Building attention mechanisms and Transformer models from scratch. Alias ATF.
Project description
Attention mechanisms and Transformers
- This goal of this repository is to host basic architecture and model traning code associated with the different attention mechanisms and transformer architecture.
- At the moment, I more interested in learning and recreating these new architectures from scratch than full-fledged training. For now, I'll just be training these models on small datasets.
Installation
- Using pip to install from pypi
pip install Attention-and-Transformers
- Using pip to install latest version from github
pip install git+https://github.com/veb-101/Attention-and-Transformers.git
- Local clone and install
git clone https://github.com/veb-101/Attention-and-Transformers.git atf
cd atf
python setup.py install
Test Installation
python load_test.py
Attention Mechanisms
# No. | Mechanism | Paper |
---|---|---|
1 | Multi-head Self Attention | Attention is all you need |
2 | Multi-head Self Attention 2D | MobileViT V1 |
2 | Separable Self Attention | MobileViT V2 |
Transformer Models
# No. | Models | Paper |
---|---|---|
1 | Vision Transformer | An Image is Worth 16x16 Words: |
2 | MobileViT-V1 | MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer |
3 | MobileViT-V2 (under development) | Separable Self-attention for Mobile Vision Transformers |
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