Fastai is a high-level library built on top of PyTorch that simplifies the process of training deep learning models. It provides a comprehensive set of tools and pre-built neural network architectures that make it easy to solve various computer vision tasks, including image-to-image transformation.

In this tutorial, we will explore how to use fastai for image-to-image transformation tasks using python. We will walk through an example where we convert grayscale images to color images using a pre-trained model.

Setup

Before we begin, let’s make sure we have fastai and its dependencies installed. Run the following command to install them:

pip install fastai

Data Preparation

To train our image-to-image transformation model, we need a dataset of grayscale images paired with their corresponding color images. For this tutorial, let’s assume we have a folder named data that contains two subfolders: grayscale and color. The grayscale folder contains grayscale images, and the color folder contains their corresponding color images.

Let’s create a DataLoaders object from this dataset using fastai’s ImageDataLoaders class. Run the following code:

from fastai.vision.all import *

path = Path('data')
dls = ImageDataLoaders.from_folder(path, train='grayscale', valid='color')

Model Training

Fastai provides pre-trained models for image-to-image transformation tasks. We will use one of these models for our task. Let’s load the pre-trained model using the unet_learner function and train it:

learn = unet_learner(dls, resnet34)
learn.fine_tune(10)

This code initializes a U-Net model architecture based on ResNet34 and uses transfer learning to fine-tune the model on our dataset for 10 epochs. The fine_tune method automatically determines the appropriate learning rate and trains the model.

Inference

After training the model, let’s test it by converting a grayscale image to a color image. First, we need to load the grayscale image using fastai’s load_image function:

img_gray = load_image('path_to_grayscale_image.jpg')

Next, we can pass this grayscale image to our trained model for inference using the predict method:

img_color = learn.predict(img_gray)[0]

The predict method returns a tuple with the predicted outputs. We extract the first element of this tuple, which contains the predicted color image.

Conclusion

In this tutorial, we have seen how to use fastai for image-to-image transformation tasks in Python. We learned how to prepare the data, train a pre-trained model, and perform inference. Fastai’s high-level interface and pre-trained models make it easy to achieve state-of-the-art results in various computer vision tasks, including image-to-image transformation.