SAM or YOLOV5-seg?

[fewshotstudy]

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In 2023, meta publish their SAM, it is a big boom in Segment task. so

Additional

In 2023, meta publish their SAM, it is a big boom in Segment task. so we students who tudy at segment ,should change our study direction?

[glenn-jocher]

@fewshotstudy hey there! 👋

It's great that you're keeping up with the latest advancements like SAM and considering their impact on your studies in segmentation. In the world of tech and AI, evolving and adapting is key. However, whether or not you should shift your study direction depends on your specific goals, projects, and interests. YOLOv5 and its variants, including YOLOv5-seg for segmentation tasks, continue to provide powerful, efficient, and accessible tools for a wide range of applications.

If your focus is on achieving state-of-the-art performance on specific segmentation tasks, exploring new methods like SAM could be beneficial. On the other hand, if your goal is to develop practical, deployable solutions, YOLOv5's balance of performance and efficiency might still serve your needs well. Plus, the YOLO community and Ultralytics are constantly working to improve and evolve the models, so staying tuned to our updates can also guide your decision.

Ultimately, diversifying your knowledge and toolkit in the AI field, including understanding both traditional and cutting-edge models, will serve you well in your studies and future career. Happy studying! 🚀

For more details on the capabilities and implementations of YOLOv5-seg, our documentation can guide you: https://docs.ultralytics.com/yolov5/.

[fewshotstudy]

Thank you for your response. After the introduction of the SAM model, most subsequent academic papers are based on SAM. Are you suggesting that if one wants to develop an easily deployable model, then basing it on yolov5-seg or other classic few shot semantic segmentation models could also lead to publishing academic papers, right?

[glenn-jocher]

@fewshotstudy Absolutely! While the SAM model has certainly made waves in the academic community, it's essential to remember that innovation and significant contributions can come from a variety of approaches, including building upon or refining existing models like YOLOv5-seg. The key to impactful academic work often lies in addressing a unique problem, improving efficiency, or enhancing performance in real-world applications. Modifying and deploying models like YOLOv5-seg for specific use cases can definitely lead to valuable academic contributions. Just ensure your work proposes a clear advancement or novel application. Happy researching! 🌟

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