Obtaining meaningful results from the data set using the model trained with machine learning methods.

Trump vs Kamala - Running Inference Model AI Predicting the USA Presidential Party Winner 2024 by Allora Network Republican and Democrat

Detection and localization of Asian hornets with a CNN using PyTorch

This repository contains my practice of Introduction to Computer Vision and Image Processing lab notebooks.

#AI Tensorflow, Machine Learning and Building a data model to recognize object detection with Keras back-end. This a research work. This library is designed for everyone to learn fast.

implementation of using model.h5 on flask

For MLSA Workshop

Classificationbox Toolkit - a .Net wrapper for Machine Box's Classificationbox service and a CLI program using it to train a model and sort image folders

Image classification for dogs and cats with VGG-16 using PyTorch. Model accuracy: 99.6%. Classification API included

Comprehensive Guide Running Kuzco Inference Training LLM Models AI w/ CPU & Docker - Commit Deployment

The Advanced Food Supply Chain Forecasting System is a machine learning-powered application designed to build unified, dual-objective forecasting model for sustainable food supply chains. This simultaneously predicts food demand (orders) and food waste by leveraging real-time external signals such as weather data, fuel prices, economic indicators.

Keras pretrained models (VGG16, InceptionV3, Resnet50, Resnet152) + Transfer Learning for predicting classes in the Oxford 102 flower dataset

CineMint is a machine learning-powered tool that predicts the worldwide box office revenue of Telugu movies before release.

this project includes the model to predict the number of requests for bike rental according to the dataset.

Two-stream cnn models for action recognition on the UCF-101 dataset

in this project, a model is presented to identify and recognize customers who will leave the cycle of existing customers.

This project focuses on fine-tuning a pre-trained RoBERTa model (benjamin/roberta-base-wechsel-swahili) for Named Entity Recognition (NER) in Swahili using the MasakhaNER dataset. The goal is to identify and classify named entities such as PERSON, LOCATION, ORGANIZATION, DATE, etc., in Swahili text.

YOLOv8 for Object Detection. This model is trained for the custom data set. YOLOv8 is an Open Source SOTA model built and maintained by the Ultralytics team. You guys can use this model for your custom dataset. I tried to provide the model just like plug and play.

Classify UCF101 videos using one frame at a time with a CNN(InceptionV3)

Social Emotion Analysis | SEA