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48 lines
1.9 KiB
Markdown
48 lines
1.9 KiB
Markdown
# Pixelwise Segmentation
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> Pixelwise segmentation for document images
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## Introduction
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This repository contains the source code for training an encoder model for document image segmentation.
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## Installation
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Either clone the repository via `git clone https://github.com/qurator-spk/sbb_pixelwise_segmentation.git` or download and unpack the [ZIP](https://github.com/qurator-spk/sbb_pixelwise_segmentation/archive/master.zip).
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### Pretrained encoder
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Download our pretrained weights and add them to a ``pretrained_model`` folder:
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https://qurator-data.de/pretrained_encoder/
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## Usage
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### Train
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To train a model, run: ``python train.py with config_params.json``
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### Ground truth format
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Lables for each pixel are identified by a number. So if you have a
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binary case, ``n_classes`` should be set to ``2`` and labels should
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be ``0`` and ``1`` for each class and pixel.
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In the case of multiclass, just set ``n_classes`` to the number of classes
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you have and the try to produce the labels by pixels set from ``0 , 1 ,2 .., n_classes-1``.
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The labels format should be png.
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If you have an image label for a binary case it should look like this:
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Label: [ [[1 0 0 1], [1 0 0 1] ,[1 0 0 1]],
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[[1 0 0 1], [1 0 0 1] ,[1 0 0 1]] ,
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[[1 0 0 1], [1 0 0 1] ,[1 0 0 1]] ]
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This means that you have an image by `3*4*3` and `pixel[0,0]` belongs
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to class `1` and `pixel[0,1]` belongs to class `0`.
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### Training , evaluation and output
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The train and evaluation folders should contain subfolders of images and labels.
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The output folder should be an empty folder where the output model will be written to.
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### Patches
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If you want to train your model with patches, the height and width of
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the patches should be defined and also the number of batches (how many patches
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should be seen by the model in each iteration).
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In the case that the model should see the image once, like page extraction,
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patches should be set to ``false``.
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