Get the most out of your training, scoring, algorithms and frameworks on Intel® architecture for Deep Learning and Artificial Intelligence.
URL: https://github.com/vibin17604/Freshness_Detection.git
The area of focus for your idea/solution is automating the detection of fruit and vegetable freshness using a device with an attached webcam. The aim is to simplify and streamline the sorting process by categorizing the produce into two main categories: good and rotten
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URL: https://github.com/iurk1702/intelOneAPI_submission
Refugee status determiner using OneAPi toolkit
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URL: https://github.com/sayashraaj/oneapi
Obtain a differentially private dataset mirroring the original while safeguarding individual privacy
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URL: https://github.com/scholar-2001/Semi_labelling/tree/master
Creating an open source auto labelling tool that labels image using pretrained models. We can then edit those labelling too using this tool. Currently, only supported for yolov5 models but working on expanding it. Also, it is using stable PyTorch in backend for better acceleration by intel libraries
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URL: https://github.com/VinayVaishnav/Water_Quality_Prediction
Using Intel's AI Analytics Toolkit we tried to predict freshwater quality, achieving an F1 score of 0.81786 with TabNet ensemble model, addressing data challenges and optimizing performance for sustainable water assessment.
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URL: https://github.com/Praveen880890/Social-Distancing-Using-Deep-Learning
This project utilizes the OpenVINO (Open Visual Inference and Neural Network Optimization) toolkit and Yolov8 object detection model to implement social distancing enforcement using computer vision and deep learning techniques.
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URL: https://github.com/Senthil-Riddhish/Intel-One-Api-Hackathon
Harnessed the power of Intel technologies to enhance the capabilities of the YOLOv5 algorithm for object detection. By leveraging Intel's optimized libraries and frameworks, such as Intel oneDAL, Intel optimized PyTorch, and the SYCL/DPC++ libraries, we have achieved superior performance, accuracy,
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URL: https://github.com/viswanathsr-04/diabetic-foot-ulcer
A mini project based on Detecting Diabetic Foot Ulcer using Deep Learning Methods. The concepts like Faster R-CNN and ResNet-50 Architecture are used in the project. Gradio is used to deploy the model.
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URL: https://github.com/subinoybiswas/RishiGPT
RishiGPT is an AI-powered chatbot designed to provide insightful and wise responses based on the knowledge and teachings of a Rishi or Indian Monk. This project combines the power of the OpenAI API and the CreateChatCompletion method from the OpenAI GPT-3.5 language model. The front end is built usi
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URL: https://github.com/lokeshwaran173/intelunnati_-New-Floraison-
Analyzing Indian road accidents using GIS and Ministry of Road Transport data (up to 2021). Identifies hotspots, patterns, and high-risk areas for informed road safety strategies by policymakers, law enforcement, and urban planners to reduce accidents and enhance safety.
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URL: https://github.com/Ashutoshjha0007/intelunnati_AshutoshJha/tree/main
The Fashion-MNIST dataset is used as a standard for assessing how well image classification models perform. Classifying fashion items presents a difficult task that is applicable to real-world applications. I have tried to add to the body of knowledge in the field of computer vision by creating
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URL: https://github.com/shruthi2608/CureQuest/
AI revolutionizes health: Predict. Prevent. Prevail
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URL: https://github.com/SSKlearns/IntelOneAPI/
Trained ResNet-50 for 5 epochs, optimized with IPEX for 15 epochs. Converted to ONNX, further refined with OpenVINO, all on Intel Dev Cloud. The Gradio app takes video input and detects accidents.
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URL: https://github.com/balasuriyaranganathan/Network-Traffic-Analysis-using-Machine-learning
This Python script focuses on network traffic analysis and anomaly detection using autoencoder models. It preprocesses network data, trains an autoencoder for feature extraction, and identifies anomalies in network traffic patterns.
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URL: https://github.com/nb0309/Network-Traffic-Analysis-using-Machine-learning
Anomaly detection is a sophisticated technique used to identify patterns and behaviors that deviate significantly from normal network activity. In computer networks, normal behavior is typically established by observing historical data, which is then used as a benchmark for detecting anomalies.
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URL: https://github.com/nb0309/trafficsignrecognition
Traffic sign recognition uses computer vision & machine learning to detect & classify road signs. Enhances road safety, aids drivers, and aids autonomous vehicles. Challenges include varying conditions & real-time processing. Crucial for modern transportation systems.
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URL: https://github.com/Apollo9999/Smart-Farming-Bot-Using-IOT
An IOT based Agriculture solution which automates the process of farming, making scalable urbanized farms come true In order to feed today's global population, we currently need farmland equivalent to the size of South America. Despite the exorbitant amount of land already allocated to agriculture
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URL: https://yashz74.github.io/posenet-demo-ml5.js/
PoseNet Demo: A real-time webcam-based pose estimation project using ml5.js. Detect body keypoints, overlay images, and explore creative possibilities. Deployed on GitHub Pages.
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URL: https://github.com/saianurag234/APS-Sensor-Fault-Detection
The APS Sensor, a critical element in heavy-duty vehicles, facilitates brake pad pressure by converting compressed air into piston force for deceleration. This study investigates the correlation between component failures and the APS.
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URL: https://github.com/AghoraGuru/1API
An interactive image upscaling application, combining Gradio and OpenVINO. The gradio app offers users the ability to upload images and experience real-time upscaling.
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