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.

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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 InformationYolov9 Object Detection with Programmable Gradient Information (PGI) and Generalized EfficientYolov9: A Comprehensive Guide and Custom Dataset Fine-TuningYOLOv9 SOTA Machine Learning Object Detection ModelYOLOv9Unleashing the Power of YOLOv9YOLOv9 with NNCF and OpenVINOArXiv:2402.13616 Parameter efficient LLMs:

Hugging Face Paper page, 1-Bit LLMsArXiv 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!