Readme.md

ResNet50 Model Implementation

This implementation provides a customizable ResNet50 model for image classification tasks.

Model Architecture

The model uses the ResNet50 architecture, which is a deep convolutional neural network with 50 layers. Key features include:

• Based on the standard ResNet50 architecture
• Customizable number of output classes
• Modified final fully connected layer to match the desired number of classes
• Initialized from scratch (no pre-training)

Functions

get_model(num_classes)

Initializes a new ResNet50 model with a custom number of output classes.

• Input: Number of classes (integer)
• Output: Initialized ResNet50 model
• Note: The model is initialized without pre-trained weights

save_model(model, path)

Saves the model's state dictionary to a specified path.

• Input:
  • model: Trained PyTorch model
  • path: File path for saving the model

load_model(num_classes, path)

Loads a previously saved model.

• Input:
  • num_classes: Number of output classes
  • path: Path to the saved model file
• Output: Loaded ResNet50 model

Usage Example

model = get_model(num_classes=1000)
save_model(model, 'model.pth')
loaded_model = load_model(num_classes=1000, path='model.pth')

Attachments