This project explores various deep learning architectures for object recognition. It's structured into three main approaches: Feedforward Neural Networks (ffNN), Convolutional Neural Networks (CNN), and Transfer Learning (tLearning). Each method is designed to evaluate different strategies and improve the accuracy of object recognition.
Object recognition in photo captured from webcam
Identify and classify objects in real-time video streams using TensorFlow and OpenCV. This project is designed for applications like security systems, robotics, and interactive installations, combining the power of TensorFlow for deep learning with OpenCV's webcam interaction.
Object Recognition & Manipulation Program
🌐👁️An innovative project using computer vision for instant object recognition in live video streams. This project uses computer vision techniques to detect objects in real-time video streams. 🚀👁️
A Smart Vehicle WrongWay Detection and Tracking using YOLOV8 and Sort Algo
A Python implementation of object recognition using a pre-trained convolutional neural network called ResNet50. The goal of the project is to recognize objects in images accurately. To achieve this, the code uses various libraries such as NumPy, Pandas, PIL, Matplotlib, and OpenCV.
A robot that pushes a ball.
'BRACU CSE461: Introduction to Robotics' Project 1
A Tensorflow Lite based app from where you can indentify objects.
Implementation of paper :Lightweight Deep Convolutional Network for Tiny Object Recognition
densenet-bc chainer v2 implementation
A camera object recognition demo using the CoreML & AVCam framework. Required XCode 9 & iOS 11.
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