Hiera is a hierarchical vision transformer designed to be fast, simple, and strong across image and video recognition tasks. The core idea is to use straightforward hierarchical attention with a minimal set of architectural “bells and whistles,” achieving competitive or superior accuracy while being markedly faster at inference and often faster to train. The repository provides installation options (from source or Torch Hub), a model zoo with pre-trained checkpoints, and code for evaluation and fine-tuning on standard benchmarks. Documentation emphasizes that model weights may have separate licensing and that the code targets practical experimentation for both research and downstream tasks. Community discussions cover topics like dataset pretrains, integration in other frameworks, and comparisons with related implementations. Security and contribution guidelines follow Meta’s open-source practices, and activity shows ongoing interest and usage across the community.

Features

  • Hierarchical attention transformer architecture
  • High-throughput inference with strong accuracy
  • Model zoo with ready-to-use checkpoints
  • Training and fine-tuning scripts for common benchmarks
  • Torch Hub and source installation paths
  • Active community discussions and issue tracking

Project Samples

Project Activity

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License

Apache License V2.0

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Additional Project Details

Programming Language

Python

Related Categories

Python Computer Vision Libraries

Registered

2025-10-08