Fastai is a popular deep learning library built on top of PyTorch. It provides an easy-to-use interface and a range of useful techniques for training deep learning models. One such technique is progressive resizing, which helps in improving the performance and accuracy of models.

Progressive resizing involves training a model with progressively larger image sizes during training. This technique has been shown to be effective in improving model performance, generalization, and reducing overfitting. It allows the model to learn both low-level and high-level features from different resolutions, helping it to capture more intricate details of the data.

Here, we will demonstrate how to implement progressive resizing using the fastai library in Python.

First, we need to import the necessary libraries:

from fastai.vision.all import *

Next, we can define our data and create a DataBlock object. For simplicity, let’s assume we are working with an image classification task. We will use the ImageDataLoaders.from_folder method to load the data from a folder containing subfolders for each class.

path = Path("path/to/dataset")
data = ImageDataLoaders.from_folder(path, train="train", valid="valid", item_tfms=Resize(224))

In the example above, we specify the path to our dataset and set the train and valid folders. We also use the Resize transform to resize the images to a fixed size of 224x224 pixels. This will be our initial image size for training.

Now, we can create a Learner object and use the fit_one_cycle method to train our model:

learn = cnn_learner(data, resnet34, metrics=accuracy)
learn.fit_one_cycle(10)

In this example, we use a ResNet-34 architecture and train the model for 10 epochs.

Once the initial training is complete, we can update our data object with a new image size and train the model again:

data = data.new(item_tfms=Resize(448))
learn.dls = data
learn.fit_one_cycle(10)

Here, we update the image size to 448x448 pixels and train the model for another 10 epochs.

We can repeat this process by further increasing the image size and training the model for additional epochs until the desired accuracy or performance is achieved.

Progressive resizing can be a useful technique to improve the performance of deep learning models. By gradually increasing the image size during training, the model can learn to capture more detailed features and generalize better. The fastai library provides a convenient way to implement this technique and experiment with different image sizes in a few lines of code.

In conclusion, progressive resizing is a valuable technique in deep learning that can help improve model performance. By progressively increasing the image size during training, models can learn to capture more intricate details. Fastai makes it easy to implement progressive resizing in Python, enabling us to experiment with different image sizes and improve our models’ accuracy and generalization abilities.