Self-Attention-Cv

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Self-attention-cv is an Open Source & Models tool that provides implementations of various self-attention mechanisms for computer vision tasks. It focuses on self-attention modules in PyTorch using einsum and einops.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is self-attention-cv?

Self-attention-cv is an open-source repository offering implementations of diverse self-attention mechanisms specifically tailored for computer vision applications. Built in PyTorch, it leverages `einsum` and `einops` for efficient and flexible module creation. The repository serves as an ongoing collection of building blocks, enabling developers to integrate advanced attention models into their projects. It supports a range of computer vision tasks, including image recognition and segmentation, with examples for Multi-head attention, Axial attention, Vision Transformers (ViT), and TransUnet. It also includes various positional embedding implementations.

Best used for

Ideal for developers who need to implement and experiment with various self-attention mechanisms in computer vision, build custom Vision Transformers, and integrate advanced attention blocks into their PyTorch models. Especially valuable for researchers and practitioners working on image classification, segmentation, and other visual recognition tasks.

Common actions

implement attention mechanisms

build computer vision models

integrate self-attention

research deep learning architectures

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Capabilities

Key features

Multi-head attention implementation
Axial attention implementation
Vision Transformer (ViT)
TransUnet re-implementation
Bottleneck Attention block
1D positional embeddings
2D positional embeddings

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What programming language and framework does self-attention-cv use?

Self-attention-cv is implemented in Python and built on the PyTorch deep learning framework. It leverages `einsum` and `einops` for efficient tensor operations, making it suitable for developers familiar with these tools.

Can self-attention-cv be used for image classification tasks?

Yes, self-attention-cv includes implementations like the Vision Transformer (ViT) and ResNet50ViT, which are specifically designed for image classification. It provides the necessary building blocks to construct and train models for this task.

Does self-attention-cv offer modules for image segmentation?

Yes, the repository features a re-implementation of TransUnet, which combines a Vision Transformer encoder with a U-Net-like decoder. This makes it suitable for medical image segmentation and other pixel-level prediction tasks.

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This tool also appears in

AI Agents & Automation › AI Frameworks & Infra Research & Education › Scientific Computing

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