MODEL PROFILE

pvt-tiny-224

Xrenya · pvt-tiny-224

Pyramid Vision Transformer (PVT) model pre-trained on ImageNet-1K (1 million images, 1000 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and first released in this repository. Disclaimer: The team releasing PVT did not write a model card for this model so this model card has been written by [Rinat S. [@Xrenya]](https://huggingface.co/Xrenya). The Pyramid Vision Transformer (PVT) is a transformer encoder model (BERT-like) pretrained on ImageNet-1k (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.

Image classificationlicense:apache-2.0
Context windowNot disclosed
Input → Output图像 → 待核验
Model weightsNot verified
Profile updated2026/11/2 19:16:20
OVERVIEW

Model overview

Pyramid Vision Transformer (PVT) model pre-trained on ImageNet-1K (1 million images, 1000 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and first released in this repository. Disclaimer: The team releasing PVT did not write a model card for this model so this model card has been written by [Rinat S. [@Xrenya]](https://huggingface.co/Xrenya). The Pyramid Vision Transformer (PVT) is a transformer encoder model (BERT-like) pretrained on ImageNet-1k (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.

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:apache-2.0。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。