There are 2 repositories under yolo-nas topic.
Train and Inference your custom YOLO-NAS model by Single Command Line
:rocket: :star: The list of the most popular YOLO algorithms - awesome YOLO
Object tracking pipelines complete with YOLOv9, YOLO-NAS, YOLOv8, and YOLOv7 detection and BYTETracker tracking
Inference YOLO-NAS ONNX model
This repo provides the C++ implementation of YOLO-NAS based on ONNXRuntime for performing object detection in real-time.Support float32/float16/int8 inference.
Train and Inference your custom YOLO-NAS model by Pytorch on Windows
This repository contains code for object tracking in videos using the YOLO-NAS object detection model and the DeepSORT algorithm.
This repository contains project of yolo-NAS (which gives more accurate result out of all other yolo versions), you can use these projects as a reference for building innovative projects using yolo-nas, It also contain links to some pre-trained custom model which you can use for your work
An CPP library for object detection with full processing using OpenCV DNN (for ONNX) on YOLO-NAS model.
Multi-object tracking using a tracking-by-detection scheme with YOLO-NAS + DeepSORT.
C++ implementation of YOLO-NAS utilizing OpenVINO backend
YOLO-NAS module for use with Autodistill.
Object detection Image data analysis or EDA
Go gRPC client for YOLO-NAS, YOLOv8 inference using the Triton Inference Server.
SAMYOL is a Python library that combines an object detection model and a segmentation model.
V-QUIP: A Vision-based Quick Impromptu Polling System for the Classroom
This repository contains a Streamlit web application for vehicle tracking using different SOTA object detection models. The app offers two options: YOLO-NAS with SORT tracking and YOLOv8 with ByteTrack and Supervision tracking. It enables users to upload a video file, set confidence levels, and visualize the tracking results in real-time.
This project focuses on leveraging the YOLO-NAS model for Smoke Detection.
This project demonstrates how to perform object detection and image segmentation using YOLO-NAS for object detection and SAM for image segmentation.
A Computer Vision project for Clean India, Green India.
C++ implementation of YOLO-NAS using DeepSparse
Repository containing implemetation and documentation of diploma thesis Object detection and segmentation in historical encrypted manuscripts at at Faculty of Electrical Engineering and Information Technology of Slovak University of Technology in Bratislava (FEI STU).
A collection of computer vision projects, specifically covering image classification and object detection.
A computer Vision project for avoiding potholes on road.
detecting license plates and cars in a video using YOLO NAS and YOLOv5
About YOLO_NAS is an architecture for object detection that automatically searches for optimal neural network structures, while Segment Anything Model is a versatile model for segmenting various objects in images.
This project is completed as a fulfilment for the CDS590 Consultancy Project & Practicum provided by School of Computer Sciences, USM as part of their Masters of Science in Data Science and Analytics program.