Building production-ready Computer Vision and Vision-Language Models using PyTorch and state-of-the-art transformers.
💡 Passionate about bridging the gap between research and real-world applications
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Complete ViT implementation from scratch with attention visualization |
Semantic search with natural language queries using OpenAI CLIP |
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Automatic captioning with BLIP, BLIP-2, and GIT models |
Real-time detection with YOLOv8 for 80+ object classes |
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Vision Transformers ViT, CLIP, TrOCR, Swin |
Vision-Language BLIP, BLIP-2, GIT, LLaVA |
Object Detection YOLO, Mask R-CNN, Detectron2 |
Segmentation DeepLab, U-Net, SAM |
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Face Recognition FaceNet, ArcFace, MTCNN |
OCR TrOCR, EasyOCR, Tesseract |
Image Enhancement Super-Resolution, Style Transfer |
Zero-Shot Learning CLIP, Open-vocabulary models |
- 🎯 12 Production-Ready Projects in Computer Vision
- 📝 6,400+ Lines of well-documented code
- 🌟 State-of-the-Art implementations
- 🔧 Modern Architectures: Transformers, CNNs, Vision-Language Models
- 📚 Comprehensive Documentation with examples and demos
- 🎨 Interactive Web Interfaces using Gradio
- ⚡ GPU-Accelerated implementations
- 🧪 Research-to-Production pipeline
- 🔬 Exploring multi-modal foundation models
- 🚀 Optimizing inference speed for production deployment
- 📖 Studying latest research in Vision Transformers
- 🤝 Contributing to open-source CV projects
- 📝 Writing technical blog posts on Medium
- 🎓 Preparing video tutorials on YouTube
- 🎯 Published 12 CV projects in 1 day
- 🔥 Specializing in Vision Transformers and Multi-modal AI
- 📖 Always learning the latest research papers
- 🎨 Love building interactive demos for models
- 🌍 Open to collaboration on CV projects
⭐️ From selfishout - Building the future of Computer Vision, one commit at a time