YOLOv9: Computer vision is alive and well

While everyone is super hyped about generative AI, computer vision researchers have been working in the background on significant advancements in deep learning architectures. YOLOv9 was just released with some noteworthy advancements relevant to parameter efficient models. In this episode, Chris and Daniel dig into the details and also discuss advancements in parameter efficient LLMs, such as Microsofts 1-Bit LLMs and Qualcomm’s new AI Hub.

While everyone is super hyped about generative AI, computer vision researchers have been working in the background on significant advancements in deep learning architectures. YOLOv9 was just released with some noteworthy advancements relevant to parameter efficient models. In this episode, Chris and Daniel dig into the details and also discuss advancements in parameter efficient LLMs, such as Microsofts 1-Bit LLMs and Qualcomm’s new AI Hub.

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Featuring:

Chris Benson – Twitter, GitHub, LinkedIn, WebsiteDaniel Whitenack – Twitter, GitHub, Website

Show Notes:

YOLOv9:

Yolov9: Learning What You Want to Learn Using Programmable Gradient Information
Yolov9 Object Detection with Programmable Gradient Information (PGI) and Generalized Efficient
Yolov9: A Comprehensive Guide and Custom Dataset Fine-Tuning
YOLOv9 SOTA Machine Learning Object Detection Model
YOLOv9
Unleashing the Power of YOLOv9
YOLOv9 with NNCF and OpenVINO
ArXiv:2402.13616

Parameter efficient LLMs:

Hugging Face Paper page, 1-Bit LLMs
ArXiv paper: “The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits”
Qualcomm AI Hub

Something missing or broken? PRs welcome!