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https://github.com/bjornmelin/ml-vision-lab

👁️ Production-grade computer vision implementations. Real-world applications in image processing, object detection, and video analytics with GPU acceleration. 📸
https://github.com/bjornmelin/ml-vision-lab

computer-vision cuda deep-learning image-processing object-detection opencv pytorch video-analytics

Last synced: 7 months ago
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👁️ Production-grade computer vision implementations. Real-world applications in image processing, object detection, and video analytics with GPU acceleration. 📸

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README 

          
# ML Vision Lab 👁️



[![Python](https://img.shields.io/badge/python-3.11%2B-blue.svg)](https://www.python.org/downloads/)

[![PyTorch](https://img.shields.io/badge/pytorch-2.3%2B-red.svg)](https://pytorch.org/)

[![TensorFlow](https://img.shields.io/badge/tensorflow-2.15%2B-orange.svg)](https://tensorflow.org/)

[![OpenCV](https://img.shields.io/badge/opencv-5.0%2B-green.svg)](https://opencv.org/)

[![CUDA](https://img.shields.io/badge/cuda-12.2%2B-green.svg)](https://developer.nvidia.com/cuda-toolkit)

[![Projects](https://img.shields.io/badge/projects-5%2B-brightgreen.svg)](https://github.com/BjornMelin/ml-vision-lab/pulse)

[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)



> Modular computer vision implementations - A collection of production-grade vision systems spanning multiple domains.



[Featured Projects](#-project-matrix) • [Installation](#-installation) • [Quick Start](#-quick-start) • [Contributing](#-contributing)



## 📑 Table of Contents



- [Project Organization](#-project-organization)

- [Core Features](#-core-features)

- [Prerequisites](#-prerequisites)

- [Tech Stack](#-tech-stack)

- [Installation](#-installation)

- [Quick Start](#-quick-start)

- [Project Matrix](#-project-matrix)

- [Development Standards](#-development-standards)

- [Contributing](#-contributing)

- [Documentation](#-documentation)

- [Benchmarks](#-benchmarks)

- [Versioning](#-versioning)

- [Authors](#-authors)

- [Citation](#-citation)

- [License](#-license)

- [Acknowledgments](#-acknowledgments)



## 🗂️ Project Organization



```mermaid

graph TD

    A[ML Vision Lab] --> B[projects]

    A --> C[core]

    A --> D[docs]

    B --> E[food-classification]

    B --> F[object-detection]

    B --> G[medical-imaging]

    B --> H[satellite-analysis]

    C --> I[utils]

    C --> J[models]

    C --> K[pipelines]

    D --> L[api]

    D --> M[guides]

    D --> N[architecture]

```



```

ml-vision-lab/

├── projects/               # Individual vision projects

│   ├── food-classification/  # Food analysis system

│   ├── object-detection/      # Real-time detection

│   ├── medical-imaging/       # DICOM processing

│   └── satellite-analysis/    # Geospatial vision

├── core/                   # Shared vision components

│   ├── utils/              # Common utilities

│   ├── models/             # Base model architectures

│   └── pipelines/          # Processing workflows

└── docs/                   # Project documentation

```



## ✨ Core Features



```mermaid

mindmap

  root((ML Vision Lab))

    Cross-Project

      Modular architecture

      Shared pipelines

      Hardware optimization

      Standardized metrics

    Project Types

      Classification

      Detection

      Medical

      Satellite

    Optimization

      GPU acceleration

      TensorRT

      Memory efficiency

    Development

      MLflow tracking

      DVC versioning

      CI/CD pipelines

```



**Cross-Project Capabilities**



- Modular project architecture

- Shared preprocessing pipelines

- Hardware-optimized inference

- Standardized evaluation metrics

- GPU-accelerated processing

- Production deployment examples

- Memory-efficient inference

- TensorRT integration



**Project Types**



- Image Classification

- Object Detection & Tracking

- Medical Imaging Analysis

- Satellite Imagery Processing

- Industrial Quality Inspection



## 🔧 Prerequisites



- Python 3.11+

- CUDA 12.2+

- OpenCV 5.0+

- PyTorch 2.3+

- TensorFlow 2.15+

- NVIDIA GPU (Compute Capability 6.0+)



## 🛠️ Tech Stack



```mermaid

graph TD

    A[Tech Stack] --> B[Core Libraries]

    A --> C[Project Libraries]

    B --> D[PyTorch]

    B --> E[TensorFlow]

    B --> F[OpenCV]

    B --> G[CUDA]

    C --> H[MONAI]

    C --> I[RasterIO]

    C --> J[DeepSORT]

    C --> K[MLflow]

```



**Core Libraries**



- PyTorch - Deep learning framework

- TensorFlow - Machine learning platform

- OpenCV - Computer vision operations

- CUDA - GPU acceleration

- TensorRT - Inference optimization

- NumPy - Numerical computing

- Pandas - Data manipulation

- Scikit-learn - Machine learning utilities

- Matplotlib - Visualization

- Plotly - Visualization

- Pillow - Image processing



**Project-Specific Libraries**



- MONAI - Medical imaging

- RasterIO - Geospatial analysis

- DeepSORT - Object tracking

- Albumentations - Image augmentation

- MLflow - Experiment tracking

- DVC - Data version control



## 📦 Installation



```bash

# Clone repository

git clone https://github.com/BjornMelin/ml-vision-lab.git

cd ml-vision-lab



# Create virtual environment

python -m venv .venv

source .venv/bin/activate  # Linux/MacOS

# or

.venv\Scripts\activate  # Windows



# Install core requirements

pip install -r requirements.txt



# Install project-specific requirements (optional)

pip install -r projects/food-classification/requirements.txt

```



## 🚀 Quick Start



**Food Classification**



```python

from projects.food_classification import predict



result = predict("pizza.jpg")

print(f"Identified: {result.label} ({result.confidence:.1%})")

```



**Object Detection**



```python

from projects.object_detection import VideoAnalyzer



analyzer = VideoAnalyzer(model="yolov9")

analyzer.process_stream("input.mp4", output="results.mp4")

```



## 📊 Project Matrix



| Project                                             | Task                 | Models              | Input Types   |

| --------------------------------------------------- | -------------------- | ------------------- | ------------- |

| [Food Classification](projects/food-classification) | Image Classification | EfficientNetV2, ViT | JPEG/PNG      |

| [Object Detection](projects/object-detection)       | Real-time Tracking   | YOLOv9, DeepSORT    | Video Streams |

| [Medical Imaging](projects/medical-imaging)         | DICOM Analysis       | UNet3+, MONAI       | CT/MRI Scans  |

| [Satellite Analysis](projects/satellite-analysis)   | Geospatial ML        | ResNet50-ADE20K     | GeoTIFF       |



## 🔧 Development Standards



```mermaid

flowchart TD

    A[Development] --> B[Code Quality]

    A --> C[Testing]

    A --> D[Documentation]

    B --> E[Black]

    B --> F[MyPy]

    C --> G[PyTest]

    C --> H[Coverage]

    D --> I[Docstrings]

    D --> J[Examples]

```



**Code Quality**



```bash

# Format all projects

black projects/



# Type checking

mypy projects/



# Run tests

pytest projects/ --cov

```



**Project Structure Template**



```

projects/new-project/

├── app/          # Application interface

├── engine/       # Core logic

├── models/       # Trained weights

├── tests/        # Unit tests

├── README.md     # Project docs

└── requirements.txt # Local dependencies

```



## 🤝 Contributing



**Adding New Projects**



1. Create project folder in `projects/`

2. Follow structure template

3. Add cross-links to:

   - Core utilities (avoid duplication)

   - Related projects

4. Submit PR with:

   - [ ] Black-formatted code

   - [ ] Google-style docstrings

   - [ ] Unit tests (≥80% coverage)



See [CONTRIBUTING.md](CONTRIBUTING.md) for full guidelines.



## 📚 Documentation



### Pipeline Optimization



```mermaid

graph LR

    A[Input] --> B[Preprocessing]

    B --> C[Inference]

    C --> D[Postprocessing]

    B --> E[GPU Pipeline]

    C --> F[TensorRT]

    D --> G[Batch Processing]

```



- GPU-accelerated preprocessing

- Batch processing optimization

- Memory-efficient inference

- TensorRT integration

- Multi-GPU support

- Mixed precision training



### Models



| Model      | Task         | Performance | Speed (FPS) |

| ---------- | ------------ | ----------- | ----------- |

| YOLOv8     | Detection    | mAP: 52.3   | 120         |

| Mask R-CNN | Segmentation | mAP: 47.8   | 45          |

| DeepSORT   | Tracking     | MOTA: 76.5  | 80          |



## 📊 Benchmarks



Performance on standard datasets:



| Task         | Dataset | Model      | GPU  | FPS | Accuracy   |

| ------------ | ------- | ---------- | ---- | --- | ---------- |

| Detection    | COCO    | YOLOv8     | A100 | 120 | mAP: 52.3  |

| Segmentation | COCO    | Mask R-CNN | V100 | 45  | mAP: 47.8  |

| Tracking     | MOT17   | DeepSORT   | 3090 | 80  | MOTA: 76.5 |



## 📌 Versioning



We use [SemVer](http://semver.org/) for versioning. For available versions, see the [tags on this repository](https://github.com/BjornMelin/ml-vision-lab/tags).



## ✍️ Authors



**Bjorn Melin**



- GitHub: [@BjornMelin](https://github.com/BjornMelin)

- LinkedIn: [Bjorn Melin](https://linkedin.com/in/bjorn-melin)



## 📝 Citation



```bibtex

@misc{melin2024mlvisionlab,

  author = {Melin, Bjorn},

  title = {ML Vision Lab: Production Computer Vision Implementations},

  year = {2024},

  publisher = {GitHub},

  url = {https://github.com/BjornMelin/ml-vision-lab}

}

```



## 📄 License



This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.



## 🙏 Acknowledgments



- OpenCV community

- YOLO authors and contributors

- Deep SORT implementation team

- Medical imaging community (MONAI)

- Satellite imagery processing teams

- TensorFlow and PyTorch teams

- NVIDIA for CUDA and TensorRT support



---



![Architecture Overview](docs/architecture/overview.png)



Made with 👁️ and ❤️ by Bjorn Melin