2vin
Enhancing YAT's Usability for Large-Scale Annotation Projects
Dear YAT Contributors,
I trust this message finds you in good spirits. I am reaching out to discuss potential enhancements to the Yolo Annotation Tool (YAT), which I believe could significantly improve its utility for large-scale annotation projects.
Having utilised YAT extensively, I appreciate its straightforward approach to annotating datasets for YOLO using OpenCV. However, as the scale of annotation projects increases, certain limitations become apparent. To address these, I propose the following enhancements:
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Batch Processing Capabilities: The ability to annotate multiple images or video frames in a batch mode would greatly expedite the annotation process. This could include the option to apply the same annotation to a sequence of frames where the object of interest remains relatively static.
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Integration with Cloud Storage: For collaborative and distributed annotation efforts, the ability to directly read from and write to cloud storage services would be invaluable. This would facilitate a seamless workflow for teams working remotely.
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Advanced Class Management: As projects grow, so does the number of classes. An enhanced class management system that allows for the categorisation and searching of classes would streamline the annotation process for complex datasets.
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Undo/Redo Functionality: Mistakes are inevitable in any manual process. The inclusion of undo/redo functionality would allow annotators to quickly correct errors without needing to reset the entire frame's annotations.
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Inter-Frame Object Tracking: For video annotation, the ability to track objects across frames and automatically suggest annotations based on previous frames would significantly reduce the manual effort required.
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User Interface Customisation: Allowing users to customise the interface, such as changing the layout or creating annotation templates, would enhance the user experience and increase efficiency.
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Annotation Export Options: Expanding the range of export formats for annotations to include other popular formats used in machine learning could make YAT a more versatile tool for various frameworks beyond YOLO.
I am keen to hear your thoughts on these suggestions and explore how we might collaborate to bring these enhancements to fruition. I believe that by addressing these areas, YAT could become an even more powerful tool for the computer vision research community.
Thank you for your time and consideration. I look forward to the possibility of contributing to the YAT project.
Best regards, yihong1120
