# upernet-convnext-tiny by OpenMMLab: Open-Weight Model Source: https://savrn.com/models/upernet-convnext-tiny Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve upernet-convnext-tiny (60M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/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](https://savrn.com/ai-index/pricing/gpus), read Oct 25, 2026. [upernet-convnext-tiny on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/upernet-convnext-tiny/gpus) ## Model Card By OpenMMLab, published under mit, revision 876ffc56b819. ### UperNet, ConvNeXt tiny-sized backbone UperNet framework for semantic segmentation, leveraging a ConvNeXt backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://savrn.com/papers/unified-perceptual-parsing-for-scene-understanding) by Xiao et al. Combining UperNet with a ConvNeXt backbone was introduced in the paper [A ConvNet for the 2020s](https://savrn.com/papers/a-convnet-for-the-2020s). Disclaimer: The team releasing UperNet + ConvNeXt 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](https://huggingface.co/models?search=openmmlab/upernet) to look for fine-tuned versions (with various backbones) on a task that interests you. #### How to use [Read the full model card (163 words)](https://savrn.com/models/upernet-convnext-tiny/card) ## Configuration Architecture UperNetForSemanticSegmentation Hidden size 512 Stored precision float32 Model type upernet ## Identity and Version Repository openmmlab/upernet-convnext-tiny Publisher OpenMMLab Task Image segmentation Modality Image Library transformers Parameters 60M parameters Languages en Revision 876ffc56b819a829448f7e81e9f8606deef6fb65 First published 2023-01-13 Last updated 2023-04-24 ## Files and Weights 6 files, 482.2 MB in total. The weights are 2 files totalling 482.2 MB in bin, safetensors. Weights2 files · 482.2 MB Configuration2 files · 9.1 KB Documentation1 file · 1.6 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 241.1 MB | cd55b1d36c66 | | pytorch_model.bin | Weights | 241.1 MB | 4f5d3b46a4fc | | config.json | Configuration | 8.8 KB | — | | preprocessor_config.json | Configuration | 372 B | — | | README.md | Documentation | 1.6 KB | — | | .gitattributes | Repository | 1.5 KB | — | ## License and Download License mit Access Open weights, no gate Download size 482.2 MB [Download from OpenMMLab](https://huggingface.co/openmmlab/upernet-convnext-tiny) Released by OpenMMLab through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Built From - Described by [arXiv:1807.10221](https://savrn.com/papers/unified-perceptual-parsing-for-scene-understanding) - Described by [arXiv:2201.03545](https://savrn.com/papers/a-convnet-for-the-2020s) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 482.2 MB | | 16-bit | 0.1 GB | | 8-bit | 0.1 GB | | 4-bit | 0.0 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About upernet-convnext-tiny ### How much GPU memory does upernet-convnext-tiny need? About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (60M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run upernet-convnext-tiny 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-convnext-tiny commercially? Yes. upernet-convnext-tiny 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. ## Similar Models Model · Image segmentation ### [fashn-human-parser](https://savrn.com/models/fashn-human-parser) [FASHN](https://savrn.com/model-publishers/fashn-ai) A SegFormer-B4 model fine-tuned for human parsing with 18 semantic classes, optimized for fashion and virtual try-on applications. This model segments human images into 18 semantic categories including body parts (face, hair, arms, hands, legs, feet, torso), clothing items (top, dress, skirt, pants, belt, scarf), and accessories (bag, hat, glasses, jewelry). The pipeline automatically manages GPU/CPU and returns per-class masks at the original image resolution. 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To purchase a… Open weights other 44M parameters transformers [View model](https://savrn.com/models/rmbg-1-4) ## OpenMMLab [All models and datasets](https://savrn.com/model-publishers/openmmlab) ## Versions - [876ffc56b819](https://savrn.com/models/upernet-convnext-tiny/versions/876ffc56b819) · current 2026-09-18 ## Explore More - [All image segmentation models](https://savrn.com/models/tasks/image-segmentation) - [All models under mit](https://savrn.com/models/licenses/mit) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-20. - [Hugging Face record](https://huggingface.co/openmmlab/upernet-convnext-tiny) - [How the hub is built](https://savrn.com/model-hub/methodology)