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Real-World Applications
Code Examples
Sample implementations and code snippets referenced in articles. Real working examples, not pseudo-code.
Browse Repositories
🛠️ AI & Computer Vision Tools
Deep Learning Frameworks
Defect Detection Platforms
📚 Learning Resources
Online Courses
- CS231n: Convolutional Neural Networks - Stanford’s famous computer vision course
- Deep Learning Specialization - Andrew Ng’s comprehensive series
- Fast.ai Practical Deep Learning - Hands-on approach to deep learning
- OpenCV Python Tutorial - Official OpenCV documentation
Books
📘 Recommended Reading (Amazon affiliate links)
Deep Learning for Vision Systems
Mohamed Elgendy
Comprehensive guide to building CV systems with deep learning. Perfect for production applications.
View on Amazon →Hands-On Machine Learning
Aurélien Géron (3rd Edition)
The bible of practical ML. Includes TensorFlow, Keras, and scikit-learn examples.
View on Amazon →Computer Vision with Python
Jan Erik Solem
Learn CV fundamentals with Python and OpenCV. Great for beginners.
View on Amazon →Other Recommended Books:
- Deep Learning for Computer Vision by Rajalingappaa Shanmugamani
- Computer Vision: Algorithms and Applications by Richard Szeliski (Free online)
Research Papers
- YOLO: Real-Time Object Detection - Joseph Redmon et al.
- ResNet: Deep Residual Learning - Kaiming He et al.
- EfficientDet: Scalable Object Detection - Mingxing Tan et al.
�️ Recommended Hardware
For Training & Development
NVIDIA RTX 4070
Best value GPU for deep learning. 12GB VRAM, perfect for training medium models.
Raspberry Pi 5 (8GB)
For edge deployment and real-time inference. Great for production prototypes.
Industrial USB Camera
High-resolution USB3.0 camera for defect detection systems. 1080p @ 60fps.
Budget Option: Start with Google Colab (free GPU) or cloud platforms before investing in hardware.
Cloud Platforms for Training
Free tier options for getting started:
- AWS Free Tier - 12 months free tier includes EC2 instances. GPU instances available with pay-as-you-go pricing.
- Google Cloud Platform - $300 credit for new users (90 days). Excellent for training with TPUs and GPUs.
- Microsoft Azure - $200 credit plus 12 months of free services. Good GPU instance selection.
All three platforms offer powerful GPU instances suitable for training defect detection models. Start with free credits to experiment before committing to paid plans.
Datasets for Training
Public Datasets
Manufacturing & Industrial:
- MVTec AD - Industry-standard anomaly detection dataset with 15 categories
- DAGM 2007 - Surface defect detection challenge dataset
- NEU Surface Defect - Steel surface defect database with 6 classes
General Computer Vision:
- ImageNet - Large-scale image database (14M+ images, 20K+ categories)
- COCO Dataset - Object detection, segmentation, and captioning (330K images)
- Open Images - Google’s massive dataset (9M images with annotations)
Development Tools
Annotation Tools
- LabelImg - Image annotation for object detection
- VGG Image Annotator (VIA) - Lightweight annotation tool
- Roboflow Annotate - Web-based collaborative annotation
Model Training Platforms
- Google Colab - Free GPU/TPU for training
- Kaggle Notebooks - Free GPU access and datasets
- Paperspace Gradient - Cloud ML development platform
Stay Updated
Blogs & Publications
- Towards Data Science - Medium publication on AI/ML
- OpenCV Blog - Latest in computer vision
- PyImageSearch - Practical CV tutorials
- The Batch (DeepLearning.AI) - AI news by Andrew Ng
Communities
- r/computervision - Reddit community
- Computer Vision Foundation - Research and conferences
- Papers With Code - Latest research with implementations
Quick Start Templates
Example Projects on GitHub
- YOLOv8 Defect Detection - Latest YOLO implementation
- TensorFlow Object Detection API - Production-ready detection
- Anomaly Detection Examples - Various approaches
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