Exploring YOLOv9's Programmable Gradient Information and Efficient Architectures for Computer Vision Models

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 – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

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!