🧑‍💻 Computer Vision Projects Portfolio

This repository contains a collection of Computer Vision projects implemented using Keras, PyTorch, YOLO, and other libraries.
The projects cover a wide range of tasks including Classification, Segmentation, Object Detection, OCR, and Face Recognition.

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📂 Projects Overview

🔹 Classification

• 100_sports.ipynb
  Multi-class classification of sports images (100 classes).

• car bike.ipynb
  Binary classification between cars and bikes.

• Cat Dog Classifier.ipynb
  Binary classification of cats vs. dogs using EfficientNetB0 (Keras).

• Captcha Image.ipynb
  Multi-class CAPTCHA image classification with EfficientNetB0 (Keras).

• chest_xray.ipynb
  Chest X-ray classification using EfficientNetB0 (PyTorch).

• Rice_Image_Dataset.ipynb
  Rice variety classification with DenseNet201 (PyTorch).

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🔹 Segmentation

• Annotated Ultrasound Liver images Dataset.ipynb
  Multi-class liver ultrasound segmentation with U-Net (Keras).

• Common Carotid Artery Ultrasound Images.ipynb
  Single-class segmentation of carotid artery ultrasound with U-Net (Keras).

• brain-tumor-keras.ipynb
  Brain tumor segmentation using U-Net (Keras).

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🔹 Object Detection

• Car_Sign_Detection.ipynb
  Road sign detection using YOLO.

• Face Mask Detection.ipynb
  Mask vs. no-mask detection with YOLO.

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🔹 OCR

• arabic ocr.ipynb
  Arabic text recognition from images using EasyOCR.

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🔹 Face Recognition

• face recognition.ipynb
  Face recognition and identification using DeepFace.

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⚙️ Technologies & Libraries

• Keras / TensorFlow → for Classification & Segmentation
• PyTorch → for Classification (EfficientNet, DenseNet)
• YOLO → for Object Detection
• EasyOCR → for Optical Character Recognition
• DeepFace → for Face Recognition

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🎯 Purpose

These projects serve as a portfolio of Computer Vision skills, demonstrating practical implementations of common deep learning tasks across multiple domains.