vitax 0.0.10

Vitax: Vision Transformers in Flax

VITAX

VITAX: An open-source platform for training and inference of Vision Transformers (ViT) with the new and elegant Flax NNX API.

This library provides a clean, from-scratch implementation of the Vision Transformer model and makes it easy to leverage powerful pretrained models from the Hugging Face Hub for your own computer vision tasks.

Core Features

• Modern Flax API: Built entirely using flax.nnx, offering a more intuitive, object-oriented, and explicit way to build neural networks in JAX.
• Hugging Face Integration: Seamlessly load pretrained ViT weights from google/vit-* models for transfer learning and fine-tuning.
• Custom Models: Easily create and train Vision Transformer models from scratch with custom configurations.
• Simple & Efficient Training: Includes a straightforward and JIT-compiled training and evaluation pipeline using optax for optimization.
• Modular Design: The code is well-structured, separating the model definition, weight loading, and training logic for clarity and extensibility.

Installation

You can install vitax directly from PyPI:

pip install vitax

Model creation with Vitax

In Vitax, you can create vision transformers in different ways

Load a Pretrained Model for Fine-Tuning

This is the most common use case. You can load a model pretrained on ImageNet-21k and adapt its final classification layer for your specific dataset (e.g., CIFAR-100 with 100 classes).

from vitax.models import create_model

# Load a base pretrained ViT model and adapt it for 100 classes
model = create_model(
    'google/vit-base-patch16-224',
    num_classes=100,
    pretrained=True
)

Create a Model from Scratch (Random Weights)

If you want to train a model from the ground up, you can create one with random weights. You can either use a standard configuration or define your own.

Using a standard model configuration:

from vitax.models import create_model

# Create a 'vit-base-patch16-224' architecture with random weights
model = create_model(
    'google/vit-base-patch16-224',
    num_classes=10, # For a 10-class dataset like CIFAR-10
    pretrained=False
)

Using a fully custom architecture:

from vitax.models import create_model

# Define a custom configuration for a smaller model, compatible with HuggingFace ViT config
custom_config = {
    'image_size': 224,
    'patch_size': 16,
    'num_hidden_layers': 6,          # Fewer layers
    'num_attention_heads': 8,           # Fewer attention heads
    'intermediate_size': 500,          # Smaller MLP dimension
    'hidden_size': 128,       # Embedding dimension
}

# Create the custom model with random weights
custom_model = create_model(
    name_or_config=custom_config,
    num_classes=10,
    pretrained=False
)

Contributing

Contributions are welcome! If you find a bug or have a feature request, please open an issue on the GitHub repository.

License

This project is licensed under the MIT License. See the LICENSE file for details.