SAVRN
Search Contact SAVRN

Open-weight model · Image classification

RK182X-VIT-vit-base-patch16-224

by RKNNAI RKNNAI/RK182X-VIT-vit-base-patch16-224

RK182X-VIT-vit-base-patch16-224 is an open-weight model for image classification from RKNNAI, released under Apache License 2.0. Its published files total 66.5 MB.

本仓库提供由 google/vit-base-patch16-224 转换的 RKNN 模型。 - Model ID:RKNNAI/RK182X-VIT-vit-base-patch16-224 - 模型显示名称:RK182X-VIT-vit-base-patch16-224 - 源模型:google/vit-base-patch16-224 - 模型类型:VIT 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:…

Parameters—
Context—
Weights66.5 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—

Model Card

By RKNNAI, published under apache-2.0, revision 76b837f0a435.

本仓库提供由 google/vit-base-patch16-224 转换的 RKNN 模型。 - Model ID:RKNNAI/RK182X-VIT-vit-base-patch16-224 - 模型显示名称:RK182X-VIT-vit-base-patch16-224 - 源模型:google/vit-base-patch16-224 - 模型类型:VIT 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:Apache License 2.0,正文见 LICENSE,归属与转换修改说明见 NOTICE。 - 同时遵守 RKNN Toolkit、RKNN Runtime 相关许可;本包不包含运行库。

Read RKNNAI's full model card

1. 模型介绍

本仓库提供由 google/vit-base-patch16-224 转换的 RKNN 模型。

  • Model ID:RKNNAI/RK182X-VIT-vit-base-patch16-224
  • 模型显示名称:RK182X-VIT-vit-base-patch16-224
  • 发布版本:v1.1.0
  • 源模型:google/vit-base-patch16-224
  • 模型类型:VIT
  • 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 vit-base-patch16-224-224x224-w4a16-8 RK1820、RK1828 224x224 w4a16 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-VIT-vit-base-patch16-224 --revision v1.1.0 --local_dir ./RK182X-VIT-vit-base-patch16-224

Hugging Face 完整下载:

hf download RKNNAI/RK182X-VIT-vit-base-patch16-224 --revision v1.1.0 --local-dir ./RK182X-VIT-vit-base-patch16-224

ModelScope 指定配置下载:

from modelscope import snapshot_download

snapshot_download(
    "RKNNAI/RK182X-VIT-vit-base-patch16-224",
    revision="v1.1.0",
    allow_patterns=["README.md", "LICENSE", "NOTICE", "vit-base-patch16-224-224x224-w4a16-8/**"],
    local_dir="./RK182X-VIT-vit-base-patch16-224",
)

Hugging Face 指定配置下载:

hf download RKNNAI/RK182X-VIT-vit-base-patch16-224 --revision v1.1.0 --include "README.md" "LICENSE" "NOTICE" "vit-base-patch16-224-224x224-w4a16-8/**" --local-dir ./RK182X-VIT-vit-base-patch16-224

4. SHA-256 校验

在配置目录执行:

cd ./RK182X-VIT-vit-base-patch16-224/vit-base-patch16-224-224x224-w4a16-8
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:Apache License 2.0,正文见 LICENSE,归属与转换修改说明见 NOTICE。
  • 本仓库交付物为源权重的转换衍生物,仍受源模型许可和使用限制约束;不是上游未经修改的原始权重。
  • 同时遵守 RKNN Toolkit、RKNN Runtime 相关许可;本包不包含运行库。

Identity and Version

Repository
RKNNAI/RK182X-VIT-vit-base-patch16-224
Publisher
RKNNAI
Task
Image classification
Modality
Image
Library
Not stated by the source
Parameters
Not stated by the source
Languages
vit
Revision
76b837f0a435469a721dcb918bbcd36aa2fdde37
First published
2026-09-24
Last updated
2026-09-28

Files and Weights

10 files, 66.5 MB in total.

Configuration1 file · 2.2 KB
Documentation4 files · 16.8 KB
Other4 files · 66.5 MB
Repository1 file · 1.7 KB
Every file
FileTypeSizeSHA-256
vit-base-patch16-224-224x224-w4a16-8/config.jsonConfiguration2.2 KB —
LICENSEDocumentation11.4 KB —
NOTICEDocumentation456 B —
README.mdDocumentation3.0 KB —
vit-base-patch16-224-224x224-w4a16-8/README.mdDocumentation2.0 KB —
vit-base-patch16-224-224x224-w4a16-8/SHA256SUMSOther409 B —
vit-base-patch16-224-224x224-w4a16-8/ViT-B-16-model_report.htmlOther85.0 KB —
vit-base-patch16-224-224x224-w4a16-8/ViT-B-16.rknnOther2.6 MB b82619abadf5
vit-base-patch16-224-224x224-w4a16-8/ViT-B-16.weightOther63.8 MB 29bb835ec323
.gitattributesRepository1.7 KB —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download from RKNNAI

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

Built From

Questions About RK182X-VIT-vit-base-patch16-224

Can I use RK182X-VIT-vit-base-patch16-224 commercially?

Yes. RK182X-VIT-vit-base-patch16-224 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

Similar Models

Model · Image classification

swinv2-tiny-patch4-window16-256

Microsoft

Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository. Disclaimer: The team releasing Swin Transformer v2 did not write a model card for this model so this model card has been written by the Hugging Face team. The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose…

Open weights apache-2.0 transformers

Model · Image classification

AI-image-detector

Matthew Maybe

NOTE: Unless you are trying to detect imagery generated using older models such as VQGAN+CLIP, please use the updated version of this detector instead. This model is a proof-of-concept demonstration of using a ViT model to predict whether an artistic image was generated using AI. It was created in October 2022, and as such, the training data did not include any samples generated by Midjourney 5, SDXL, or DALLE-3. It still may be able to correctly identify samples from these more recent models due to being trained on outputs of their predecessors. Furthermore the intended scope of this tool is artistic images; that is to say, it is not a deepfake photo detector, and general computer imagery…

Open weights cc-by-4.0 transformers

Measured on device (edge-compat): Raspberry Pi 5 · LiteRT 2.2.0.dev20260804 · CPU/XNNPACK, 4 threads · 34.1 ms p50 (2026-08-31); browser · Chromium 151 (2026-08-11). Record: https://github.com/john-rocky/edge-compat/blob/main/cards/plantnet-300k-resnet18/CARD.md On-device fine-grained plant species identification — 1081 species — running fully on the LiteRT CompiledModel GPU delegate (no CPU fallback). A PlantNet-300K (NeurIPS 2021) ResNet18. ~16 ms/frame on a Pixel 8a. (mean [0.485,0.456,0.406], std [0.229,0.224,0.225]; center-crop then resize 224). Labels: class index i maps to the i-th species when the PlantNet-300K species-id strings are sorted (torchvision ImageFolder order); names…

Open weights apache-2.0 litert

Model · Image classification

traffic-sign-adverse-weather

Yy

Official model checkpoints for the solution in the Traffic Sign Recognition under Adverse Weather Competition. See classes.txt for the 25 traffic sign classes. For inference scripts, training code, and in-depth engineering retrospective, visit the GitHub Repository.

Open weights mit timm

Model · Image classification

tinyvit-5m-int8-imagenet

Core Epoch

TinyViT-5M (timm/tinyvit5m224.distin22kftin1k, Apache-2.0) quantized to INT8 with Kenosis — 128-image calibration, no retraining. 80.53% top-1 from a 9.2 MB single file, on ONNX Runtime or OpenVINO, CPU or GPU, no accelerator required. ImageNet-1K validation, 49,872 images (disjoint from the 128 calibration images). Measured on a CPU with AVX-VNNI; on CPUs without VNNI this model's INT8 top-1 sits ~0.9 below FP32 rather than 0.34. Input 1x3x224x224, RGB, /255, ImageNet mean/std. Output logits [1,1000], sorted-synset order. runclassify.py / evalimagenet.py reproduce the demo and table. tinyvit5m224int8kenosis.onnx (9,228,567 B) — SHA-256…

Open weights apache-2.0 onnx