Train Model

Function Signature

train_model(data, architecture='resnet', epochs=100, batch_size=32, learning_rate=0.001, device='cpu')

Parameters

• data: Dataset The dataset used for training the model. This should be an instance of a dataset class that provides images and labels for training.

• architecture: str, optional, default: ‘resnet’ The neural network architecture to be used for training. Supported architectures include ‘resnet’, ‘vgg’, ‘mobilenet’, and more.

• epochs: int, optional, default: 100 The number of training epochs. An epoch refers to one complete pass through the entire training dataset.

• batch_size: int, optional, default: 32 The number of samples per batch of computation. Larger batch sizes can improve training speed at the cost of memory usage.

• learning_rate: float, optional, default: 0.001 The learning rate controls how much to change the model in response to the estimated error each time the model weights are updated.

• device: str, optional, default: ‘cpu’ Specifies the device to run the training on. Options include ‘cpu’, ‘cuda’, or any specific GPU like ‘cuda:0’. Using CUDA enables GPU acceleration, significantly improving training speed.

Return Value

• model: object The trained model instance, which can be used for inference or further fine-tuning.

• logs: dict A dictionary containing training logs, including metrics such as loss and accuracy for each epoch.

Example Usage

Here’s a basic example of how to use the train_model function to train a model on a dataset:

from ai_vision_library import train_model, load_dataset

# Load a custom dataset
dataset = load_dataset('path_to_dataset')

# Train the model using the ResNet architecture
model, logs = train_model(data=dataset, architecture='resnet', epochs=50, batch_size=64, device='cuda')

# Save the trained model
model.save('my_custom_model.h5')

# Print training logs
for epoch, metrics in logs.items():
    print(f"Epoch {epoch}: Loss = {metrics['loss']:.4f}, Accuracy = {metrics['accuracy']*100:.2f}%")

Detailed Example: Fine-Tuning a Pre-Trained Model

This example demonstrates how to fine-tune a pre-trained model on your dataset. Fine-tuning is particularly useful when you have a limited dataset and want to leverage the knowledge of a model trained on a larger dataset.

from ai_vision_library import train_model, load_dataset, load_pretrained_model

# Load a custom dataset
dataset = load_dataset('path_to_dataset')

# Load a pre-trained model (e.g., ResNet trained on ImageNet)
pretrained_model = load_pretrained_model('resnet', pretrained=True)

# Fine-tune the pre-trained model on your dataset
model, logs = train_model(data=dataset, architecture='resnet', epochs=30, batch_size=32, learning_rate=0.0001, device='cuda')

# Save the fine-tuned model
model.save('fine_tuned_model.h5')

# Print training logs
for epoch, metrics in logs.items():
    print(f"Epoch {epoch}: Loss = {metrics['loss']:.4f}, Accuracy = {metrics['accuracy']*100:.2f}%")

Advanced Usage: Customizing the Training Process

To fully customize the training process, you can adjust additional parameters such as learning rate, batch size, and number of epochs:

• Learning Rate: Adjust the learning_rate parameter based on your specific dataset and model architecture. A lower learning rate may lead to slower but more stable convergence, while a higher learning rate can speed up training but may cause instability.

model, logs = train_model(data=dataset, architecture='resnet', epochs=100, batch_size=16, learning_rate=0.0005, device='cuda')

• Batch Size: Experiment with different batch sizes to balance training speed and memory usage. Larger batch sizes can improve GPU utilization but may require more memory.

model, logs = train_model(data=dataset, batch_size=128, device='cuda')

• Epochs: The number of epochs should be chosen based on the complexity of your model and the size of your dataset. More epochs may improve accuracy, but can also lead to overfitting.

model, logs = train_model(data=dataset, epochs=200, device='cuda')

Optimization Tips

• Use Data Augmentation: Augment your training data to improve model generalization. This can include techniques like flipping, rotating, or scaling images.

augmented_dataset = augment_data(dataset, flip=True, rotate=True, scale=True)
model, logs = train_model(data=augmented_dataset, architecture='vgg', epochs=50, device='cuda')

• Monitor Training: Regularly monitor training metrics, such as loss and accuracy, to ensure that the model is learning effectively. Consider using early stopping to prevent overfitting.

if logs[epoch]['loss'] > previous_loss:
    print("Warning: Loss is increasing, consider stopping early.")

Common Errors and Troubleshooting

• Out of Memory Errors: If you encounter out-of-memory errors, reduce the batch size or use a smaller model architecture.

• Slow Convergence: If training is slow or the model is not converging, try adjusting the learning rate or increasing the number of epochs.

• Overfitting: If the model performs well on the training data but poorly on validation data, consider using techniques like data augmentation, dropout, or early stopping.

Conclusion

The train_model function provides a flexible and powerful way to train custom object recognition models with the AI Vision Library. By understanding and leveraging its parameters, you can create models tailored to your specific application needs.

For more advanced examples and use cases, be sure to explore the other sections of this documentation.