YOLO Training Pipeline

YOLO Training Pipeline

This guide describes how to prepare a dataset and train a custom YOLO model using annotations created with Label Studio.

Prerequisites

Before starting, ensure that:

• Label Studio has been installed and configured
• A dataset has been annotated and exported
• Python 3.10 or later is available
• Ultralytics YOLO is installed

Extract the Dataset

After annotating the documents in Label Studio (see Label Studio Guide), extract the downloaded archive to a local directory.

The extracted archive contains:

• labels/ (YOLO annotation files)
• classes.txt (class definitions)
• notes.json (Label Studio metadata)
• images/ (may be empty depending on export settings)

YOLO archive content

Prepare the folder to be correctly used for the training

Dataset Structure

The training pipeline expects the following directory structure:

dataset/
├── images/
│   ├── train/
│   └── val/
├── labels/
│   ├── train/
│   └── val/
├── data.yaml
└── synset.txt

Where:

• images/train contains the training images
• images/val contains the validation images
• labels/train contains the training annotations
• labels/val contains the validation annotations
• data.yaml contains the dataset configuration

IMPORTANT

Move the .txt file into labels/train directory.

IMPORTANT

The conversion process generates only the annotation files and does not move the images.

Copy the corresponding images into the images/train directory.

Do not worry about populating the images/val or labels/val directories.

Those will be automatically populated moving the 30% of files from images/train and labels/train

Verify that every annotation file has a matching image. Each image must have a corresponding annotation file with the same filename.

Example:

images/train/invoice001.jpg
labels/train/invoice001.txt

Rename classes.txt to synset.txt, as the training pipeline expects class definitions to be provided through a file named synset.txt.

Create the data.yaml File

YOLO uses a configuration file named data.yaml to locate the dataset and identify the available classes.

The file specifies:

• Training image directory
• Validation image directory
• Number of classes
• Class names

Example:

path: dataset
train: images/train
val: images/val

names:
  0: invoice_number
  1: date
  2: seller_name
  3: total

The class definitions must match those used during annotation.

Start the Training

Create a Python training script using the Ultralytics YOLO framework.

A pretrained YOLO model such as:

• yolo11n.pt
• yolo11s.pt
• yolo11m.pt
• yolo11l.pt

is required as the starting point for training.

The model does not need to be downloaded manually. Ultralytics automatically downloads it when the training script is executed.

Execute the training script.

During training, Ultralytics YOLO:

• Loads the dataset
• Trains the model
• Evaluates the model on the validation set
• Saves the best-performing weights

The number of iterations depends on:

• Dataset size
• Number of epochs
• Batch size

The batch size can be specified through the training parameters. Using -1 allows Ultralytics to select the default value automatically.

Training Output

At the end of the training process, Ultralytics creates a runs directory containing the training artifacts.

Typical outputs include:

• Training logs
• Validation metrics
• Loss curves
• Confusion matrix
• Model weights

The most important file is:

best.pt

This file contains the trained model and should be used for subsequent validation, conversion, or deployment.