Fine-tune Pretrained Convolutional Neural Networks with PyTorch

As the field of artificial intelligence continues to evolve, Convolutional Neural Networks (CNNs) have emerged as a powerful tool for image classification and recognition tasks. These neural networks can be trained on large datasets, such as ImageNet, to learn features and patterns that can be transferred to other similar tasks. However, training CNNs from scratch can be time-consuming and computationally expensive.

To address these challenges, the PyTorch library provides a solution called pytorch-cnn-finetune. This library allows you to fine-tune pretrained CNN architectures, giving you the flexibility to adapt them to your specific image classification tasks. In this article, we will explore the features, supported architectures, major changes, requirements, and installation process of pytorch-cnn-finetune.

Features

The pytorch-cnn-finetune library offers several features that make it a powerful tool for deep learning practitioners:

1. Access to popular CNN architectures pretrained on ImageNet: The library provides access to a wide range of CNN architectures, including ResNet, ResNeXt, DenseNet, Inception, VGG, SqueezeNet, MobileNet, ShuffleNet, AlexNet, GoogLeNet, and more. These architectures are pretrained on the large-scale ImageNet dataset, enabling transfer learning for various image classification tasks.

2. Automatic replacement of classifiers: One common challenge when using pretrained models is adapting them to datasets with a different number of classes. The pytorch-cnn-finetune library automatically replaces the classifier layer on top of the network, allowing you to train the network with a dataset that has a different number of classes.

3. Support for images with any resolution: Unlike many other libraries, pytorch-cnn-finetune allows you to use images with any resolution, not just the resolution used for training the original model on ImageNet. This flexibility is crucial when working with datasets that have varying image sizes.

4. Addition of custom layers: The library enables you to add additional layers to the pretrained models. Whether you want to add a Dropout layer for regularization or a custom pooling layer, pytorch-cnn-finetune provides the flexibility to modify the architecture according to your needs.

Supported Architectures and Models

The pytorch-cnn-finetune library supports a wide range of architectures and models, sourced from both the torchvision package and the Pretrained models for PyTorch package. Here is a list of some of the supported architectures:

From the torchvision package:
– ResNet
– ResNeXt
– DenseNet
– Inception
– VGG
– SqueezeNet
– MobileNet V2
– ShuffleNet v2
– AlexNet
– 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

This wide range of supported architectures provides flexibility in choosing the most suitable model for your image classification task.

Requirements and Installation

Before using the pytorch-cnn-finetune library, ensure that you have the following requirements:

• Python 3.5+
• PyTorch 1.1+

To install the library, simply run the following command:

pip install cnn_finetune

Example Usages

To illustrate the usage of the pytorch-cnn-finetune library, let’s explore a few example scenarios:

1. Making a model with ImageNet weights for 10 classes:

“`
from cnn_finetune import make_model

model = make_model(‘resnet18’, num_classes=10, pretrained=True)
“`

1. Making a model with Dropout layer:

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

1. Making a model with 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))
“`

1. Making a VGG16 model that takes images of size 256×256 pixels:

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

1. Making a VGG16 model with a 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)
“`

Conclusion

The pytorch-cnn-finetune library provides a powerful and flexible solution for fine-tuning pretrained Convolutional Neural Networks using PyTorch. With its wide range of supported architectures, automatic classifier replacement, flexibility in image resolution, and the ability to add custom layers, this library empowers deep learning practitioners to adapt state-of-the-art models to their specific image classification tasks.

By leveraging the pytorch-cnn-finetune library, you can save time and computational resources by building upon the knowledge learned by pretraining on large-scale datasets. With the ability to access and modify pretrained models, you can quickly prototype and deploy deep learning solutions that make accurate predictions on your own datasets. Embrace the power of fine-tuning with pytorch-cnn-finetune and take your image classification projects to new heights.