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YO-FLO: YOLO-Like Object Detection with Florence Models

Welcome to YO-FLO, a proof-of-concept implementation of YOLO-like object detection using the Florence-2-base-ft model. Inspired by the powerful YOLO (You Only Look Once) object detection framework, YO-FLO leverages the capabilities of the Florence foundational vision model to achieve real-time inference while maintaining a lightweight footprint.

Table of Contents

  • Introduction
  • Features
  • Installation
  • Usage
  • Error Handling
  • Contributing
  • License

Introduction

YO-FLO explores whether the new Florence foundational vision model can be implemented in a YOLO-like format for object detection. Florence-2 is designed by Microsoft as a unified vision-language model capable of handling diverse tasks such as object detection, captioning, and segmentation. To achieve this, it uses a sequence-to-sequence framework where images and task-specific prompts are processed to generate the desired text outputs. The model's architecture combines a DaViT vision encoder with a transformer-based multi-modal encoder-decoder, making it versatile and efficient.

Florence-2 has been trained on the extensive FLD-5B dataset, containing 126 million images and over 5 billion annotations, ensuring high-quality performance across multiple tasks. Despite its relatively small size, Florence-2 demonstrates strong zero-shot and fine-tuning capabilities, making it an excellent choice for real-time applications.

Features

  • Real-Time Object Detection: Achieve YOLO-like performance using the Florence-2-base-ft model.
  • Class-Specific Detection: Specify the class of objects you want to detect (e.g., 'cat', 'dog').
  • Expression Comprehension: Detect objects or states via questions for mundane, cool, and exotic results!
  • Beep and Screenshot on Detection: Toggle options to beep and take screenshots when the target class or phrase is detected.
  • Tkinter GUI: A user-friendly graphical interface for easy interaction.
  • Cross-Platform Compatibility: Works on Windows, macOS, and Linux.
  • Toggle Headless Mode: Enable or disable headless mode for running without GUI.
  • Update Inference Rate: Display the rate of inferences per second during real-time detection.
  • Screenshot on Yes/No Inference: Automatically save screenshots based on yes/no answers from expression comprehension.
  • Visual Grounding: Identify and highlight specific regions in an image based on descriptive phrases.
  • Evaluate Inference Tree: Use a tree of inference phrases to evaluate multiple conditions in a single run.
  • Plot Bounding Boxes: Visualize detection results by plotting bounding boxes on the image.
  • Save Screenshots: Save screenshots of detected objects or regions of interest.
  • Robust Error Handling: Comprehensive error management for smooth operation.
  • Webcam Detection Control: Start and stop webcam-based detection with ease.
  • Debug Mode: Toggle detailed logging for development and troubleshooting purposes.

Installation

Prerequisites

  • Python 3.7 or higher
  • pip

Installing Dependencies

pip install torch transformers pillow opencv-python colorama simpleaudio huggingface-hub

Usage

Running YO-FLO

To start YO-FLO, run the following command:

python yo-flo.py

Menu Options

  1. Select Model Path: Choose a local directory containing the Florence model.
  2. Download Model from HuggingFace: Download and initialize the Florence-2-base-ft model from HuggingFace.
  3. Set Class Name: Specify the class name you want to detect (leave blank to show all detections).
  4. Set Phrase: Enter the phrase for comprehension detection (e.g., 'Is the person smiling?', 'Is the cat laying down?').
  5. Set Visual Grounding Phrase: Enter the phrase for visual grounding.
  6. Set Inference Tree: Enter multiple inference phrases to evaluate several conditions.
  7. Toggle Beep on Detection: Enable or disable the beep sound on detection.
  8. Toggle Screenshot on Detection: Enable or disable taking screenshots on detection.
  9. Toggle Screenshot on Yes/No Inference: Enable or disable taking screenshots based on yes/no inference results.
  10. Start Webcam Detection: Begin real-time object detection using your webcam.
  11. Stop Webcam Detection: Stop the webcam detection and return to the menu.
  12. Toggle Debug Mode: Enable or disable debug mode for detailed logging.
  13. Toggle Headless Mode: Enable or disable headless mode for running without GUI.
  14. Exit: Exit the application.

Example Workflow

  1. Select Model Path or Download Model from HuggingFace.
  2. Set Class Name to specify what you want to detect (e.g., 'cat', 'dog').
  3. Set Phrase for specific phrase-based inference.
  4. Set Visual Grounding Phrase to bound specific regions to detect.
  5. Set Inference Tree for evaluating multiple conditions.
  6. Toggle Beep on Detection if you want an audible alert.
  7. Toggle Screenshot on Detection if you want to save screenshots of detections.
  8. Toggle Screenshot on Yes/No Inference to save screenshots based on comprehension results.
  9. Start Webcam Detection to begin detecting objects in real-time.

Error Handling

YO-FLO includes robust error handling to ensure smooth operation:

  • Model Initialization Errors: Handles cases where the model path is incorrect or the model fails to load.
  • Webcam Access Errors: Notifies if the webcam cannot be accessed.
  • Image Processing Errors: Catches errors during frame processing and provides detailed messages.
  • File Not Found Errors: Alerts if required files (e.g., beep sound file) are missing.
  • General Exception Handling: Catches and logs any unexpected errors to prevent crashes.

Example Error Messages

  • Error loading model: Model path not found or model failed to load.
  • Error running object detection: Issues during object detection process.
  • Error plotting bounding boxes: Problems with visualizing detection results.
  • Error toggling beep: Issues enabling or disabling the beep sound.
  • Error saving screenshot: Problems saving detection screenshots.
  • OpenCV error: Errors related to OpenCV operations.

Contributing

We welcome contributions to improve YO-FLO. Please follow these steps:

  1. Fork the repository.
  2. Create a new branch (git checkout -b feature-branch).
  3. Commit your changes (git commit -am 'Add new feature').
  4. Push to the branch (git push origin feature-branch).
  5. Create a new Pull Request.

License

YO-FLO is licensed under the MIT License.

Thank you for using YO-FLO! We are excited to see what amazing applications you will build with this tool. Happy detecting!