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iris-semantic-segmentation

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tanguy-jeanneau commited on Dec 14, 2023

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@@ -97,6 +97,16 @@ Thorough examination of the results enabled us to pinpoint situations where the
 - Segmenting images with high specular reflection comming usually from glasses may lead to bad segmentation map predictions.
 - Data based on which the model was trained were captured in the constrained environment with cooperative users. Therefore, in practise model is expected to produce poor segmentation maps for cases like: offgazes, misaligned eyes, blurry images etc.
 ## Further reading
 1. Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2018). UNet++: A nested U-Net Architecture for Medical Image Segmentation. (https://arxiv.org/abs/1807.10165v1)

 - Segmenting images with high specular reflection comming usually from glasses may lead to bad segmentation map predictions.
 - Data based on which the model was trained were captured in the constrained environment with cooperative users. Therefore, in practise model is expected to produce poor segmentation maps for cases like: offgazes, misaligned eyes, blurry images etc.
+## License
+Unless otherwise specified, the contents of this repository are dual-licensed under either:
+- MIT License (LICENSE-MIT)
+- Apache License, Version 2.0 (LICENSE-APACHE)
+at your option. This means you may select the license you prefer to use.
+Any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.
 ## Further reading
 1. Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2018). UNet++: A nested U-Net Architecture for Medical Image Segmentation. (https://arxiv.org/abs/1807.10165v1)