dhSegment-text-torch

This repository contains an add-on to dhSegment torch to use it with text embeddings maps.

For more details about text embeddings map, please see the following publication:

Barman, Raphaël, Ehrmann, Maud, Clematide, Simon, Ares Oliveira, Sofia, and Kaplan, Frédéric  (2020).
Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers.
Journal of Data Mining and Digital Humanities. https://arxiv.org/abs/2002.06144

Usage

This repository introduces new input code for using text embeddings maps as well as new networks.

Using dhSegment torch training script, the following config parameters must be changed:

• train_dataset and val_dataset should now be of type image_text_csv (note that patches datasets are not supported).
• train_loader and val_loader must be set to text_data_loader.
• model type should be set to text_segmentation_model.
• Either the encoder or decoder should be set to the text_ variant (currently supported architectures are text_resnet50 and text_unet).
• The text_ encoder or decoder should have the following additional parameters:
  • "embeddings_encoder": {"target_embeddings_size": 300} set to the size of the embeddings (here 300).
  • "embeddings_level": 0 set to the level in the network where the embeddings map should be input (here 0)

An example config file can be found in example_conf.json.

In addition to these changes to the config file, the training script should be modified to by adding import dh_segment_text_torch to the top.