This Discord chatbot is incredibly versatile. Powered incredibly fast Groq API

A generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception.

OSMDeepOD - OpenStreetMap (OSM) and Machine Learning (Deep Learning) based Object Detection from Aerial Imagery (Formerly also known as "OSM-Crosswalk-Detection").

ROS2 nodes for computer vision tasks in Tensorflow

Official implementation of ColonSegNet: Real-Time Polyp Segmentation (Used in NVIDIA Clara Holoscan App for Polyp Segmentation)

A Flask Pneumonia Detection web app from chest X-Ray Images using CNN

Selenium automation script to draw any image into HTML canvas using edge detection algorithm

Use your classification neural network for object detection and localization

Real-Time Social Distance Monitoring tool using Computer Vision

[IEEE TCSVT 2024] Preprocessing Enhanced Image Compression for Machine Vision

NautilusChess is a lightweight Python chess bot with a GUI, screen-based move recognition, and Minimax logic. It plays on platforms like Lichess and Chess.com using pixel-level board tracking, and supports external chess engines and opening books.

Discord AI bot with a hybrid command system, imagination generation, free LLM model, mention recognition, smart message handling, channel-specific responses, GPT integration, secure credential management, & web access for unparalleled functionality.

Image Object Detection using pretrained model FastAPI and UI by Streamlit

Python script that automates mouse clicks using image detection in screen

An Auto Clicker with Image Detection for Windows coded with Python.

A bot that uses Computer Vision to play Clonehero

Python functions to import and display the images and labels from the Kitty Object Detection Evaluation 2012 Dataset.

Distinguish between real and AI-generated images.

This is a simple face-detection code written in python using opencv module which can detect your face as well your face part like eyes , mouth etc.

This system uses a perceptual hashing function, similar to Apple's CSAM Detection. Instead of generating image hashes using NeuralHash, it uses a difference hash (dHash), which is simpler and less computationally intensive as it doesn't require neural networks. Since we don't have the same privacy constraints as Apple, we will be using nearest n…