ultralytics/docs/en/guides/kfold-cross-validation.md

---
comments: true
description: An in-depth guide demonstrating the implementation of K-Fold Cross Validation with the Ultralytics ecosystem for object detection datasets, leveraging Python, YOLO, and sklearn.
keywords: K-Fold cross validation, Ultralytics, YOLO detection format, Python, sklearn, object detection
---

# K-Fold Cross Validation with Ultralytics

## Introduction

This comprehensive guide illustrates the implementation of K-Fold Cross Validation for object detection datasets within the Ultralytics ecosystem. We'll leverage the YOLO detection format and key Python libraries such as sklearn, pandas, and PyYaml to guide you through the necessary setup, the process of generating feature vectors, and the execution of a K-Fold dataset split.

<p align="center">
  <img width="800" src="https://user-images.githubusercontent.com/26833433/258589390-8d815058-ece8-48b9-a94e-0e1ab53ea0f6.png" alt="K-Fold Cross Validation Overview">
</p>

Whether your project involves the Fruit Detection dataset or a custom data source, this tutorial aims to help you comprehend and apply K-Fold Cross Validation to bolster the reliability and robustness of your machine learning models. While we're applying `k=5` folds for this tutorial, keep in mind that the optimal number of folds can vary depending on your dataset and the specifics of your project.

Without further ado, let's dive in!

## Setup

- Your annotations should be in the [YOLO detection format](https://docs.ultralytics.com/datasets/detect/).

- This guide assumes that annotation files are locally available.

    - For our demonstration, we use the [Fruit Detection](https://www.kaggle.com/datasets/lakshaytyagi01/fruit-detection/code) dataset.

        - This dataset contains a total of 8479 images.
        - It includes 6 class labels, each with its total instance counts listed below.

          | Class Label | Instance Count |
          |:------------|:--------------:|
          | Apple       |      7049      |
          | Grapes      |      7202      |
          | Pineapple   |      1613      |
          | Orange      |     15549      |
          | Banana      |      3536      |
          | Watermelon  |      1976      |

- Necessary Python packages include:

    - `ultralytics`
    - `sklearn`
    - `pandas`
    - `pyyaml`

- This tutorial operates with `k=5` folds. However, you should determine the best number of folds for your specific dataset.

1. Initiate a new Python virtual environment (`venv`) for your project and activate it. Use `pip` (or your preferred package manager) to install:

    - The Ultralytics library: `pip install -U ultralytics`. Alternatively, you can clone the official [repo](https://github.com/ultralytics/ultralytics).
    - Scikit-learn, pandas, and PyYAML: `pip install -U scikit-learn pandas pyyaml`.

2. Verify that your annotations are in the [YOLO detection format](https://docs.ultralytics.com/datasets/detect/).

    - For this tutorial, all annotation files are found in the `Fruit-Detection/labels` directory.

## Generating Feature Vectors for Object Detection Dataset

1. Start by creating a new Python file and import the required libraries.

    ```python
    import datetime
    import shutil
    from pathlib import Path
    from collections import Counter

    import yaml
    import numpy as np
    import pandas as pd
    from ultralytics import YOLO
    from sklearn.model_selection import KFold
    ```

2. Proceed to retrieve all label files for your dataset.

    ```python
    dataset_path = Path('./Fruit-detection') # replace with 'path/to/dataset' for your custom data
    labels = sorted(dataset_path.rglob("*labels/*.txt")) # all data in 'labels'
    ```

3. Now, read the contents of the dataset YAML file and extract the indices of the class labels.

    ```python
    yaml_file = 'path/to/data.yaml'  # your data YAML with data directories and names dictionary
    with open(yaml_file, 'r', encoding="utf8") as y:
        classes = yaml.safe_load(y)['names']
    cls_idx = sorted(classes.keys())
    ```

4. Initialize an empty `pandas` DataFrame.

    ```python
    indx = [l.stem for l in labels] # uses base filename as ID (no extension)
    labels_df = pd.DataFrame([], columns=cls_idx, index=indx)
    ```

5. Count the instances of each class-label present in the annotation files.

    ```python
    for label in labels:
        lbl_counter = Counter()

        with open(label,'r') as lf:
            lines = lf.readlines()

        for l in lines:
            # classes for YOLO label uses integer at first position of each line
            lbl_counter[int(l.split(' ')[0])] += 1

        labels_df.loc[label.stem] = lbl_counter

    labels_df = labels_df.fillna(0.0) # replace `nan` values with `0.0`
    ```

6. The following is a sample view of the populated DataFrame:

    ```pandas
                                                           0    1    2    3    4    5
    '0000a16e4b057580_jpg.rf.00ab48988370f64f5ca8ea4...'  0.0  0.0  0.0  0.0  0.0  7.0
    '0000a16e4b057580_jpg.rf.7e6dce029fb67f01eb19aa7...'  0.0  0.0  0.0  0.0  0.0  7.0
    '0000a16e4b057580_jpg.rf.bc4d31cdcbe229dd022957a...'  0.0  0.0  0.0  0.0  0.0  7.0
    '00020ebf74c4881c_jpg.rf.508192a0a97aa6c4a3b6882...'  0.0  0.0  0.0  1.0  0.0  0.0
    '00020ebf74c4881c_jpg.rf.5af192a2254c8ecc4188a25...'  0.0  0.0  0.0  1.0  0.0  0.0
     ...                                                  ...  ...  ...  ...  ...  ...
    'ff4cd45896de38be_jpg.rf.c4b5e967ca10c7ced3b9e97...'  0.0  0.0  0.0  0.0  0.0  2.0
    'ff4cd45896de38be_jpg.rf.ea4c1d37d2884b3e3cbce08...'  0.0  0.0  0.0  0.0  0.0  2.0
    'ff5fd9c3c624b7dc_jpg.rf.bb519feaa36fc4bf630a033...'  1.0  0.0  0.0  0.0  0.0  0.0
    'ff5fd9c3c624b7dc_jpg.rf.f0751c9c3aa4519ea3c9d6a...'  1.0  0.0  0.0  0.0  0.0  0.0
    'fffe28b31f2a70d4_jpg.rf.7ea16bd637ba0711c53b540...'  0.0  6.0  0.0  0.0  0.0  0.0
    ```

The rows index the label files, each corresponding to an image in your dataset, and the columns correspond to your class-label indices. Each row represents a pseudo feature-vector, with the count of each class-label present in your dataset. This data structure enables the application of K-Fold Cross Validation to an object detection dataset.

## K-Fold Dataset Split

1. Now we will use the `KFold` class from `sklearn.model_selection` to generate `k` splits of the dataset.

    - Important:
        - Setting `shuffle=True` ensures a randomized distribution of classes in your splits.
        - By setting `random_state=M` where `M` is a chosen integer, you can obtain repeatable results.

    ```python
    ksplit = 5
    kf = KFold(n_splits=ksplit, shuffle=True, random_state=20)   # setting random_state for repeatable results

    kfolds = list(kf.split(labels_df))
    ```

2. The dataset has now been split into `k` folds, each having a list of `train` and `val` indices. We will construct a DataFrame to display these results more clearly.

    ```python
    folds = [f'split_{n}' for n in range(1, ksplit + 1)]
    folds_df = pd.DataFrame(index=indx, columns=folds)

    for idx, (train, val) in enumerate(kfolds, start=1):
        folds_df[f'split_{idx}'].loc[labels_df.iloc[train].index] = 'train'
        folds_df[f'split_{idx}'].loc[labels_df.iloc[val].index] = 'val'
    ```

3. Now we will calculate the distribution of class labels for each fold as a ratio of the classes present in `val` to those present in `train`.

    ```python
    fold_lbl_distrb = pd.DataFrame(index=folds, columns=cls_idx)

    for n, (train_indices, val_indices) in enumerate(kfolds, start=1):
        train_totals = labels_df.iloc[train_indices].sum()
        val_totals = labels_df.iloc[val_indices].sum()

        # To avoid division by zero, we add a small value (1E-7) to the denominator
        ratio = val_totals / (train_totals + 1E-7)
        fold_lbl_distrb.loc[f'split_{n}'] = ratio
    ```

The ideal scenario is for all class ratios to be reasonably similar for each split and across classes. This, however, will be subject to the specifics of your dataset.

4. Next, we create the directories and dataset YAML files for each split.

    ```python
    supported_extensions = ['.jpg', '.jpeg', '.png']

    # Initialize an empty list to store image file paths
    images = []

    # Loop through supported extensions and gather image files
    for ext in supported_extensions:
        images.extend(sorted((dataset_path / 'images').rglob(f"*{ext}")))

    # Create the necessary directories and dataset YAML files (unchanged)
    save_path = Path(dataset_path / f'{datetime.date.today().isoformat()}_{ksplit}-Fold_Cross-val')
    save_path.mkdir(parents=True, exist_ok=True)
    ds_yamls = []

    for split in folds_df.columns:
        # Create directories
        split_dir = save_path / split
        split_dir.mkdir(parents=True, exist_ok=True)
        (split_dir / 'train' / 'images').mkdir(parents=True, exist_ok=True)
        (split_dir / 'train' / 'labels').mkdir(parents=True, exist_ok=True)
        (split_dir / 'val' / 'images').mkdir(parents=True, exist_ok=True)
        (split_dir / 'val' / 'labels').mkdir(parents=True, exist_ok=True)

        # Create dataset YAML files
        dataset_yaml = split_dir / f'{split}_dataset.yaml'
        ds_yamls.append(dataset_yaml)

        with open(dataset_yaml, 'w') as ds_y:
            yaml.safe_dump({
                'path': split_dir.as_posix(),
                'train': 'train',
                'val': 'val',
                'names': classes
            }, ds_y)
    ```

5. Lastly, copy images and labels into the respective directory ('train' or 'val') for each split.

    - __NOTE:__ The time required for this portion of the code will vary based on the size of your dataset and your system hardware.

    ```python
    for image, label in zip(images, labels):
        for split, k_split in folds_df.loc[image.stem].items():
            # Destination directory
            img_to_path = save_path / split / k_split / 'images'
            lbl_to_path = save_path / split / k_split / 'labels'

            # Copy image and label files to new directory (SamefileError if file already exists)
            shutil.copy(image, img_to_path / image.name)
            shutil.copy(label, lbl_to_path / label.name)
    ```

## Save Records (Optional)

Optionally, you can save the records of the K-Fold split and label distribution DataFrames as CSV files for future reference.

```python
folds_df.to_csv(save_path / "kfold_datasplit.csv")
fold_lbl_distrb.to_csv(save_path / "kfold_label_distribution.csv")
```

## Train YOLO using K-Fold Data Splits

1. First, load the YOLO model.

    ```python
    weights_path = 'path/to/weights.pt'
    model = YOLO(weights_path, task='detect')
    ```

2. Next, iterate over the dataset YAML files to run training. The results will be saved to a directory specified by the `project` and `name` arguments. By default, this directory is 'exp/runs#' where # is an integer index.

    ```python
    results = {}

    # Define your additional arguments here
    batch = 16
    project = 'kfold_demo'
    epochs = 100

    for k in range(ksplit):
        dataset_yaml = ds_yamls[k]
        model.train(data=dataset_yaml,epochs=epochs, batch=batch, project=project)  # include any train arguments
        results[k] = model.metrics  # save output metrics for further analysis
    ```

## Conclusion

In this guide, we have explored the process of using K-Fold cross-validation for training the YOLO object detection model. We learned how to split our dataset into K partitions, ensuring a balanced class distribution across the different folds.

We also explored the procedure for creating report DataFrames to visualize the data splits and label distributions across these splits, providing us a clear insight into the structure of our training and validation sets.

Optionally, we saved our records for future reference, which could be particularly useful in large-scale projects or when troubleshooting model performance.

Finally, we implemented the actual model training using each split in a loop, saving our training results for further analysis and comparison.

This technique of K-Fold cross-validation is a robust way of making the most out of your available data, and it helps to ensure that your model performance is reliable and consistent across different data subsets. This results in a more generalizable and reliable model that is less likely to overfit to specific data patterns.

Remember that although we used YOLO in this guide, these steps are mostly transferable to other machine learning models. Understanding these steps allows you to apply cross-validation effectively in your own machine learning projects. Happy coding!