How to do cross Validation with yolov8? about ultralytics HOT 2 OPEN

@srikar242 hello!

Thank you for your question. Cross-validation is a great way to ensure your model's robustness and generalizability. While YOLOv8 is not directly compatible with scikit-learn's StratifiedKFold, you can still perform cross-validation by manually splitting your dataset and training the model on each fold. Here's a step-by-step guide to help you achieve this:

1. Setup: Ensure you have the necessary libraries installed:

   pip install -U ultralytics scikit-learn pandas pyyaml

2. Prepare Your Dataset: Ensure your annotations are in the YOLO format. For this example, let's assume your dataset is structured with images and labels in separate directories.

3. Generate Feature Vectors: Create a feature vector for each image based on the presence of class labels. This will help in stratifying the dataset.

   import pandas as pd
   from collections import Counter
   from pathlib import Path
   
   dataset_path = Path("./path/to/dataset")
   labels = sorted(dataset_path.rglob("labels/*.txt"))
   
   # Assuming your classes are defined in a YAML file
   with open("path/to/data.yaml", "r") as y:
       classes = yaml.safe_load(y)["names"]
   cls_idx = sorted(classes.keys())
   
   labels_df = pd.DataFrame([], columns=cls_idx, index=[l.stem for l in labels])
   
   for label in labels:
       lbl_counter = Counter()
       with open(label, "r") as lf:
           lines = lf.readlines()
       for l in lines:
           lbl_counter[int(l.split(" ")[0])] += 1
       labels_df.loc[label.stem] = lbl_counter
   
   labels_df = labels_df.fillna(0.0)

4. K-Fold Split: Use KFold from sklearn to split the dataset.

   from sklearn.model_selection import KFold
   
   ksplit = 5
   kf = KFold(n_splits=ksplit, shuffle=True, random_state=20)
   kfolds = list(kf.split(labels_df))

5. Training with Cross-Validation: Train your YOLOv8 model on each fold.

   from ultralytics import YOLO
   
   weights_path = "path/to/weights.pt"
   model = YOLO(weights_path, task="detect")
   
   results = {}
   batch = 16
   project = "kfold_demo"
   epochs = 100
   
   for k, (train_idx, val_idx) in enumerate(kfolds):
       train_files = labels_df.iloc[train_idx].index
       val_files = labels_df.iloc[val_idx].index
   
       # Create dataset YAML for each fold
       dataset_yaml = f"split_{k+1}_dataset.yaml"
       with open(dataset_yaml, "w") as ds_y:
           yaml.safe_dump({
               "path": dataset_path.as_posix(),
               "train": train_files.tolist(),
               "val": val_files.tolist(),
               "names": classes,
           }, ds_y)
   
       model.train(data=dataset_yaml, epochs=epochs, batch=batch, project=project)
       results[k] = model.metrics

This approach ensures that you manually handle the cross-validation process while leveraging the power of YOLOv8 for training and evaluation.

For a more detailed guide, you can refer to our K-Fold Cross Validation documentation.

Feel free to reach out if you have any more questions. Happy coding! 😊

from ultralytics.

@srikar242 hello!

Thank you for your question. Cross-validation is a great way to ensure your model's robustness and generalizability. While YOLOv8 is not directly compatible with scikit-learn's StratifiedKFold, you can still perform cross-validation by manually splitting your dataset and training the model on each fold. Here's a step-by-step guide to help you achieve this:

1. Setup: Ensure you have the necessary libraries installed:

   pip install -U ultralytics scikit-learn pandas pyyaml

2. Prepare Your Dataset: Ensure your annotations are in the YOLO format. For this example, let's assume your dataset is structured with images and labels in separate directories.
3. Generate Feature Vectors: Create a feature vector for each image based on the presence of class labels. This will help in stratifying the dataset.

   import pandas as pd
   from collections import Counter
   from pathlib import Path
   
   dataset_path = Path("./path/to/dataset")
   labels = sorted(dataset_path.rglob("labels/*.txt"))
   
   # Assuming your classes are defined in a YAML file
   with open("path/to/data.yaml", "r") as y:
       classes = yaml.safe_load(y)["names"]
   cls_idx = sorted(classes.keys())
   
   labels_df = pd.DataFrame([], columns=cls_idx, index=[l.stem for l in labels])
   
   for label in labels:
       lbl_counter = Counter()
       with open(label, "r") as lf:
           lines = lf.readlines()
       for l in lines:
           lbl_counter[int(l.split(" ")[0])] += 1
       labels_df.loc[label.stem] = lbl_counter
   
   labels_df = labels_df.fillna(0.0)

4. K-Fold Split: Use KFold from sklearn to split the dataset.

   from sklearn.model_selection import KFold
   
   ksplit = 5
   kf = KFold(n_splits=ksplit, shuffle=True, random_state=20)
   kfolds = list(kf.split(labels_df))

5. Training with Cross-Validation: Train your YOLOv8 model on each fold.

   from ultralytics import YOLO
   
   weights_path = "path/to/weights.pt"
   model = YOLO(weights_path, task="detect")
   
   results = {}
   batch = 16
   project = "kfold_demo"
   epochs = 100
   
   for k, (train_idx, val_idx) in enumerate(kfolds):
       train_files = labels_df.iloc[train_idx].index
       val_files = labels_df.iloc[val_idx].index
   
       # Create dataset YAML for each fold
       dataset_yaml = f"split_{k+1}_dataset.yaml"
       with open(dataset_yaml, "w") as ds_y:
           yaml.safe_dump({
               "path": dataset_path.as_posix(),
               "train": train_files.tolist(),
               "val": val_files.tolist(),
               "names": classes,
           }, ds_y)
   
       model.train(data=dataset_yaml, epochs=epochs, batch=batch, project=project)
       results[k] = model.metrics

This approach ensures that you manually handle the cross-validation process while leveraging the power of YOLOv8 for training and evaluation.

For a more detailed guide, you can refer to our K-Fold Cross Validation documentation.

Feel free to reach out if you have any more questions. Happy coding! 😊

Hello, I found that this approach leads to each round of training inheriting the training results from the previous dataset, i.e. the first epoch will get the highest validation accuracy, I'm not sure if this is correct or not

from ultralytics.