smart and strong object detection ai built from scatch with some new features 🚀🚀
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Updated
Jan 27, 2023 - Jupyter Notebook
smart and strong object detection ai built from scatch with some new features 🚀🚀
This repository presents my project on Car Detection Algorithm for Self-Driving Cars using the YOLO object detection algorithm.
📷 This library is presented as a concrete implementation aimed at enabling a proof of concept. The library allows for positioning users in indoor spaces using various object recognition models.
Object detection model inferencing with DETR
A fullstack object detection project I made for fun
Object detection using the YOLO (You Only Look Once) algorithm is a popular deep-learning approach that allows for real-time, efficient, and accurate detection of multiple objects in an image or video stream. YOLOv7.cfg and YOLOv3.weights are specific configurations and pre-trained weights for two versions of the YOLO algorithm.
Is there a fish 🐟 or not? Fish Detector App and Model
An AI tool that enables the workers to monitor the social distancing in a crowded workplace.
Object Detection and Tracking using yolov3 and deepsort
Sini Dek Dekat Yanda is software dangerous object detection.
This project demonstrates object detection using YOLOv5. The model is trained on a custom dataset and can detect objects in new images. YOLOv5 is a state-of-the-art object detection model known for its speed and accuracy, making it suitable for real-time applications.
Object Detection With YOLO
Image Object Recogniser with COCO pre trained machine learning model
Custom object detection model for low clearance signs
This is a python project that recognizes truck when truck comes at the weighbridge and take the picture and update on the WhatsApp number.
Real time AI object detection and labelling app build by using ReactJS and react webcam
Explore the Computer Vision Interview Prep repository! This GitHub collection offers interview questions and answers for Data Scientists. Elevate your knowledge of computer vision, confidently tackle technical interviews, and succeed in the dynamic field of data science with a focus on computer vision applications.
This a simple comparison and benefits of the pre-trained weights model vs the random weights model
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