Pinned Repositories
onnxruntime
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Last-Mile-Delivery
Prototype and simulation addressing the complex challenges of the ’Last Mile Delivery’ problem
Multiple-Object-Tracking-_-Opencv.js-_-Camshift
Tracking multiple objects using OpenCV.js, CamShift, and YOLOv5 library for detection
ONNX_Runtime_Web_Example_on_GPU_and_GPU
This project demonstrates an ONNX Runtime Web example, comparing inference session speeds on CPU and GPU. It highlights the performance benefits of GPU acceleration in web-based machine learning applications
ONNX_Runtime_Web_Yolov8-seg_Batching
This repository demonstrates how to use ONNX Runtime to run Yolov8-seg models in the browser, including support for batched image processing. The example application displays several images and applies the Yolov8-seg model to detect objects and segment them, with results displayed directly on the webpage.
Single-Object-Tracking
Tracking single object in video using OpenCV.js, CamShift algorithm.
Student_Performance
The Student Performance dataset comprises approximately 300 rows and 15 attributes, covering various aspects of student performance. This system allows users to perform essential operations on the dataset, including insertion, deletion, updating, indexing, mapping, reducing, and selection.
yolov4-deepsort
Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.
ultralytics
Ultralytics YOLO11 🚀
yolov5
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
shimaamorsy's Repositories
shimaamorsy/Multiple-Object-Tracking-_-Opencv.js-_-Camshift
Tracking multiple objects using OpenCV.js, CamShift, and YOLOv5 library for detection
shimaamorsy/Single-Object-Tracking
Tracking single object in video using OpenCV.js, CamShift algorithm.
shimaamorsy/Last-Mile-Delivery
Prototype and simulation addressing the complex challenges of the ’Last Mile Delivery’ problem
shimaamorsy/ONNX_Runtime_Web_Example_on_GPU_and_GPU
This project demonstrates an ONNX Runtime Web example, comparing inference session speeds on CPU and GPU. It highlights the performance benefits of GPU acceleration in web-based machine learning applications
shimaamorsy/ONNX_Runtime_Web_Yolov8-seg_Batching
This repository demonstrates how to use ONNX Runtime to run Yolov8-seg models in the browser, including support for batched image processing. The example application displays several images and applies the Yolov8-seg model to detect objects and segment them, with results displayed directly on the webpage.
shimaamorsy/Student_Performance
The Student Performance dataset comprises approximately 300 rows and 15 attributes, covering various aspects of student performance. This system allows users to perform essential operations on the dataset, including insertion, deletion, updating, indexing, mapping, reducing, and selection.