mobilenet_v4_hybrid_large

lucid.models.mobilenet_v4_hybrid_large(num_classes: int = 1000, **kwargs) → MobileNet_V4

Overview

The mobilenet_v4_hybrid_large function instantiates a MobileNet_V4 model variant that leverages a hybrid architecture in its large configuration. This model is designed to maximize performance and accuracy for demanding classification tasks by integrating advanced convolutional strategies with supplementary architectural enhancements.

Total Parameters: 37,755,152

Function Signature

@register_model
def mobilenet_v4_hybrid_large(num_classes: int = 1000, **kwargs) -> MobileNet_V4

Parameters

• num_classes (int, optional): Specifies the number of output classes. The default value is 1000, aligning with standard benchmarks like ImageNet.

• **kwargs (dict): Additional keyword arguments for further customization of the model. These can override default configurations to adapt the network for specific scenarios.

Usage Example

>>> import lucid.models as models
>>> model = models.mobilenet_v4_hybrid_large(num_classes=1000)
>>> print(model)

Details

The mobilenet_v4_hybrid_large function delivers a high-performance MobileNet-v4 model by employing a large-scale hybrid architecture that integrates cutting-edge convolutional techniques with extra architectural optimizations. This design is optimal for scenarios where the highest accuracy is required and computational resources are abundant.

Note

Due to its increased complexity and resource demands, the hybrid large variant is best suited for applications where performance is prioritized over computational efficiency.