super-gradients/documentation/source/YoloNASPoseQuickstart.md

YOLO-NAS-POSE Quickstart

Deci’s leveraged its proprietary Neural Architecture Search engine (AutoNAC) to generate YOLO-NAS-POSE - a new object detection architecture that delivers the world’s best accuracy-latency performance.

The YOLO-NAS-POSE model incorporates quantization-aware RepVGG blocks to ensure compatibility with post-training quantization, making it very flexible and usable for different hardware configurations.

In this tutorial, we will go over the basic functionality of the YOLO-NAS-POSE model.

Instantiate a YOLO-NAS-POSE Model

from super_gradients.training import models
from super_gradients.common.object_names import Models

yolo_nas_pose = models.get(Models.YOLO_NAS_POSE_L, pretrained_weights="coco_pose")

Predict

prediction = yolo_nas_pose.predict("https://deci-pretrained-models.s3.amazonaws.com/sample_images/beatles-abbeyroad.jpg")
prediction.show()

Export to ONNX & TensorRT

yolo_nas_pose.export("yolo_nas_pose.onnx")

Please follow our Pose Estimation Models Export tutorial for more details.

Evaluation using pycocotools

We provide example notebook to evaluate YOLO-NAS POSE using COCO protocol. Please check Pose Estimation Models Export tutorial for more details.