MODEL PROFILE

hiera-tiny-224-hf

facebook · hiera-tiny-224-hf

**Hiera** is a _hierarchical_ vision transformer that is fast, powerful, and, above all, _simple_. It was introduced in the paper Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles and outperforms the state-of-the-art across a wide array of image and video tasks _while being much faster_. Vision transformers like ViT use the same spatial resolution and number of features throughout the whole network. But this is inefficient: the early layers don't need that many features, and the later layers don't need that much spatial resolution. Prior hierarchical models like ResNet accounted for this by using fewer features at the start and less spatial resolution at the end. Several domain specific vision transformers have been introduced that employ this hierarchical design, such as Swin or MViT. But in the pursuit of state-of-the-art results using fully supervised training on ImageNet-1K, these models have become more and more complicated as they add specialized modules to make up for spatial biases that ViTs lack. While these changes produce effective models with attractive FLOP counts, under the hood the added complexity makes these models _slower_ overall.

license:cc-by-nc-4.0
Context windowNot disclosed
Input → Output待核验 → 待核验
Model weightsNot verified
Profile updated2026/11/2 19:15:53
OVERVIEW

Model overview

**Hiera** is a _hierarchical_ vision transformer that is fast, powerful, and, above all, _simple_. It was introduced in the paper Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles and outperforms the state-of-the-art across a wide array of image and video tasks _while being much faster_. Vision transformers like ViT use the same spatial resolution and number of features throughout the whole network. But this is inefficient: the early layers don't need that many features, and the later layers don't need that much spatial resolution. Prior hierarchical models like ResNet accounted for this by using fewer features at the start and less spatial resolution at the end. Several domain specific vision transformers have been introduced that employ this hierarchical design, such as Swin or MViT. But in the pursuit of state-of-the-art results using fully supervised training on ImageNet-1K, these models have become more and more complicated as they add specialized modules to make up for spatial biases that ViTs lack. While these changes produce effective models with attractive FLOP counts, under the hood the added complexity makes these models _slower_ overall.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:cc-by-nc-4.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。