Using LightlyTrain with YOLO

This tutorial demonstrates how to pre-train a YOLO model using lightly-train and then fine-tune it for object detection using the ultralytics framework.

Warning

Using Ultralytics models might require a commercial Ultralytics license. See the Ultralytics website for more information.

Install Dependencies

Install the required packages:

pip install lightly-train[ultralytics]

Download the Dataset

For the purpose of this tutorial, we use the Fruits Detection dataset.

You can clone it from GitHub:

git clone https://github.com/lightly-ai/dataset_fruits_detection.git ./Fruits-detection

All the detections in Fruits-detection/train/labels are in YOLO detection format. The content of Fruits-detection/data.yaml is the following:

names:
  - Apple
  - Banana
  - Grape
  - Orange
  - Pineapple
  - Watermelon
nc: 6
test: test/images
train: train/images
val: valid/images

Note

Labels are not required for self-supervised pre-training. We will use the labels only for finetuning.

Pre-train and Fine-tune

We will use lightly-train to pre-train a YOLO model using self-supervised learning.

The following scripts will:

  • Initialize a YOLOv8s model with random weights.

  • Pre-train the YOLOv8s model on the Fruits Detection train set using DINO self-supervised learning.

  • Export the pre-trained YOLOv8s model.

  • Fine-tune the pre-trained model on the Fruits Detection dataset using labels.

# pretrain_yolo.py
from ultralytics import YOLO

import lightly_train

if __name__ == "__main__":
    model = YOLO("yolov8s.yaml")
    # model = YOLO("yolov8s.pt") # Uncomment this to start from a COCO checkpoint.

    # Pre-train with lightly-train.
    lightly_train.train(
        out="out/my_experiment",                # Output directory.
        data="Fruits-detection/train/images",   # Directory with images.
        model=model,                            # Pass the YOLO model.
        method="dino",                          # Self-supervised learning method.
        epochs=100,                             # Adjust epochs for faster training.
        batch_size=64,                          # Adjust batch size based on hardware.
    )


# finetune_yolo.py
from pathlib import Path

from ultralytics import YOLO

if __name__ == "__main__":
    # Load the exported model.
    model = YOLO("out/my_experiment/exported_models/exported_last.pt")

    # Fine-tune with ultralytics.
    data = Path("Fruits-detection/data.yaml").absolute()
    model.train(data=data, epochs=100)

Note

The exported model can also be used from the command line.

Use: yolo detect train model=out/my_experiment/exported_models/exported_last.pt" data="Fruits-detection/data.yaml" epochs=100.

Congratulations! You have successfully pre-trained a model using lightly-train and fine-tuned it for object detection using ultralytics.

For more advanced options, explore the Python API and Ultralytics documentation.

Next Steps

  • Experiment with different self-supervised learning methods in lightly-train.

  • Try various YOLO models (YOLOv5, YOLOv6, YOLOv11).

  • Use the pre-trained model for other tasks, like image embeddings.