypoints
What it does
By using dataprovider.get_data()
functionality, the data is subjected to the following pipeline:
- Download the data from source (specified by the
_URLS
variable in each module) - Unzip the files if needed (in case of
tar
,zip
orgz
files downloaded) - Move the files to
<root>/raw
directory - Find the split ids (file basenames or indices - depending on the dataset)
- Arrange files, i.e. move (or copy) files from
<root>/raw
directory to task-specific directories - [Optional] Create labels in specific format (f.e. YOLO)
Example
Resulting directory structure of the get_data(name="VOC_SemanticSegmentation", root="data/VOC")
.
└── data
└── VOC
├── raw
│ ├── Annotations
│ ├── ImageSets
│ ├── JPEGImages
│ ├── SegmentationClass
│ └── SegmentationObject
├── SegmentationClass
│ ├── annots
│ ├── images
│ ├── labels
│ └── masks
└── trainval_2012.tar
Currently supported datasets
Image classification
Image Segmentation
Keypoints detection
Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
License
Project details
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