resnest_200¶
The resnest_200 function creates an instance of the ResNeSt-200 model, a lightweight variant of the ResNeSt architecture, tailored for tasks requiring fewer parameters.
Total Parameters: 70,201,288
Function Signature¶
@register_model
def resnest_200(num_classes: int = 1000, **kwargs) -> ResNeSt
Parameters¶
num_classes (int, optional): Number of output classes for the classification task. Defaults to 1000.
kwargs (dict, optional): Additional keyword arguments passed to the ResNeSt constructor, allowing customization of the model’s hyperparameters such as base_width, stem_width, cardinality, and radix.
Returns¶
ResNeSt: An instance of the ResNeSt-200 model, configured with the provided parameters.
Layer Configuration¶
The layer configuration for ResNeSt-200 is [3, 24, 36, 3], which represents the number of blocks in each of the four stages of the ResNet architecture:
Stage 1: 3 block
Stage 2: 24 block
Stage 3: 36 block
Stage 4: 3 block
Examples¶
from lucid.models import resnest_200
# Create a ResNeSt-200 model for 10-class classification
model = resnest_200(num_classes=10, base_width=64, stem_width=32)
# Forward pass with a sample input
input_tensor = lucid.random.randn((1, 3, 224, 224))
output = model(input_tensor)
print(output.shape) # Output: (1, 10)