RKNNAI/RK182X-CNN-ResNet50V2

ResNet50V2

中文

1. Model Introduction

ResNet50V2 is an image classification model. This repository provides its RKNN deployment configuration with 1,000 output classes.

Field	Value
Model ID	RKNNAI/RK182X-CNN-ResNet50V2
Display Name	RK182X-CNN-ResNet50V2
Source Model	onnx/models
Model Type	CNN

Available Models

Configuration	Supported Chips	RKNN Runtime Version	Quantization	NPU Cores	Resolution
ResNet50V2-224x224-w8a8-1	RK1820, RK1828	v1.1.0	model: w8a8	model: 1	model: 224x224

2. Files

Path	Description
README.md	English documentation
README_CN.md	Chinese documentation
LICENSE	Source model license
NOTICE	Source attribution
ResNet50V2-224x224-w8a8-1/	Deployment configuration

3. Download

ModelScope

Full repository:

modelscope download --model RKNNAI/RK182X-CNN-ResNet50V2 --revision v1.1.0 --local_dir ./RK182X-CNN-ResNet50V2

Selected configuration and root documents:

from modelscope import snapshot_download

snapshot_download(
    "RKNNAI/RK182X-CNN-ResNet50V2",
    revision="v1.1.0",
    allow_patterns=["README.md", "README_CN.md", "LICENSE", "NOTICE", "ResNet50V2-224x224-w8a8-1/**"],
    local_dir="./RK182X-CNN-ResNet50V2",
)

Hugging Face

Full repository:

hf download RKNNAI/RK182X-CNN-ResNet50V2 --revision v1.1.0 --local-dir ./RK182X-CNN-ResNet50V2

Selected configuration and root documents:

hf download RKNNAI/RK182X-CNN-ResNet50V2 --revision v1.1.0 --local-dir ./RK182X-CNN-ResNet50V2 --include README.md README_CN.md LICENSE NOTICE 'ResNet50V2-224x224-w8a8-1/**'

4. SHA-256 Verification

From the directory where you ran the download command, enter the configuration directory and verify:

cd ./RK182X-CNN-ResNet50V2/ResNet50V2-224x224-w8a8-1
sha256sum -c SHA256SUMS

All entries must report OK before deployment.

5. Compatibility and Limitations

• Supported chips are listed per configuration above.
• Use the matching files from the same configuration.

6. Copyright and License

The source model is from onnx/models and is licensed under the Apache License 2.0. See LICENSE and NOTICE. These artifacts are RKNN-converted derivatives; runtime libraries are not included.