Master Image Classification Effortlessly with FAI-Trainer: A Comprehensive Guide

Master Image Classification Effortlessly with FAI-Trainer: A Comprehensive Guide

Welcome to this beginner-friendly tutorial on using FAI-Trainer by Falcons.AI, a powerful Python package designed to make training and testing image classification models with PyTorch as simple as possible. In this guide, we'll walk you through every step, from installation to model testing, ensuring you can harness the full power of FAI-Trainer with ease.

What is FAI-Trainer?

FAI-Trainer is a comprehensive tool that automates the tedious aspects of image classification tasks. It handles everything from data preparation and model training to testing, allowing you to focus on developing accurate models.

Key Features:

  • Data Preparation: Converts, resizes, and cleans your image dataset.
  • Dataset Splitting: Automatically divides your dataset into training and validation sets.
  • Model Training: Trains a ResNet50 model with customizable parameters.
  • Progress Tracking: Provides visual feedback during training and validation.
  • Model Testing: Evaluates trained models on specific images or datasets.

Installation

First, let's install FAI-Trainer. Open your terminal and run:

pip install fai-trainer

This command will install FAI-Trainer along with its dependencies.

Preparing Your Dataset

Before you can train a model, you need to prepare your dataset. FAI-Trainer requires your dataset to be organized in a specific structure:

datasets/
├── class1/
│   ├── image1.jpg
│   ├── image2.jpg
│   └── ...
├── class2/
│   ├── image1.jpg
│   ├── image2.jpg
│   └── ...
└── class3/
    ├── image1.jpg
    ├── image2.jpg
    └── ...

Ensure your dataset follows this structure for smooth processing.

Using FAI-Trainer

1. Cleaning Up Previous Data

If you have previously processed data or training results that you want to clean up, you can use the --clean option. This command will delete all temporary files and reset your environment:

fai-trainer --clean

2. Preparing Data and Training the Model

To prepare your data and train a model, you can use the following command. This command will handle data preparation and model training in one go:

fai-trainer --prepare-data --train --batch-size 32 --epochs 3 --image-size 96 96

Here's a breakdown of the command:

  • --prepare-data: Prepares the dataset by converting, resizing, and cleaning the images.
  • --train: Trains the model on the prepared dataset.
  • --batch-size 32: Sets the batch size for training.
  • --epochs 3: Sets the number of epochs for training.
  • --image-size 96 96: Resizes images to 96x96 pixels.

3. Testing the Model

Once the model is trained, you can test it on a specific image or on the entire validation dataset. To test the model on a single image, use the command:

fai-trainer --test --image-path path/to/your/image.jpg --image-size 96 96

Here's a breakdown of the command:

  • --test: Indicates that the model will be tested.
  • --image-path path/to/your/image.jpg: Path to the image you want to test.
  • --image-size 96 96: Resizes the test image to 96x96 pixels.

If you want to test the model on the validation dataset instead, simply omit the --image-path parameter:

fai-trainer --test --image-size 96 96

4. Cleaning Up

If you need to clean up the directories after processing, you can use:

fai-trainer --clean

This command removes all temporary files and resets the environment for a fresh start.

Conclusion

FAI-Trainer is a powerful and user-friendly tool that simplifies the process of image classification. With its easy-to-use commands and robust features, you can focus on developing accurate models without worrying about the complexities of data preparation and training.

For more details and to get started with FAI-Trainer, visit the Package Repository.

Happy training!