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Open-weight model · Image segmentation

upernet-swin-large

by OpenMMLab openmmlab/upernet-swin-large

UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper Unified Perceptual Parsing for Scene Understanding by Xiao et al.

Parameters234M
Context—
Weights1.9 GB
Licensemit
AccessOpen weights
Monthly Downloads27.7k

Runs On

What it takes to serve upernet-swin-large (234M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 0.5 GB 0.6 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.2 GB 0.3 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.1 GB 0.1 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

Model Card

By OpenMMLab, published under mit, revision e7abf9547552.

UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper Unified Perceptual Parsing for Scene Understanding by Xiao et al. Combining UperNet with a Swin Transformer backbone was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Disclaimer: The team releasing UperNet + Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. UperNet is a framework for semantic segmentation. It consists of several components, including a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM). Any visual backbone can…

Read OpenMMLab's full model card

UperNet, Swin Transformer large-sized backbone

UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper Unified Perceptual Parsing for Scene Understanding by Xiao et al.

Combining UperNet with a Swin Transformer backbone was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.

Disclaimer: The team releasing UperNet + Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.

Model description

UperNet is a framework for semantic segmentation. It consists of several components, including a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM).

Any visual backbone can be plugged into the UperNet framework. The framework predicts a semantic label per pixel.

Intended uses & limitations

You can use the raw model for semantic segmentation. See the model hub to look for fine-tuned versions (with various backbones) on a task that interests you.

How to use

For code examples, we refer to the documentation.

Configuration

Architecture
UperNetForSemanticSegmentation
Hidden size
512
Stored precision
float32
Model type
upernet

Identity and Version

Repository
openmmlab/upernet-swin-large
Publisher
OpenMMLab
Task
Image segmentation
Modality
Image
Library
transformers
Parameters
234M parameters
Languages
en
Revision
e7abf954755276353891cc5991c3b047fbb6e646
First published
2023-01-13
Last updated
2023-04-24

Files and Weights

6 files, 1.9 GB in total. The weights are 2 files totalling 1.9 GB in bin, safetensors.

Weights2 files · 1.9 GB
Configuration2 files · 9.4 KB
Documentation1 file · 1.6 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights940.0 MB 9687e932e51b
pytorch_model.binWeights940.1 MB 78069b1c568d
config.jsonConfiguration9.0 KB —
preprocessor_config.jsonConfiguration372 B —
README.mdDocumentation1.6 KB —
.gitattributesRepository1.5 KB —

License and Download

License
mit
Access
Open weights, no gate
Download size
1.9 GB
Download from OpenMMLab

Released by OpenMMLab through its official repository on Hugging Face. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published1.9 GB
16-bit0.5 GB
8-bit0.2 GB
4-bit0.1 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About upernet-swin-large

How much GPU memory does upernet-swin-large need?

About 0.6 GB at 16-bit and 0.1 GB at 4-bit: the weights (234M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run upernet-swin-large on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use upernet-swin-large commercially?

Yes. upernet-swin-large is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

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