🚨 YOLO-NAS-Pose is here!
A new SOTA model for Keypoint Detection
What’s up, community!
I’m happy to announce the release of YOLO-NAS-Pose! See it in action 👇🏽
🧑🏽💻 Want to get right into some code? I've got you covered!
⭐️ Go and star SuperGradients, the official home of YOLO-NAS-Pose on GitHub
The community has created some fantastic content about the model!
What Advantages Does YOLO-NAS Pose Offer Over YOLOv8? by Ritesh Kanjee
Joel Nader with a quick video showing off the HuggingFace space
A new contender to the battle of pose estimation: Unveiling YOLO-NAS Pose by Henry Navarro
Introducing YOLO-NAS Pose: A Leap in Pose Estimation Technology by the team at OpenCV
Check out Nicolai Nielsen’s video about the model 👇🏽
⭐️ Go and star SuperGradients, the official home of YOLO-NAS-Pose on GitHub
Computer vision has witnessed remarkable strides, and the latest leap comes from YOLO-NAS Pose. This model isn't just an iteration; it's a redefinition of pose estimation's potential.
👤 Takes the foundational brilliance of YOLOv8 Pose and propels it to new heights. Focusing on real-time performance, it offers a unique blend of precision and speed, critical for applications in healthcare diagnostics, athletic performance analytics, and vigilant security systems.
🏗️ At its core, YOLO-NAS Pose is engineered using a state-of-the-art NAS framework, AutoNAC, which meticulously optimizes the architecture for unparalleled efficiency. This process has birthed a model with an ingenious pose estimation head seamlessly integrated into the YOLO-NAS structure.
📈 The training regimen of YOLO-NAS Pose deserves a spotlight – refined loss functions, strategic data augmentation, and a meticulously planned training schedule.
The result?
A robust model tailored for diverse computational demands and crowd densities without compromising accuracy.
🛠️ Deployment-wise, YOLO-NAS Pose stands as a versatile juggernaut.
Whether it's low-latency applications or scenarios where accuracy can't be traded off, this model adapts. It simplifies post-processing by unifying detection and pose prediction, giving us consistently reliable outputs.
🌐 And the best part? It's open-sourced. Deci has provided YOLO-NAS Pose under an open-source license with pre-trained weights for non-commercial research purposes.
This isn't just another model; it's a testament to where the field is heading. YOLO-NAS Pose is here to elevate our work, from experimental tinkering to deploying large-scale solutions.
Let's harness this technological marvel and see where it takes us. The future of pose estimation is here, looking incredibly precise and efficient.
Nov 9th: Optimizing Pose Estimation for Real-Time Performance
Join Eugene Khvedchenya, a Kaggle Grandmaster and deep learning engineer at Deci, for a live webinar that delves deep into the intricacies of pose estimation and offers unparalleled insights on optimizing it for better accuracy and real-time performance.
⭐️ Go and star SuperGradients, the official home of YOLO-NAS-Pose on GitHub
That’s it for this week!
Cheers,