How to simplify ResNet50 in a classification task?

To simplify ResNet50 for classification tasks and improve performance, several approaches can be applied:

• Pruning techniques — removing less significant weights or filters, which reduces the model size and speeds up inference.
• Quantization — converting weights and activations from float32 to more compact formats (e.g., int8), which reduces memory requirements and accelerates computations.
• Using lighter architectures or modifications of ResNet — for example, ResNet with fewer layers (ResNet18, ResNet34) or mobile versions (MobileNet, EfficientNet).
• Fine-tuning with frozen base layers — to avoid overfitting the entire model, only the top layers are trained, saving resources.
• Input data optimization — reducing image resolution or applying augmentations to speed up training.

Example of using quantization in PyTorch:

import torch
from torchvision.models import resnet50

model = resnet50(pretrained=True)
model.eval()

# Example of dynamic quantization
quantized_model = torch.quantization.quantize_dynamic(
    model, {torch.nn.Linear}, dtype=torch.qint8
)

This will reduce the model size and speed up inference without significant loss of accuracy.