circle detection using cnn

Re-identify animals from cam trap images

Fire Detection on images using Xception and dense CNNs: This project uses convolutional neural networks (CNNs) to detect fire in images, comparing the performance of three different models and visualizing predictions from the fittest.

This repository hosts a system designed to detect drowsiness in drivers using real-time video input. The system leverages OpenCV for face and eye detection and implements a deep learning model to assess eye closure duration and facial features to determine drowsiness.

This application is used to detect the person littering using the cameras and inform the authorities if people do not adhere to the rules of the environment

This repository contains notes and assignments about the course offered by standford CS231n taught by Fei Fei Li, Justin Johnson and Serena Young.

Project aims to create a face mask detection system using Convolutional Neural Networks (CNNs)

This project is part of the Neuroscience of Learning, Memory, Cognition course and focuses on the visual processing pathways in the brain, particularly the dorsal and ventral streams.

Using an image this app will predict your age, gender, and emotion.

Implementation of a deep learning model based on the VGG16 neural network to track faces in a video or with a camera in real time.

The objective of this project was to develop a deep learning model capable of identifying cracks in concrete. The goal was to create a robust pipeline for computer vision tasks that can accurately classify images as either containing cracks (positive) or not (negative).

Using state-of-the-art pre-trained Deep Neural Net architectures for Flower Species Recognition

Advertisment Sentiment Analysis

Age & Gender Detection Led the development of a state-of-the-art image-based Age and Gender Detection system leveraging Convolutional Neural Networks; facilitated accurate age and gender predictions from uploaded images, driving a 50% increase in user interaction and retention.

The goal of this project is to develop a system capable of recognizing sign language gestures and translating them into readable text or spoken language. This system aims to bridge the communication gap between the hearing and the deaf or hard-of-hearing community by providing real-time translation of sign language.

Develop a deep learning model using Convolutional Neural Networks (CNNs) to accurately classify handwritten digits from the MNIST dataset. The goal is to create a digit recognizer system that can correctly identify digits ranging from 0 to 9 with high accuracy.

VHDL implementation of a customizable CNN

It is an basic face emotion recognition model using CNN

Computer Vision Course By Kaggle