Cataract detection model

This project demonstrates image classification on the CIFAR-10 dataset using transfer learning with the pre-trained VGG16 model. The implementation is done in Google Colab and includes data preprocessing, model adaptation, training, evaluation, and result visualization using TensorFlow and Keras.

Bright Wire is an open source machine learning library for .NET with GPU support (via CUDA)

This project aims to detect speed limit signs using image processing and machine learning techniques.

Open source Python library for building bioimage analysis pipelines

The repository focuses on developing a comprehensive business opportunity analysis system that uses geospatial data, sentiment analysis, and topic modeling. The objective is to leverage these techniques to identify and evaluate potential business opportunities in area of interest.

EBOP Model Automatic input Value Estimation Neural network

Classify fashion products based on a neural-network by providing an image of the product to be classified.

Pre-trained models and utilities for deep learning on medical images in Python

Medical image analysis framework merging ANTsR and deep learning

Different projects to discover Data Science

A machine learning trigger bot for Quake3 Arena & Quake Live.

High-efficiency floating-point neural network inference operators for mobile, server, and Web

American Sign Language (ASL) Detection using CNN

A web-based AI application capable of detecting and attributing AI Art.

This repo is the homebase of a community driven course on Computer Vision with Neural Networks. Feel free to join us on the Hugging Face discord: hf.co/join/discord

Implementation of Residual Networks.

Translating Thoughts into Written Words with Robotic Finesse and Vocal Feedback

Biblioteca para manipulação de modelos de Redes Neurais

This repository is a comparative analysis of various CNN models for gesture recognition, focusing on the impact of RGB versus grayscale images and the efficacy of transfer learning with VGG-16. It includes a detailed study with custom-built CNN architectures and VGG-16 models, exploring their performance in recognizing human gestures from images.