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Open-weight model · Image segmentation

RK182X-CNN-Pytorch-UNet

by RKNNAI RKNNAI/RK182X-CNN-Pytorch-UNet

RK182X-CNN-Pytorch-UNet is an open-weight model for image segmentation from RKNNAI, released under gpl-3.0. Its published files total 33.0 MB.

本仓库提供由 milesial/Pytorch-UNet 转换得到的 RKNN CNN 模型。 - Model ID:RKNNAI/RK182X-CNN-Pytorch-UNet - 模型显示名称:RK182X-CNN-Pytorch-UNet - 源模型:milesial/Pytorch-UNet - 模型类型:CNN ModelScope 完整下载: Hugging Face 完整下载: ModelScope 指定配置下载: Hugging Face 指定配置下载: - 使用配套 RKNN Runtime…

Parameters—
Context—
Weights33.0 MB
Licensegpl-3.0
AccessOpen weights
Monthly Downloads—

Model Card

By RKNNAI, published under gpl-3.0, revision 82e8927f824c.

本仓库提供由 milesial/Pytorch-UNet 转换得到的 RKNN CNN 模型。 - Model ID:RKNNAI/RK182X-CNN-Pytorch-UNet - 模型显示名称:RK182X-CNN-Pytorch-UNet - 源模型:milesial/Pytorch-UNet - 模型类型:CNN ModelScope 完整下载: Hugging Face 完整下载: ModelScope 指定配置下载: Hugging Face 指定配置下载: - 使用配套 RKNN Runtime 和驱动;运行前用 rknn-smi -v 检查设备端版本。 - 源模型许可证:GNU General Public License v3.0,正文见 LICENSE,归属和转换说明见 NOTICE。 - 本仓库交付物是源模型的 RKNN 转换衍生物,仍受源模型许可和使用限制约束。 - 同时遵守 RKNN Toolkit、RKNN Runtime 相关许可;本仓库不包含运行库。

Read RKNNAI's full model card

1. 模型介绍

本仓库提供由 milesial/Pytorch-UNet 转换得到的 RKNN CNN 模型。

  • Model ID:RKNNAI/RK182X-CNN-Pytorch-UNet
  • 模型显示名称:RK182X-CNN-Pytorch-UNet
  • 发布版本:v1.1.0
  • 源模型:milesial/Pytorch-UNet
  • 模型类型:CNN
  • 芯片字段:RK182X
  • 具体支持芯片:RK1820、RK1828

可用模型

发布版本 配置目录 支持芯片 分辨率 量化方式 NPU 核数
v1.1.0 Pytorch-UNet-512x288-w8a8-8 RK1820、RK1828 512x288 w8a8 8

2. 文件说明

文件或目录 说明
LICENSE 源模型许可证
NOTICE 许可、使用要求及归属说明
<配置目录>/ 配套模型文件
子目录 README.md 当前配置说明
子目录 config.json 模型配置与文件清单
子目录 SHA256SUMS 当前目录交付文件的 SHA-256 校验值(不包含自身)

3. 模型下载

ModelScope 完整下载:

modelscope download --model RKNNAI/RK182X-CNN-Pytorch-UNet --revision v1.1.0 --local_dir ./RK182X-CNN-Pytorch-UNet

Hugging Face 完整下载:

hf download RKNNAI/RK182X-CNN-Pytorch-UNet --revision v1.1.0 --local-dir ./RK182X-CNN-Pytorch-UNet

ModelScope 指定配置下载:

from modelscope import snapshot_download

snapshot_download(
    "RKNNAI/RK182X-CNN-Pytorch-UNet",
    revision="v1.1.0",
    allow_patterns=["README.md", "LICENSE", "NOTICE", "Pytorch-UNet-512x288-w8a8-8/**"],
    local_dir="./RK182X-CNN-Pytorch-UNet",
)

Hugging Face 指定配置下载:

hf download RKNNAI/RK182X-CNN-Pytorch-UNet --revision v1.1.0 --include "README.md" "LICENSE" "NOTICE" "Pytorch-UNet-512x288-w8a8-8/**" --local-dir ./RK182X-CNN-Pytorch-UNet

4. SHA-256 校验

在配置目录执行:

cd ./RK182X-CNN-Pytorch-UNet/Pytorch-UNet-512x288-w8a8-8
sha256sum -c SHA256SUMS

所有条目显示 OK 后再部署。

5. 兼容性与限制

  • 支持芯片:RK1820、RK1828。
  • 使用配套 RKNN Runtime 和驱动;运行前用 rknn-smi -v 检查设备端版本。
  • 所选配置目录内的文件须配套使用。

6. 版权与许可证

  • 源模型许可证:GNU General Public License v3.0,正文见 LICENSE,归属和转换说明见 NOTICE。
  • 本仓库交付物是源模型的 RKNN 转换衍生物,仍受源模型许可和使用限制约束。
  • 同时遵守 RKNN Toolkit、RKNN Runtime 相关许可;本仓库不包含运行库。

Identity and Version

Repository
RKNNAI/RK182X-CNN-Pytorch-UNet
Publisher
RKNNAI
Task
Image segmentation
Modality
Image
Library
Not stated by the source
Parameters
Not stated by the source
Languages
cnn
Revision
82e8927f824c96d670e3a163d4fd3cf8638ddd9a
First published
2026-09-24
Last updated
2026-09-28

Files and Weights

10 files, 33.0 MB in total.

Configuration1 file · 2.3 KB
Documentation4 files · 40.3 KB
Other4 files · 33.0 MB
Repository1 file · 1.7 KB
Every file
FileTypeSizeSHA-256
Pytorch-UNet-512x288-w8a8-8/config.jsonConfiguration2.3 KB —
LICENSEDocumentation35.1 KB —
NOTICEDocumentation474 B —
Pytorch-UNet-512x288-w8a8-8/README.mdDocumentation1.9 KB —
README.mdDocumentation2.7 KB —
Pytorch-UNet-512x288-w8a8-8/Pytorch-UNet-model_report.htmlOther85.2 KB —
Pytorch-UNet-512x288-w8a8-8/Pytorch-UNet.rknnOther1.6 MB 7948fb5b0323
Pytorch-UNet-512x288-w8a8-8/Pytorch-UNet.weightOther31.3 MB 7c5cf9802c1b
Pytorch-UNet-512x288-w8a8-8/SHA256SUMSOther421 B —
.gitattributesRepository1.7 KB —

License and Download

License
gpl-3.0
Access
Open weights, no gate
Download from RKNNAI

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

Questions About RK182X-CNN-Pytorch-UNet

What license is RK182X-CNN-Pytorch-UNet released under?

gpl-3.0, as its publisher declares it. Read the license text before commercial use.

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