Update file README.md

README.md

@@ -56,15 +56,6 @@ YOLOv5 model evaluation on the test dataset, the table shows the quantitative re
 | YOLOv5m-seg.pt |           0.987 |        0.977 |       0.989 |          0.856 |            0.977 |         0.963 |        0.982 |           0.742 |
 | YOLOv5x-seg.pt |           0.986 |        0.979 |       0.989 |          0.856 |            0.978 |         0.967 |        0.982 |           0.743 |
 
-%## Support
-%For enquiry related to the project, please contact: Amit Sharma (sharma@iob.rwth-aachen.de), Doctoral Researcher, RWTH Aachen University, Germany.
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-%## Authors and acknowledgment
-%I am eternally greatful to Thomas Echterhof, Moritz Eickhoff, and Christian Wuppermann at Department for Industrial Furnace & Heat Engineering, RWTH Aachen %University, for their constant support and review of the scientific work for this project. Special acknowledgement to Lilly Schulte for helping with setup of %this GitLab repository.
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-%## License
-%For open source projects, say how it is licensed.
-
 ## References
 [1] Haas, Tim and Schubert, Christian and Eickhoff, Moritz and Pfeifer, Herbert. BubCNN: Bubble detection using Faster RCNN and shape regression network.     Chemical Engineering Science, 2020.
 [2] G. Jocher, et al., ultralytics/yolov5: v6.0 - YOLOv5n ‘Nano’ models, Roboflow integration, TensorFlow export, OpenCV DNN support, https://doi.org/10.5281/ zenodo.5563715, Oct. 2, https://github.com/ultralytics/yolov5