mohamedamine99/Neural-Style-Transfer-with-Python

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mohamedamine99/Neural-Style-Transfer-with-Python

mohamedamine99

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This repository provides an easy-to-use implementation of neural style transfer, allowing you to apply the artistic style of one image to the content of another. Choose between the functional approach and the object-oriented programming (OOP) approach based on your preference and project requirements.

Object-tracking-and-counting-using-YOLOV8

This repository contains the code for an object detection, tracking and counting project using the YOLOv8 object detection algorithm and the SORT (Simple Online and Realtime Tracking) algorithm for object tracking. The project has been implemented using object-oriented programming principles in Python.

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YOLOv8-custom-object-detection

This repository showcases the utilization of the YOLOv8 algorithm for custom object detection and demonstrates how to leverage my pre-developed modules for object tracking and counting tasks.

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Visualizing-what-convnets-learn

This Github repository explains the impact of different activation functions on CNN's performance and provides visualizations of activations, convnet filters, and heatmaps of class activation for easier understanding of how CNN works.

Jupyter Notebook

Sensor-data-visualization-with-esp32-and-python

This projects consists of sending DHT11 sensor data acquired on a esp32 board and visualizing the data with a python script using matplotlib

Keras-CNN-cats-vs-dogs-image-classification

This project is an image classification project using a deep-learning based on Convolutional Neural Networks (CNNs) with Keras. The Dogs vs. Cats is a classic problem for anyone who wants to dive deeper into deep-learning.

Jupyter Notebook

Real-time-facial-recognition-with-python-dlib

This project uses the dlib library for facial recognition and the OpenCV library for webcam and video processing. Feel free to use and modify the code for your own projects!

YOLOv8-Object-Counter

YOLOv5-object-detection

This GitHub repository showcases simple object detection using different YOLOv5-based models on images and videos.

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YOLOv3-simple-object-detection

This GitHub repository showcases simple object detection using YOLOv3 and Tiny YOLOv3 models on images and videos.

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Birds-450-species-image-classification

This project is an image classification task of 450 bird species using the MobileNetV2 architecture.

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mohamedamine99

esp32-iot-temperature-and-humidity-monitoring-with-firebase-real-time-data-base

Facial-recognition-with-dlib

This project uses dlib library to detect and identify faces from images

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Ninja-Fruit-Like-Game-with-hand-gesture-and-opencv

Neural-Network-from-scratch

This project involves creating a Neural Network from scratch, which would have provided an understanding about the fundamental concepts behind neural networks and the implementation of these concepts in code, as well as experience in designing, training and evaluating machine learning models from scratch.

Transfer-Learning-with-MobileNetV2-for-Cats-vs-Dogs-Image-Classification

This project is an image classification project based on a transfer learning approach using with MobileNetV2 architecture.

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Digital-Lock-with-VHDL-state-machine

IOT-devices-enclosures-3d-conception-with-solidworks

Virtual-Painter-Using-OpenCV-and-Hand-Detection

My-pipeline-for-data-preprocessing-for-image-classification-tasks

Data collection and preprocessing: This includes collecting a large dataset of labeled images, and then preprocessing the images by resizing, normalizing, and augmenting them to ensure that the model is not overfitting.

INTEL-landscape-classification

This is an image classifier of Natural Scenes around the world.

Jupyter Notebook

MNIST-Digits-and-Fashion-CNN-image-classifiers

This project is a MNIST Digits and Fashion CNN image classifier.

Jupyter Notebook

Object-Detection-with-YOLOv3-and-FastAPI

A streamlined implementation showcasing object detection using YOLOv3 and FastAPI, enabling seamless integration of advanced computer vision capabilities into web applications.