Free Download YOLO-NAS Object Detection, Tracking & Web App in Python 2023
Published 6/2023
Created by Muhammad Moin
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 48 Lectures ( 14h 26m ) | Size: 18.2 GB
YOLO-NAS, Train Custom Dataset, Object Detection, Segmentation, Tracking, Real World Projects & Web Apps in Python
What you’ll learn
YOLO-NAS : A New Foundation Model for Object Detection
What’s New in YOLO-NAS | Is YOLO-NAS the Future of Object Detection?
YOLO-NAS Implementation | Windows
Object Detection with YOLO-NAS on Images
Object Detection with YOLO-NAS on Videos
Object Detection with YOLO-NAS on Live Webcam Feed
Run YOLO-NAS in Google Colab
YOLO-NAS + DeepSORT Tracking
YOLO-NAS + DeepSORT Tracking on Custom Dataset
YOLO-NAS with SORT Object Tracking
Vehicles Counting (Entering and Leaving) using YOLO-NAS and SORT Object Tracking
Building a Computer Vision Web App & Why UI is important
Streamlit with YOLO-NAS Integration
Streamlit YOLO-NAS on Images
Streamlit YOLO-NAS on Videos
Deploy Your Streamlit Web Application
Streamlit App to Count the Vehicles Entering and Leaving
Empty Shelf Detection using YOLO-NAS
License Plate Detection using YOLO-NAS
Automatic Number Plate Recognition using YOLO-NAS and EasyOCR
Automatic Number Plate Recognition using YOLO-NAS and PaddleOCR
YOLO-NAS Multi-Cam Number Plate Recognition App
Face Detection using YOLO-NAS
Face Blurring using YOLO-NAS
Face Detection and Gender Classification using YOLO-NAS
Vehicle Intensity Heatmaps | YOLO-NAS
Integrating YOLO-NAS with Flask and Creating a WebApp
Personal Protective Equipment (PPE) Detection with YOLO-NAS
Web App- Personal Protective Equipment (PPE) Detection
Web App- Vehicles Counting (Entering and Leaving) using YOLO-NAS and SORT Object Tracking
Requirements
Python Programming experience is an advantage but not required
Laptop/PC
Description
Welcome to the YOLO-NAS: The Ultimate Course for Object Detection & Tracking with Hands-on Projects, Applications & Web App Development using Flask and Streamlit. YOLO-NAS is the state-of-the-art object detection model. It is the latest version of YOLO and is an improvement over the previous versions in terms of speed, accuracy and efficiency. What will you learn in this course:YOLO-NAS : A New Foundation Model for Object DetectionWhat’s New in YOLO-NAS | Is YOLO-NAS the Future of Object Detection?YOLO-NAS Implementation | WindowsObject Detection with YOLO-NAS on ImagesObject Detection with YOLO-NAS on VideosObject Detection with YOLO-NAS on Live Webcam FeedRun YOLO-NAS in Google ColabYOLO-NAS + DeepSORT TrackingYOLO-NAS + DeepSORT Tracking on Custom DatasetYOLO-NAS with SORT Object TrackingVehicles Counting (Entering and Leaving) using YOLO-NAS and SORT Object TrackingBuilding a Computer Vision Web App & Why UI is importantStreamlit with YOLO-NAS IntegrationStreamlit YOLO-NAS on ImagesStreamlit YOLO-NAS on VideosDeploy Your Streamlit Web ApplicationStreamlit App to Count the Vehicles Entering and LeavingEmpty Shelf Detection using YOLO-NASLicense Plate Detection using YOLO-NASAutomatic Number Plate Recognition using YOLO-NAS and EasyOCRAutomatic Number Plate Recognition using YOLO-NAS and PaddleOCRYOLO-NAS Multi-Cam Number Plate Recognition AppFace Detection using YOLO-NASFace Blurring using YOLO-NASFace Detection and Gender Classification using YOLO-NASVehicle Intensity Heatmaps | YOLO-NASIntegrating YOLO-NAS with Flask and Creating a WebAppPersonal Protective Equipment (PPE) Detection with YOLO-NASWeb App- Personal Protective Equipment (PPE) DetectionWeb App- Vehicles Counting (Entering and Leaving) using YOLO-NAS and SORT Object Tracking
Who this course is for
For Everyone who is interested in Computer Vision
For Everyone who study Computer Vision and want to know how to use YOLO for Object Detection
For Everyone who wants to learn the latest YOLO-NAS version
Building Deep learning Apps with Computer Vision
Building Web Apps with Computer Vision
Homepage
https://www.udemy.com/course/yolo-nas-object-detection-tracking-web-app-in-python-2023/
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