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Triplet Attention

Triplet Attention is an open-source PyTorch implementation of a lightweight, three-branch attention module for computer-vision neural networks. It computes attention weights that capture cross-dimension interactions, encoding channel and spatial information through rotations and residual transformations.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Triplet Attention — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you are developing or studying PyTorch vision networks and want to try a three-branch mechanism for modeling relationships across channel and spatial dimensions.

Who it is for

  • Researchers studying attention mechanisms for computer vision.
  • Practitioners experimenting with attention modules in image-classification or object-detection networks.

Documented uses

  • Add cross-dimension attention to a computer-vision neural network.
  • Train or evaluate supported ImageNet model architectures using the documented training entry point.
  • Review published benchmark configurations and pretrained-weight links for selected vision models.
Before you choose

Requirements & limits

The public GitHub repository lists an MIT license.

  • The implementation is PyTorch-specific.
  • The README documents an ImageNet training command and options, but the supplied documentation does not provide a general integration API or complete setup walkthrough.
  • The repository links pretrained weights and reports results for selected classification and detection configurations.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare an ImageNet training or evaluation run

Use the documented training entry point with an ImageNet dataset directory and choose supported training or evaluation options.

Before you start

  • The repository's PyTorch implementation.
  • A path to an ImageNet dataset.

Steps

  1. Provide the dataset directory as the positional DIR argument to train_imagenet.py.

  2. Select a listed architecture with --arch, or use the documented default, resnet18.

  3. Set documented training options such as --epochs, --batch-size, and --lr as needed.

  4. Use -e to evaluate, --pretrained to request pretrained weights, or --resume PATH to resume from a checkpoint.

Expected outcome

A configured ImageNet training or evaluation invocation using the options documented by the repository.

Guide limitations

  • The documented positional dataset input is for ImageNet; this guidance does not cover custom datasets or integrating the module into an arbitrary model.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
landskape-ai/triplet-attention · Official homepage title · retained in its source language
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