Pose Estimation
YOLO11 is here! Continuing the legacy of the YOLO series, YOLO11 sets new standards in speed and efficiency. With enhanced architecture and multi-task capabilities, it outperforms previous models, making it
Unveiling a significant breakthrough in computer vision, Deci introduces YOLO-NAS Pose, the latest evolution in Pose Estimation technology. Building on the foundations of the acclaimed YOLO-NAS, this advanced model stands
In this article, fine-tune the YOLOv8 Pose model for Animal Pose Estimation.
PaddlePaddle: Welcome to our guide of machine learning frameworks, where we’ll examine PaddlePaddle, TensorFlow, and PyTorch. Recent benchmark tests have revealed PaddlePaddle as a potential frontrunner, showcasing benchmark speeds that
Build an AI fitness trainer application that analyzes squats using MediaPipe’s Pose solution and prompts appropriate feedback.
CenterNet: Object as Points is one of the milestones in the anchor-free (anchorless) object detection algorithm. Anchor-free object detection is more generalizable in other computer vision tasks, e.g., pose estimation,
YOLOv7 Pose is a real time, multi-person, keypoint detection model capable of giving highly accurate pose estimation results.
Mean Average Precision (mAP) is a performance metric used for evaluating machine learning models. We have covered mAP evaluation in detail to clear all your confusions regarding model evaluation metrics.
A technical review of YOLOv7 paper along with inference report. YOLOv7 Pose detection code included.
Recently, we had a lot of fun playing with Body Posture Detection using MediaPipe POSE. We built a poor posture alert application using OpenCV and MediaPipe. Continue reading the article
