Hi @7rkMnpl, - Githubissues

          Hi @7rkMnpl,

To integrate a custom callback with early stopping in YOLOv5, you would need to modify the training script to include your custom callback logic. Here's a general outline of how you can achieve this:

Create Your Custom Callback: Define your custom callback class. For example, you might want to create a callback that monitors a specific metric and stops training based on that metric.

class CustomEarlyStopping:
   def __init__(self, patience=10, min_delta=0):
       self.patience = patience
       self.min_delta = min_delta
       self.best_score = None
       self.counter = 0

   def __call__(self, current_score):
       if self.best_score is None:
           self.best_score = current_score
       elif current_score < self.best_score + self.min_delta:
           self.counter += 1
           if self.counter >= self.patience:
               return True
       else:
           self.best_score = current_score
           self.counter = 0
       return False

Integrate the Callback into the Training Loop: Modify the training loop in train.py to include your custom callback. You will need to check the callback condition at the end of each epoch.

from train import train

# Initialize your custom callback
custom_early_stopping = CustomEarlyStopping(patience=10, min_delta=0.01)

# Modify the training loop to include the callback check
for epoch in range(epochs):
   # Training code...

   # Calculate your custom metric (e.g., recall)
   current_score = calculate_recall()

   # Check the custom early stopping condition
   if custom_early_stopping(current_score):
       print(f"Early stopping at epoch {epoch}")
       break

Run Your Training Script: Execute your modified training script to train your YOLOv5 model with the custom early stopping callback.

This is a basic example to get you started. Depending on your specific requirements, you might need to adjust the callback logic and how you integrate it into the training loop.

Feel free to ask if you have any further questions or need additional assistance. Happy training! 😊

Originally posted by @glenn-jocher in https://github.com/ultralytics/yolov5/issues/5561#issuecomment-2275619501

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Hi @7rkMnpl,

To integrate a custom callback with early stopping in YOLOv5, you can follow these steps to modify the training script. This will allow you to include your custom callback logic effectively.

1. Create Your Custom Callback

First, define your custom callback class. For instance, you might want to create a callback that monitors a specific metric and stops training based on that metric.

class CustomEarlyStopping:
    def __init__(self, patience=10, min_delta=0):
        self.patience = patience
        self.min_delta = min_delta
        self.best_score = None
        self.counter = 0

    def __call__(self, current_score):
        if self.best_score is None:
            self.best_score = current_score
        elif current_score < self.best_score + self.min_delta:
            self.counter += 1
            if self.counter >= self.patience:
                return True
        else:
            self.best_score = current_score
            self.counter = 0
        return False

2. Integrate the Callback into the Training Loop

Next, modify the training loop in train.py to include your custom callback. You will need to check the callback condition at the end of each epoch.

from train import train

# Initialize your custom callback
custom_early_stopping = CustomEarlyStopping(patience=10, min_delta=0.01)

# Modify the training loop to include the callback check
for epoch in range(epochs):
    # Training code...

    # Calculate your custom metric (e.g., recall)
    current_score = calculate_recall()

    # Check the custom early stopping condition
    if custom_early_stopping(current_score):
        print(f"Early stopping at epoch {epoch}")
        break

3. Run Your Training Script

Finally, execute your modified training script to train your YOLOv5 model with the custom early stopping callback.