16 docs tagged with "Object Detection"

Measure the inference speed of pre-trained YOLO models on Astra.

A lightweight MobileNet model optimized for people detection on high-resolution images.

A MobileNet model optimized for object detection on the COCO dataset with full resolution at 224x224 and 80 classes.

Find and locate objects in images with YOLO on the SL1600 NPU.

Find and locate objects in images with YOLO on the SL2600 NPU.

A PoseNet model using MobileNet architecture with 75% width multiplier for efficient body pose estimation.

A quantized PoseNet model using MobileNet architecture with 75% width multiplier for efficient and optimized body pose estimation.

Fine-tune YOLO11 for PPE detection with Roboflow and deploy it to Astra.

Export an Ultralytics YOLOv8 pose model and compile it for the Astra NPU.

YOLO26 NMS-free object detection models (nano and small) optimized for 320x320 resolution, with the decode head compiled onto the NPU.

A YOLOv5m model for object detection optimized for 640x480 resolution, offering a balance between speed and accuracy.

A YOLOv5s model for object detection optimized for 640x480 resolution, suitable for real-time applications.

A YOLOv5s model specialized for face detection, optimized for 640x480 resolution using ONNX with mixed quantization.

A YOLOv8n model for object detection optimized for 320x320 resolution, offering a balance between speed and accuracy.

A YOLOv8s model specialized for body pose estimation, optimized for real-time applications.

A YOLOv8s model for object detection optimized for 320x320 resolution, offering a balance between speed and accuracy.