easy-image-model 0.1.1

Easy PyTorch image classification library

Easy Image Model

by Harrison Hensarling

Easy Image Model is a lightweight Python library for quickly building, training, and evaluating image classification models using PyTorch. It is designed for simplicity and ease of use. Supports:

-small datasets

-non-normalized images

-batch training

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Features

• Create a custom neural network with any number of hidden layers and nodes.
• Train models on folders of labeled images (each folder represents a category).
• Batch training for faster convergence.
• Automatically preprocesses images (resizing, normalization) — no manual preprocessing required except for labeling.
• Evaluate a single image and return category probabilities.
• Save and reload model weights as JSON for portability.
• Customizable image input size (default 224x224, can be adjusted for your dataset).
• Supported image types:
  • .jpg
  • .jpeg
  • .png
  • .bmp
  • .gif (only first frame is used)
  • .tif
  • .tiff
  • More supported but untested

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Installation

pip install easy-image-model

Or for development:

git clone https://github.com/hhensar/easy-image-model.git
cd easy-image-model
pip install -e ".[dev]"

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Example Usage

from easy_image_model import create_model, train_model_batch_folders, evaluate_model

# Define categories
categories = ['Eagles', 'Penguins', 'Owls', 'Others']

# Create model with hidden layers (3 layers with 512, 256, 128 nodes)
# img_size defaults to 224, but can be customized (e.g., 128, 256, 512)
model = create_model([512, 256, 128], categories, img_size=224)

# Train on folders (each folder contains images of one category)
folder_paths = {
    'Eagles': 'dataset/Eagles',
    'Penguins': 'dataset/Penguins',
    'Owls': 'dataset/Owls',
    'Others': 'dataset/Others'
}
model = train_model_batch_folders(model, folder_paths, batch_size=4, epochs=5)

# Evaluate a single image
result = evaluate_model(model, 'test_images/test1.jpg')
print(result)

Example Output:

{'Eagles': 0.87, 
 'Penguins': 0.05, 
 'Owls': 0.23, 
 'Others': 0.1}