# The-AI-Summer/self-attention-cv

Implementation of various self-attention mechanisms focused on computer vision. Ongoing repository.

Repository: https://github.com/The-AI-Summer/self-attention-cv
Canonical: https://ross.abutalabs.com/products/self-attention-cv
Homepage: https://theaisummer.com/
Language: Python
License: MIT
License Family: permissive
Topics: deep-learning, transformer, transformers, self-attention, attention-mechanism, attention, machine-learning, machine-learning-algorithms, artificial-intelligence
Last push: 2021-09-14T13:23:07+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2040, "days_push": 1814, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1214, forks 152 (observed 2026-08-28T04:04:00.794926+00:00)

## What it is
A PyTorch library implementing various self-attention mechanisms and transformer building blocks for computer vision, including multi-head attention, axial attention, Vision Transformers, and TransUNet. It is pip-installable and built with einsum and einops.

## Use cases
- implement vision transformer for image classification
- add self-attention blocks to a CNN in PyTorch
- build a TransUNet segmentation model
- learn how multi-head self-attention works with code
- use axial attention for image feature maps
- experiment with transformer encoder blocks in PyTorch

## When to choose
- you need ready-made PyTorch implementations of vision attention modules
- you want a lightweight pip-installable library for ViT-style models
- you are studying or prototyping attention mechanisms for images

## When to avoid
- you need production-grade, actively maintained transformer code
- you work in TensorFlow or JAX instead of PyTorch
- you need large-scale pretrained vision models rather than building blocks

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing
- domain: deep-learning, computer-vision, machine-learning, artificial-intelligence
- platform: python, cross-platform
- tags: pytorch, self-attention, transformer, vision-transformer, einops, attention-mechanism, transunet, axial-attention, gpu

## Member repositories
- The-AI-Summer/self-attention-cv (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:00.794926+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:17:20.608146+00:00, confidence not recorded.
  - readme: https://github.com/The-AI-Summer/self-attention-cv (fetched 2026-08-28T04:04:00.794926+00:00, sha ec6ef2515160)
  - homepage: https://theaisummer.com/ (fetched 2026-08-29T12:25:39.956455+00:00, sha 3d3d850175aa)
  - site_page: https://theaisummer.com/about (fetched 2026-08-29T12:25:39.959602+00:00, sha 45ef0f87d5ec)
  - site_page: https://theaisummer.com/transformers-computer-vision (fetched 2026-08-29T12:25:39.961736+00:00, sha 614c047e5e25)
  - site_page: https://theaisummer.com/medical-image-deep-learning (fetched 2026-08-29T12:25:39.964187+00:00, sha b4983ed5dea3)
  - site_page: https://theaisummer.com/classifier-free-guidance (fetched 2026-08-29T12:25:39.966594+00:00, sha fcceb061f758)
  - site_page: https://theaisummer.com/iccv-2023 (fetched 2026-08-29T12:25:39.968975+00:00, sha 7ee5ac0208dc)
  - site_page: https://theaisummer.com/apache-airflow-tutorial (fetched 2026-08-29T12:25:39.971512+00:00, sha bfd889f5575b)
  - site_page: https://theaisummer.com/learn-pytorch (fetched 2026-08-29T12:25:39.973685+00:00, sha 51aa80054694)
- Data as of 2026-08-30T08:39:29.467469+00:00.