mmllm-face-refinement 0.1.11

Face detection and LLM-based face refinement using YOLO and multimodal LLMs (Gemini, LLaVA).

MMLLM Face Refinement

This project implements a novel approach to face detection by combining traditional face detection methods with multimodal large language models (MMLLM) for refinement and false positive elimination.

Approach

1. Initial Detection: Use YOLOv11-Face to detect potential faces in images
2. Refinement: Each detected face is then analyzed using multimodal LLMs:
   • Gemini API for cloud-based analysis
   • LLaVA-NeXT (local model) for on-device analysis
3. False Positive Elimination: The LLMs determine if the detection is actually a face
4. Bounding Box Refinement: The LLMs can suggest refinements to the bounding boxes

Requirements

• Python 3.8+
• See requirements.txt for all dependencies
• YOLOv11-Face model file (yolov11l-face.pt) in the models directory

Installation

Automatic Installation

Linux/macOS

# Clone the repository
git clone https://github.com/JonathanLehner/mmllm-face-refinement.git
cd mmllm-face-refinement

# Run the installation script
chmod +x install.sh
./install.sh

Windows

# Clone the repository
git clone https://github.com/yourusername/mmllm-face-refinement.git
cd mmllm-face-refinement

# Run the installation script
install.bat

Manual Installation

1. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate.bat
   

2. Install the package:

   pip install -e .
   

3. Download the YOLOv11-Face model:

   • Get the model from akanametov/yolo-face
   • Place it in the models directory as yolov11l-face.pt

4. Set up API keys:

   cp .env.example .env
   # Edit .env file with your API keys
   

Usage

1. Place input images in the input folder
2. Run the main script: python main.py
3. Results will be saved in the output folder

For debugging purposes, you can run the test script with debug enabled:

python test.py --image input/your_image.jpg --debug

Configuration

You can adjust parameters in config.yaml:

• YOLO confidence threshold
• Bounding box padding
• LLM endpoint selection
• Output formatting options
• Debug settings

Debug Mode

The system includes a debug mode that saves intermediate results:

debug:
  enabled: true  # Enable debug mode
  save_raw_detections: true  # Save initial YOLO detections
  save_intermediate_steps: true  # Save cropped faces before LLM analysis

When debug mode is enabled, the following files are saved to the output/debug directory:

• Images with YOLO detections visualized
• JSON files with detection coordinates
• Cropped face images before LLM processing
• JSON files with detection metadata

References

This project utilizes the following models and repositories:

• YOLOv11-Face: State-of-the-art face detection model from akanametov/yolo-face
• LLaVA-NeXT: Local multimodal LLM from LLaVA-VL/LLaVA-NeXT
• Gemini API: Google's multimodal generative AI model

Upload to pipy

• python -m build
• pip install dist/mmllm_face_refinement-0.1.0-py3-none-any.whl
• python -m twine upload dist/*

Note

This is a research project demonstrating the use of multimodal LLMs for improving traditional computer vision tasks.