Initial commit

LICENSE

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+MIT License
+
+Copyright (c) 2022 Reece Walsh
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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----
-license: mit
----
+# Image Difference Segmentation
+
+For the main repository and code, please refer to the [GitHub Repo](https://github.com/Brikwerk/image-difference-segmentation).
+
+This project enables creation of large binary segmentation datasets through use of image differences. Certain domains, such as comic books or manga, take particularly well to the proposed approach. Creating a dataset and training a segmentation model involves two manual steps (outside of the code in this repository):
+
+1. Finding and sorting suitable data. Ideally, your data should have two or more classes wherein the only difference between the classes should be the subject that is to be segmented. An example would be an English page from a comic and a French page from the same comic.
+
+2. Segmentation masks must be manually created for a small number of image differences. Using a pretrained DiffNet requires only 20-50 new masks. Re-training DiffNet from scratch requires 100-200 masks. For quickly generating binary segmentation masks, [simple-masker](https://github.com/Brikwerk/simple-masker) was written/used.
+
+## Prerequisites
+
+The following must be on your system:
+
+- Python 3.6+
+- An accompanying Pip installation
+- Python and Pip must be accessible from the command line
+- An NVIDIA GPU that is CUDA-capable (6GB+ of VRAM likely needed)
+
+## Using a Pretrained Model
+
+### Downloading the Weights File
+
+Weights for this project are hosted at [HuggingFace](https://huggingface.co/brikwerk/image-difference-segmentation) under `weights` directory. Currently, a DiffNet instance trained on text differences is provided. To use this model, download it and move it to the weights directory in your local copy of this repository.
+
+### Using Pretrained Weights
+
+Pretrained weights can be used in the `batch_process.py` file and the `evaluate.py` file. For both files, specify the path to your weights file using the `--weights_path` CLI argument.
+
+## License
+
+MIT

weights/DiffNet-Text.pth

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