In this blog, we will guide you through the process of using FastAI for image classification. FastAI is a powerful library built on top of PyTorch that simplifies deep learning and makes it more accessible. From installing packages to fine-tuning a model, we’ve got you covered!

Prerequisites

• Python installed on your system.
• Access to Google Drive for storing images.
• The FastAI library installed.

Step-by-Step Guide

1. Install Required Libraries

First, we need to install the necessary packages. Open your terminal or Jupyter notebook and run:

!pip install -Uqq fastbook
!pip install fastai==2.5

2. Set Up FastAI

Next, we will import the FastAI library and set up our environment:

import fastbook
fastbook.setup_book()

3. Prepare Your Dataset

Now, let’s define the path to your image dataset stored in Google Drive, specifically in the ‘caballos’ folder:

from fastai.vision.widgets import *
path = Path('content/gdrive/My Drive/caballos')

4. Create a DataBlock

We’ll create a DataBlock to structure our data. Think of this step as organizing a library where books are categorized into sections. In this analogy:

• blocks: The sections represent the type of data we are working with (Images and Categories).
• splitter: This acts like the librarian who randomly chooses some books to set aside for reference (validation set).
• item_tfms: This is akin to resizing books for uniformity on the shelf (randomized image cropping).
• batch_tfms: Similar to applying protective covers to books for preservation.

Here’s how you implement it:

modelo = DataBlock(
    blocks=(ImageBlock, CategoryBlock),
    get_items=get_image_files,
    splitter=RandomSplitter(valid_pct=0.2, seed=42),
    get_y=parent_label,
    item_tfms=RandomResizedCrop(224, min_scale=0.5),
    batch_tfms=aug_transforms()
)

5. Data Loaders

Once the DataBlock is created, you can generate data loaders:

dls = modelo.dataloaders(path)

6. Create a Learner and Fine-Tune

Finally, you will create a learner and fine-tune your model using a pre-trained ResNet18 architecture, similar to hiring a seasoned professional for your library organization:

learn = cnn_learner(dls, resnet18, metrics=error_rate)
learn.fine_tune(4)

Troubleshooting

Here are some common issues you might encounter:

• Import Errors: Ensure that all libraries are correctly installed and updated.
• Path Issues: Double-check that the path to your dataset is accurate.
• Memory Errors: If you’re running on limited resources, consider reducing the batch size.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By following these simple steps, you now have a solid foundation for performing image classification using FastAI. We encourage you to explore further and challenge yourself with different datasets and models.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.