Running Scripts Manually

Running YOLO Scripts Manually

This guide explains how to extract, configure, and manually execute the Python runner scripts (predict.py and train.py) provided by DiGi.YOLO.

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🛠️ Prerequisites & Installation

Before running the scripts, ensure Python (3.10+) is installed and install the required dependencies:

pip install -r requirements.txt

(Dependencies: ultralytics, torch)

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📂 Exporting Scripts via C#

Call Modify.WriteScripts(targetDirectory) to export all runner scripts (train.py, predict.py, utils.py, requirements.txt) and configuration (conf.yaml) to your working directory:

using DiGi.YOLO;

string targetDirectory = @"C:\Path\To\WorkingDir";
bool success = Modify.WriteScripts(targetDirectory);

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🔍 Running predict.py (Inference)

predict.py uses argparse to allow explicit control over input image sources, model weights, confidence thresholds, and output bounding box result files (.bbrf).

Command-Line Arguments

Flag	Default Value	Description
--model	YOLO/models/model.pt	Path to trained YOLO model weights (.pt). Defaults to best checkpoint in runs/detect/train*/weights/best.pt if default does not exist.
--source	YOLO/input	Path to single input image file or directory containing .jpg, .jpeg, or .png images.
--conf	0.1	Confidence threshold float value for detection filtering.
--output	YOLO/output/results.bbrf	Destination path for output bounding box result file (.bbrf). Written in "w" mode to prevent duplicate rows.

Usage Examples

Default Run

python predict.py

Custom Arguments

python predict.py --model YOLO/models/model.pt --source "C:/Data/Images" --conf 0.25 --output "C:/Data/Output/results.bbrf"

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🏋️ Running train.py (Training)

train.py trains or fine-tunes a YOLO model using dataset configuration defined in conf.yaml.

Directory Structure Requirements

Keep Python scripts and configuration files in the root execution directory, with dataset files structured under a common training/ folder:

.
├── conf.yaml
├── predict.py
├── requirements.txt
├── train.py
├── utils.py
└── training/
    ├── images/
    │   ├── train/
    │   └── val/
    └── labels/
        ├── train/
        └── val/

Dataset Configuration (conf.yaml)

path: training
train: images/train
val: images/train
names:
  0: building

Execution

Run training with default hyperparameters (150 epochs, imgsz=640):

python train.py

The training process outputs model checkpoints to runs/detect/train*/weights/best.pt.