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Generic framework for historical document processing

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Hello, I found a performance issue in the definition of `model_fn`, dh_segment/estimator_fn.py, [tf.cast(tf.shape(network_output)[1:3]](https://github.com/dhlab-epfl/dhSegment/blob/cca94e94aec52baa9350eaaa60c006d7fde103b7/dh_segment/estimator_fn.py#L200) will be calculated repeatedly during program execution, resulting in reduced efficiency. I think it should be created...

Model taking 2 hrs for one epoch having 2300 images and batch size is 1, but you guys have mentioned it took only 4 hrs to train page detection model...

Hi! I have locally upgraded this repo to Tensorflow 2.4.1. I thought it might be helpful if I shared this code with you. If you would like I can create...

I have a body of images which I want to segment into seven classes using the `utils.PredictionType.MULTILABEL` modality for predicting class memberships. I believe that I can do this based...

Only 7 labels are supported and this is not documented. Since effort can be expended to prepare training data, finding out this limitation when running train.py is wasteful.

Hello, I'm trying to reproduce the baseline detection results in your paper. What was the training/validation split used? Also, is it the case that demo/demo_cbad_config.json is the same configuration used...

My testing based on a variation of demo.py for classification of 7 labels/classes is showing choppy performance on a GPU. Excluding python post-processing and ignoring the first two inferences, I...

Hi everyone, I am thinking about adapting dhsegment to detect articles on historical newspaper title pages. Do you think dhsegment is a sensible application for this? Is there anything I...

Following the installation instructions in the docs the final step: `python demo.py` results in ``` File "demo.py", line 20 def page_make_binary_mask(probs: np.ndarray, threshold: float=-1) -> np.ndarray: ^ SyntaxError: invalid syntax...

When I run the code I get an error at line estimator.train(input.input_fn(train_input, input_label_dir=train_labels_input, num_epochs=training_params.evaluate_every_epoch, batch_size=training_params.batch_size, data_augmentation=training_params.data_augmentation, make_patches=training_params.make_patches, image_summaries=True, params=_config, num_threads=32)) The error is: Traceback (most recent calls WITHOUT Sacred internals):...