How to Fine-tune Pretrained Convolutional Neural Networks with PyTorch

In the realm of deep learning, fine-tuning pretrained models is like putting the finishing touches on a masterpiece. By leveraging powerful pretrained convolutional neural networks (CNNs), you can build sophisticated applications more efficiently. This guide will walk you through the process of fine-tuning CNNs using the cnn-finetune package in PyTorch, allowing you to adapt models for your specific tasks.

Features of the cnn-finetune Library

• Access to popular CNN architectures pretrained on ImageNet.
• Automatic replacement of classifiers on top of the network allows training with datasets of varying class numbers.
• Support for images of any resolution.
• Ability to add Dropout layers or custom pooling layers.

Supported Architectures and Models

The following architectures and models are supported:

From the torchvision package:

• ResNet (resnet18, resnet34, resnet50, resnet101, resnet152)
• ResNeXt (resnext50_32x4d, resnext101_32x8d)
• DenseNet (densenet121, densenet169, densenet201, densenet161)
• Inception v3 (inception_v3)
• VGG (vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, vgg19_bn)
• SqueezeNet (squeezenet1_0, squeezenet1_1)
• MobileNet V2 (mobilenet_v2)
• ShuffleNet v2 (shufflenet_v2_x0_5, shufflenet_v2_x1_0)
• AlexNet (alexnet)
• GoogLeNet (googlenet)

From the Pretrained models for PyTorch package:

• ResNeXt, NASNet-A Large, NASNet-A Mobile, Inception-ResNet v2, Dual Path Networks, Inception v4, Xception, Squeeze-and-Excitation Networks, PNASNet-5-Large, PolyNet.

Requirements

• Python 3.5+
• PyTorch 1.1+

Installation

To install the cnn-finetune package, simply run:

pip install cnn_finetune

Example Usage

Let’s consider how to create models using the cnn-finetune library with a few practical examples:

1. Create a Model with ImageNet Weights for 10 Classes

from cnn_finetune import make_model
model = make_model(resnet18, num_classes=10, pretrained=True)

2. Create a Model with Dropout

model = make_model(nasnetalarge, num_classes=10, pretrained=True, dropout_p=0.5)

3. Using Global Max Pooling Instead of Global Average Pooling

import torch.nn as nn
model = make_model(inceptionresnetv2, num_classes=10, pretrained=True, pool=nn.AdaptiveMaxPool2d(1))

4. Create a VGG16 Model that Takes Images of Size 256×256 Pixels

model = make_model(vgg16, num_classes=10, pretrained=True, input_size=(256, 256))

5. Create a VGG16 Model with Custom Classifier

import torch.nn as nn
def make_classifier(in_features, num_classes):
    return nn.Sequential(
        nn.Linear(in_features, 4096),
        nn.ReLU(inplace=True),
        nn.Linear(4096, num_classes),
    )
model = make_model(vgg16, num_classes=10, pretrained=True, input_size=(256, 256), classifier_factory=make_classifier)

6. Show Preprocessing Used to Train the Original Model on ImageNet

model = make_model(resnext101_64x4d, num_classes=10, pretrained=True)
print(model.original_model_info)

Troubleshooting

If you encounter issues when fine-tuning your model, consider the following troubleshooting tips:

• Ensure you have the correct version of Python and PyTorch installed as mentioned in the requirements.
• Check your data format and preprocessing to ensure it matches the requirements of the model.
• If you’re unsure about the model’s architecture, revisit the supported architectures section and verify you’re using a compatible model.
• Experiment with different training hyperparameters such as learning rate or batch size that might impact performance.

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