https://github.com/shaheennabi/computer-vision-practices-and-mini-projects
🎇 Computer Vision Experiments 🎆 A hands-on collection of computer vision experiments 📸, featuring models like YOLO, Mask R-CNN, and GANs. 🚀 Explore applications like object detection, image segmentation, and pose estimation 🌍. Continuously updated with cutting-edge models and techniques! 🔥
https://github.com/shaheennabi/computer-vision-practices-and-mini-projects
computer-vision convolutional-neural-networks generative-adversarial-network mask-rcnn object-detection object-segmentation pose-estimation ssd variational-autoencoder
Last synced: 6 months ago
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🎇 Computer Vision Experiments 🎆 A hands-on collection of computer vision experiments 📸, featuring models like YOLO, Mask R-CNN, and GANs. 🚀 Explore applications like object detection, image segmentation, and pose estimation 🌍. Continuously updated with cutting-edge models and techniques! 🔥
- Host: GitHub
- URL: https://github.com/shaheennabi/computer-vision-practices-and-mini-projects
- Owner: shaheennabi
- License: mit
- Created: 2024-10-14T16:11:33.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-11T08:48:42.000Z (over 1 year ago)
- Last Synced: 2025-01-31T08:14:49.936Z (over 1 year ago)
- Topics: computer-vision, convolutional-neural-networks, generative-adversarial-network, mask-rcnn, object-detection, object-segmentation, pose-estimation, ssd, variational-autoencoder
- Homepage:
- Size: 14.6 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Computer Vision Exploration & Experiments
Welcome to my **Computer Vision** repository — a collection of projects and experiments exploring the full spectrum of computer vision, from image processing and feature extraction to advanced model development.
This space covers key concepts such as convolutions, padding, strides, pooling, and model-building. It includes work on image classification, object detection, segmentation, tracking, pose estimation, and generative models like autoencoders and VAEs. MLOps practices are also integrated for reproducibility and deployment.
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## What’s Inside?
- **Model Implementations**: Hands-on projects covering classification, detection, segmentation, and pose estimation.
- **Core Concepts**: Practice notebooks on feature extraction, edge detection, and image processing fundamentals.
- **Advanced Models**: Experiments with generative models and deep learning architectures.
- **MLOps**: Tools and practices for model deployment, automation, and monitoring.
- **Research Replication**: Implementations of models based on cutting-edge research papers.
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## Why Use This Repository?
- Learn by building models from scratch and experimenting with real-world datasets.
- Strengthen both theoretical understanding and practical skills in computer vision.
- Explore and apply modern techniques with clear, organized code examples.
- Understand how to take models from research to deployment using best practices.
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## License
This repository is licensed under the **MIT License**. You are free to use, modify, and share it under the terms of that license.
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**Let’s unlock the future of computer vision — one experiment at a time.**