YOLO-TinyPerson

A comprehensive framework for training and evaluating YOLO models on the TinyPerson dataset for small object detection. This repository provides tools to benchmark different YOLO model versions (v8, v9, v10, v11, v12) on detecting small persons in images.

Features

• Complete Pipeline: Setup environment, train models, evaluate performance, and visualize results
• Multiple YOLO Models: Support for YOLOv8x, YOLOv9e, YOLOv10x, YOLO11x, and YOLO12x
• Automated Setup: Downloads required datasets, models, and dependencies
• Comprehensive Evaluation: Measures precision, recall, F1 score, mAP, and inference speed
• Rich Visualizations: Generates performance comparison plots across models

Prerequisites

• Python 3.6+
• CUDA-compatible GPU recommended (training on CPU is supported but not recommended)
• Windows or Linux operating system

Installation

1. Clone the repository:

   git clone https://github.com/xixu-me/YOLO-TinyPerson.git
   cd YOLO-TinyPerson

2. Install dependencies:

   pip install -r requirements.txt

3. Set dataset_dir to the dataset path in setup.py:

   dataset_dir = "path/to/dataset"

Usage

The framework provides a simple command-line interface for running different parts of the pipeline:

Complete Pipeline

Run the entire process (setup, train, evaluate, visualize):

python main.py all

or just:

python main.py

This will execute the default command, which is all.

Individual Steps

Setup environment and download required files:

python main.py setup

Train YOLO models on the TinyPerson dataset:

python main.py train

Evaluate trained models:

python main.py evaluate

Generate visualizations of training and evaluation results:

python main.py visualize

Project Structure

YOLO-TinyPerson/
├── main.py           # Main entry point with command-line interface
├── setup.py          # Environment setup, downloads datasets and models
├── train.py          # Model training functionality
├── evaluate.py       # Model evaluation and metrics calculation
├── visualize.py      # Results visualization and plot generation
├── requirements.txt  # Required Python packages
├── dataset/          # TinyPerson dataset (downloaded by setup.py)
├── weights/          # Pre-trained model weights (downloaded by setup.py)
├── results/          # Training results for each model
├── evaluation/       # Evaluation metrics and results
└── visualizations/   # Generated plots and visualizations

Performance Metrics

The framework evaluates models using the following metrics:

• Precision: Accuracy of positive predictions
• Recall: Ability to find all relevant instances
• F1-score: Harmonic mean of precision and recall
• mAP@0.5: Mean Average Precision with IoU threshold of 0.5
• mAP@0.5-0.95: Mean Average Precision across multiple IoU thresholds
• Inference Speed: Frames processed per second

Visualizations are generated in the visualizations/ directory, including:

• Precision/Recall/F1 comparison across models
• mAP comparison
• Training loss curves
• Inference speed comparison
• Radar charts for overall performance comparison

Model Support

The following YOLO models are supported:

Model	Description
YOLOv8x	YOLOv8 extra large model
YOLOv9e	YOLOv9 extended model
YOLOv10x	YOLOv10 extra large model
YOLO11x	YOLO11 extra large model
YOLO12x	YOLO12 extra large model

Acknowledgments

• The TinyPerson dataset is used for training and evaluation
• YOLO models from the Ultralytics framework

License

Copyright © Xi Xu. All rights reserved.

Licensed under the GPL-3.0 license.