Jai-wei/YOLOv8-PySide6-GUI: YoloSide - GitHub
YoloSide - YOLOv8 GUI By PySide6. Contribute to Jai-wei/YOLOv8-PySide6-GUI development by creating an account on GitHub.
Analyzing the Performance: YOLO NAS and YOLO v8 Side by Side
Nov 3, 2023 · Among the many smart ways to do this, there’s a popular method called YOLO, which stands for “You Only Look Once. YOLO is like a super-fast detective that can look at a picture and immediately...
GitHub - wideflat/yolov8-dice-detection: Train YOLOv8 object …
Train YOLOv8 object detection on a custom dataset, 6 sided dice from roboflow.
Ultralytics YOLO11 - Ultralytics YOLO Docs
Sep 30, 2024 · YOLO11 is the latest iteration in the Ultralytics YOLO series of real-time object detectors, redefining what's possible with cutting-edge accuracy, speed, and efficiency.
The Ultimate Guide to YOLO (You Only Look Once) - OpenCV.ai
Jan 25, 2024 · Explore the YOLO (You Only Look Once) model evolution, from foundational principles to the latest advancements in object detection, guiding both developers and researchers towards optimal application and understanding.
Mar 15, 2024 · Introducing a novel side-scan sonar (SSS) dataset specifically for wall detection. This paper is organized as follows: Section 2 reviews related work on object detection and knowledge distillation. Section 3 presents the YOLOX and YOLOX-ViT models and introduces a new side-scan sonar dataset.
Data Augmentation using Ultralytics YOLO
2 days ago · Color Space Augmentations Hue Adjustment (hsv_h)Range: 0.0 - 1.0; Default: 0.015; Usage: Shifts image colors while preserving their relationships.The hsv_h hyperparameter defines the shift magnitude, with the final adjustment randomly chosen between -hsv_h and hsv_h.For example, with hsv_h=0.3, the shift is randomly selected within-0.3 to 0.3.For values above 0.5, the hue shift wraps around ...
SS-YOLO: A Lightweight Deep Learning Model Focused on Side …
Jan 2, 2025 · In this paper, we improve the YOLO model in two aspects: lightweight design and accuracy enhancement. The lightweight design is essential for reducing computational complexity and resource consumption, allowing the model to be more efficient on edge devices with limited processing power and storage.
SIDE-YOLO: A Highly Adaptable Deep Learning Model for Ship …
Different from existing datasets that are based on unimodal data, the new dataset uses multi-modal remote sensing images with different resolutions. On this basis, this study introduces an adaptable and robust ship detection and recognition model, namely SIDE-YOLO.
Higher RAM usage when running inference on GPU than on CPU #7539 - GitHub
Jan 12, 2024 · I'm using my own trained weights from yolov8m and running inference. ... With gpu enabled my RAM usage is around 3.8Gb, without it (export CUDA_VISIBLE_DEVICES="") - 1.5Gb. Can I somehow reduce RAM usage when using GPU? My intent is to run small server on kubernetes node with gpu where RAM is very limited.