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YOLO Detection & Utilities

Lightweight repository of scripts and utilities for YOLO-based object detection, SAHI slicing, dataset creation, visualization, and Label Studio integration.

What's changed

  • requirements.txt now includes torch and torchvision. For CUDA-enabled installs, please use the official PyTorch selector to install the correct wheel for your GPU.
  • A configuration template is available at scripts/Sahi_detect/config_template.yaml (copy and edit to create your runtime config.yaml).

Installation

  1. Create and activate a Python virtual environment:
python3 -m venv yolo_env
# Linux / macOS
source yolo_env/bin/activate
# Windows (PowerShell)
.\yolo_env\Scripts\Activate.ps1
  1. Install PyTorch (choose proper wheel for your CUDA version). Example for CUDA via PyTorch's index (adjust to your CUDA):
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu130
  1. Install remaining dependencies:
pip install -r requirements.txt

Note: requirements.txt includes torch/torchvision as convenience, but installing PyTorch through the official selector ensures compatibility with your CUDA runtime.

Quick Start

  • Run a simple detection (example):
python yolo_detect_v11.py --input path/to/image.jpg --conf 0.5
  • Run SAHI detection (example):
python scripts/Sahi_detect/sahi_detection.py --config scripts/Sahi_detect/config_template.yaml

Configuration

Use scripts/Sahi_detect/config_template.yaml as a starting point. Copy it to scripts/Sahi_detect/config.yaml and update the input_dir, output_dir, and model.path values to point to your dataset and model weights.

Files of Interest

  • yolo_detect_v11.py — YOLO v11 inference script
  • scripts/train/yolo11_train.py — Training script
  • scripts/Sahi_detect/ — SAHI detection scripts and configs
  • utils/ — utilities for staging images, visualization, dataset creation, Label Studio helpers
  • requirements.txt — project dependencies

Troubleshooting

  • Verify GPU drivers and CUDA with nvidia-smi and test PyTorch with:
python -c "import torch; print('GPU Found!' if torch.cuda.is_available() else 'CPU Only')"
  • If Pillow raises errors, see scripts/Sahi_detect/INSTALL_PILLOW.md.

Contributing

Contributions welcome — open an issue or submit a PR.


Last Updated: February 2026

About

YOLO PII deID is a streamlined solution for de-identifying visual data using the YOLO object detection algorithm. It ensures privacy by automatically detecting and blurring PII information in images, suitable for high resolution 12K images.

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