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| title: MV+ (Machine Vision Plus) |
| emoji: 🔬 |
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| # 🔬 MV+ (Machine Vision Plus) |
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| **A Novel Paradigm for Advanced Computer Vision** |
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| MV+ (Machine Vision Plus) represents a groundbreaking approach to building computer vision models that revolutionize how we extract and utilize visual information. Unlike traditional computer vision systems that rely solely on spatial features, MV+ introduces a paradigm shift by combining **spatial and structural features** derived from transient images (1D time-resolved data) to make more accurate and robust inferences. |
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| ## 🎬 Demo |
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| <p align="center"> |
| <img src="https://huggingface.co/spaces/mvplus/README/resolve/main/demo_loop.gif" alt="MV+ Demo" width="100%" style="max-width: 720px; border-radius: 8px;" /> |
| </p> |
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| ## 🌟 Key Features |
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| ### 🎯 **Dual-Feature Architecture** |
| - **Spatial Features**: Traditional 2D/3D spatial information from static images |
| - **Structural Features**: Novel 1D time-resolved transient image data |
| - **Fusion**: Intelligent combination of both feature types for superior performance |
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| ### 🚀 **Advanced Vision Models** |
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| MV+ provides state-of-the-art implementations across multiple computer vision domains: |
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| #### **Tested Object Detection models with material classifier for dual detection** |
| - **DINOv3 Custom**: Self-supervised vision transformer for robust object detection |
| - **YOLOv3 Custom**: Real-time object detection with custom training |
| - **YOLOv8 Custom**: Latest YOLO architecture with enhanced accuracy |
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| #### **Material Analysis** |
| - **Material Detection Head**: Classification of flat homogeneous surfaces |
| - **Material Purity Detection**: Fluid purity analysis (e.g., homogenized milk) |
| - **Natural Material Detection**: Identification of natural vs. synthetic materials |
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| #### **Specialized Detection** |
| - **Flat Surface Detection**: Precise identification of planar surfaces |
| - **Spatiotemporal Detection**: Time-series based motion and change detection |
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| ### 🔬 **Research Innovation** |
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| MV+ introduces a novel methodology that: |
| - Extracts structural information from transient 1D signals |
| - Combines temporal and spatial features for enhanced understanding |
| - Achieves superior performance compared to conventional single-modality approaches |
| - Enables new applications in material science, quality control, and industrial inspection |
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| ## 📊 Applications |
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| ### Industrial Quality Control |
| - **Material Purity Verification**: Detect impurities in fluids and materials |
| - **Surface Quality Assessment**: Analyze flat surfaces for defects |
| - **Real-time Inspection**: Automated quality control in manufacturing |
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| ### Scientific Research |
| - **Material Classification**: Distinguish between natural and synthetic materials |
| - **Structural Analysis**: Extract structural features from transient signals |
| - **Multi-modal Fusion**: Combine spatial and temporal information |
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| ### Computer Vision Research |
| - **Novel Architecture**: Explore new paradigms in vision model design |
| - **Feature Extraction**: Advanced techniques for multi-modal feature fusion |
| - **Benchmarking**: State-of-the-art performance on various datasets |
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| ## 🛠️ Technical Architecture |
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| ### Model Components |
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| 1. **Spatial Feature Extractor**: Processes traditional 2D/3D image data |
| 2. **Structural Feature Extractor**: Analyzes 1D time-resolved transient signals |
| 3. **Feature Fusion Module**: Intelligently combines spatial and structural features |
| 4. **Inference Engine**: Makes predictions based on fused feature representations |
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| ### Supported Frameworks |
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| - **PyTorch**: Primary deep learning framework |
| - **YOLO**: Real-time object detection |
| - **DINOv3**: Self-supervised vision transformers |
| - **Custom Architectures**: Specialized models for specific applications |
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| ## 📈 Performance Highlights |
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| - **High Accuracy**: State-of-the-art performance on material classification tasks |
| - **Robust Detection**: Improved reliability through multi-modal feature fusion |
| - **Real-time Processing**: Efficient inference suitable for industrial applications |
| - **Generalization**: Strong performance across diverse datasets and scenarios |
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| ## 🔗 Resources |
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| ### Publications |
| For detailed information about the MV+ methodology, architecture, and experimental results, please refer to the associated research publications. |
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| ### Datasets |
| MV+ includes curated datasets for: |
| - Material detection and classification |
| - Object detection and recognition |
| - Surface quality assessment |
| - Fluid purity analysis |
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| ### Models |
| Pre-trained models available for: |
| - DINOv3-based object detection |
| - YOLOv3/YOLOv8 custom detectors |
| - Material classification models |
| - Spatiotemporal analysis models |
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| ## 🎓 Research Impact |
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| MV+ represents a significant advancement in computer vision research by: |
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| 1. **Introducing Novel Paradigm**: First systematic approach to combining spatial and structural features from transient images |
| 2. **Enabling New Applications**: Opens possibilities for material science, quality control, and industrial inspection |
| 3. **Improving Performance**: Demonstrates superior results compared to conventional single-modality approaches |
| 4. **Advancing the Field**: Contributes to the evolution of multi-modal computer vision systems |
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| <div align="center"> |
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| *Project designed and developed by **Deborah Akuoko** as part of PhD thesis under the supervision of **Dr. Istvan Gyongy** of **University of Edinburgh*** |
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| </div> |
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