detection-model

Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.

Robust realtime face and facial landmark tracking on CPU with Unity integration

Bounding Box Regression with Uncertainty for Accurate Object Detection (CVPR'19)

Fast anchor free Object Detection based on CenterNet (Objects As Points) and TTFNet (Training-Time-Friendly Network). Implemented in TensorFlow 2.4+.

Considering the big change that the world is facing, as well as our lives due to the COVID-19, we provide to people and companies a complete open-source tool to analyze the social distancing for streets, parks, offices, and even crowded places like malls, train stations, and others.

An image recognition/object detection model that detects handwritten digits and simple math operators. The output of the predicted objects (numbers & math operators) is then evaluated and solved.

This model is meant to help triage patients (prioritize certain patients for testing, quarantine, and medical attention) that require diagnosis for COVID-19. This model is not meant to diagnose COVID-19.

C++ trainable detection library based on libtorch (or pytorch c++). Yolov4 tiny provided now.

Real-time polyp detection, localization and segmentation in colonoscopy using deep learning (IEEE Access)

Aankh - A real time proctoring solution 🦄

Surfrider Europe's project Plastic Origins aims at mapping river plastic pollution. This citizen science project uses AI to detect litter on river banks. Results are put on a map and shared publicly. We welcome contributions from volunteers. Please, get in touch.

This library builds a graph-representation of the content of PDFs. The graph is then clustered, resulting page segments are classified and returned. Tables are retrieved formatted as a CSV.

Food object detection with base Faster R-CNN TensorFlow model with k-fold cross validation, resulting in volume estimation and producing caloric data.

This repository contains the files related to the project on frame-by-frame Drowsiness Detection in Drivers in videos using facial features.

The adversarial sample detection model based on edge noise feature

Detection Model for Epileptiform EEG Data

PanScanner is a tool to make your personal cloud storage more secure using AI

Car-Detection-in-video

detect objects from cctv camera

Implementation of YOLOv3 real time object detection model.

Building a detection model using a convolutional neural network in Keras.

This is a rumor monitoring project based on Paddle. It can also be used for short text classification, intention classification, emotion analysis, the purpose of capturing data, model, prediction and online separation. It is also a case of efficient teaching

Spam email detection

This is our repo for CS231 - Computer Vision, Spring 2021, University of Information Technology, VNU HCM

Detect faces and compare it with predefined set of faces

Execution code examples for Mut1ny's deep neural network models (https://store.mut1ny.com)

Simulating observation of an acoustic environment, while retaining noise and natural sound as independent components of the system.

It will detect any character from an image or video feed in realtime. Currently I am training the AI model to detect Minecraft characters and Destiny 2 enemies only.

Convolution Neural Network to predict Skin cancer. Skin cancer is considered as one of the most dangerous types of cancers and there is a drastic increase in the rate of deaths due to lack of knowledge on the symptoms and their prevention. Thus, early detection at premature stage is necessary so that one can prevent the spreading of cancer. Skin cancer is further divided into various types out of which the most hazardous ones are Melanoma, Basal cell carcinoma and Squamous cell carcinoma. This project is about detection of skin cancer using machine learning and image processing techniques. This model takes in image as input and tells you whether your skin cancer is Malignant or Benign. I got this dataset online. I trained this model for 25 epochs and achieved an accuracy of 89%. The Convolution Layer extracts the features of the images and is passed through a Deep Neural Network which uses Relu and sigmoid Activation functions to give us the final Output.