RKNN Model Zoo

简介

RKNN Model Zoo基于 RKNPU SDK 工具链开发, 提供了目前主流算法的部署例程. 例程包含导出RKNN模型, 使用 Python API, CAPI 推理 RKNN 模型的流程.

支持 RK3562, RK3566, RK3568, RK3588, RK3576 平台。
部分支持RV1103, RV1106
支持 RK1808, RV1109, RV1126 平台。

依赖库安装

RKNN Model Zoo依赖 RKNN-Toolkit2 进行模型转换, 编译安卓demo时需要安卓编译工具链, 编译Linux demo时需要Linux编译工具链。这些依赖的安装请参考 https://github.com/airockchip/rknn-toolkit2/tree/master/doc 的 Quick Start 文档.

请注意, 安卓编译工具链建议使用 r18 或 r19 版本. 使用其他版本可能会遇到 Cdemo 编译失败的问题.

模型支持说明

以下demo除了从对应的仓库导出模型, 也可从网盘 https://console.zbox.filez.com/l/8ufwtG (提取码: rknn) 下载模型文件.

Category	Name	Dtype	Model Download Link	Support platform
图像分类	mobilenet	FP16/INT8	mobilenetv2-12.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RV1103\|RV1106 RK1808\|RK3399PRO RV1109\|RV1126
图像分类	resnet	FP16/INT8	resnet50-v2-7.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RK1808\|RK3399PRO RV1109\|RV1126
物体检测	yolov5	FP16/INT8	./yolov5s_relu.onnx ./yolov5n.onnx ./yolov5s.onnx ./yolov5m.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RV1103\|RV1106 RK1808\|RK3399PRO RV1109\|RV1126
物体检测	yolov6	FP16/INT8	./yolov6n.onnx ./yolov6s.onnx ./yolov6m.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RK1808\|RK3399PRO RV1109\|RV1126
物体检测	yolov7	FP16/INT8	./yolov7-tiny.onnx ./yolov7.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RK1808\|RK3399PRO RV1109\|RV1126
物体检测	yolov8	FP16/INT8	./yolov8n.onnx ./yolov8s.onnx ./yolov8m.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RK1808\|RK3399PRO RV1109\|RV1126
物体检测	yolov8_obb	INT8	./yolov8n-obb.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RK1808\|RK3399PRO RV1109\|RV1126
物体检测	yolov10	FP16/INT8	./yolov10n.onnx ./yolov10s.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RV1103\|RV1106 RK1808\|RK3399PRO RV1109\|RV1126
物体检测	yolox	FP16/INT8	./yolox_s.onnx ./yolox_m.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RK1808\|RK3399PRO RV1109\|RV1126
物体检测	ppyoloe	FP16/INT8	./ppyoloe_s.onnx ./ppyoloe_m.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RK1808\|RK3399PRO RV1109\|RV1126
物体检测	yolo_world	FP16/INT8	./yolo_world_v2s.onnx ./clip_text.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576
人体关键点	yolov8_pose	INT8	./yolov8n-pose.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576
图像分割	deeplabv3	FP16/INT8	./deeplab-v3-plus-mobilenet-v2.pb	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RK1808\|RK3399PRO RV1109\|RV1126
图像分割	yolov5_seg	FP16/INT8	./yolov5n-seg.onnx ./yolov5s-seg.onnx ./yolov5m-seg.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RK1808\|RK3399PRO RV1109\|RV1126
图像分割	yolov8_seg	FP16/INT8	./yolov8n-seg.onnx ./yolov8s-seg.onnx ./yolov8m-seg.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RK1808\|RK3399PRO RV1109\|RV1126
图像分割	ppseg	FP16/INT8	pp_liteseg_cityscapes.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RK1808\|RK3399PRO RV1109\|RV1126
图像分割	mobilesam	FP16	mobilesam_encoder_tiny.onnx mobilesam_decoder.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576
人脸关键点	RetinaFace	INT8	RetinaFace_mobile320.onnx RetinaFace_resnet50_320.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RK1808\|RK3399PRO RV1109\|RV1126
车牌识别	LPRNet	FP16/INT8	./lprnet.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RK1808\|RK3399PRO RV1109\|RV1126
文字检测	PPOCR-Det	FP16/INT8	../ppocrv4_det.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RK1808\|RK3399PRO RV1109\|RV1126
文字识别	PPOCR-Rec	FP16	../ppocrv4_rec.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RK1808\|RK3399PRO RV1109\|RV1126
自然语言翻译	lite_transformer	FP16	lite-transformer-encoder-16.onnx lite-transformer-decoder-16.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576 RK1808\|RK3399PRO RV1109\|RV1126
图文匹配	clip	FP16	./clip_images.onnx ./clip_text.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576
语音识别	wav2vec2	FP16	wav2vec2_base_960h_20s.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576
语音识别	whisper	FP16	whisper_encoder_base_20s.onnx whisper_decoder_base_20s.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576
语音分类	yamnet	FP16	yamnet_3s.onnx	RK3566\|RK3568\|RK3588\|RK3562\|RK3576

Model performance benchmark(FPS)

demo	model_name	inputs_shape	dtype	RK3566 RK3568	RK3562	RK3588 @single_core	RK3576 @single_core	RV1109	RV1126	RK1808
mobilenet	mobilenetv2-12	[1, 3, 224, 224]	INT8	184.3	295.5	455.4	467.2	212.9	322.3	170.3
resnet	resnet50-v2-7	[1, 3, 224, 224]	INT8	38.5	56.1	110.4	99.8	24.4	36.2	37.1
yolov5	yolov5s_relu	[1, 3, 640, 640]	INT8	25.6	33.7	66.2	65.4	20.2	29.2	37.2
	yolov5n	[1, 3, 640, 640]	INT8	39.8	48.9	82.9	113.0	36.3	53.2	61.2
	yolov5s	[1, 3, 640, 640]	INT8	19.5	23.9	48.5	57.7	13.6	20.0	28.2
	yolov5m	[1, 3, 640, 640]	INT8	8.7	11.0	20.9	23.8	5.8	8.5	13.3
yolov6	yolov6n	[1, 3, 640, 640]	INT8	49.0	57.4	106.8	110.3	37.8	56.8	66.8
	yolov6s	[1, 3, 640, 640]	INT8	15.3	17.5	36.4	34.8	10.8	16.3	24.1
	yolov6m	[1, 3, 640, 640]	INT8	7.3	8.6	17.6	17.5	5.6	8.3	11.5
yolov7	yolov7-tiny	[1, 3, 640, 640]	INT8	28.0	37.1	72.9	75.1	15.4	22.4	37.2
	yolov7	[1, 3, 640, 640]	INT8	4.7	5.9	11.5	13.0	3.3	4.8	7.4
yolov8	yolov8n	[1, 3, 640, 640]	INT8	34.4	41.5	74.0	90.2	24.0	35.4	42.3
	yolov8s	[1, 3, 640, 640]	INT8	15.2	18.3	35.2	41.0	8.9	13.1	19.1
	yolov8m	[1, 3, 640, 640]	INT8	6.6	8.3	16.2	16.7	3.9	5.8	9.1
yolov8_obb	yolov8n-obb	[1, 3, 640, 640]	INT8	34.3	41.7	74.1	91.0	25.1	37.3	42.8
yolov10	yolov10n	[1, 3, 640, 640]	INT8	21.1	31.7	56.1	74.5	/	/	/
	yolov10s	[1, 3, 640, 640]	INT8	10.4	16.1	32.0	38.2	/	/	/
yolox	yolox_s	[1, 3, 640, 640]	INT8	15.3	18.4	37.1	42.0	10.6	15.7	23.0
	yolox_m	[1, 3, 640, 640]	INT8	6.7	8.3	16.0	17.6	4.6	6.8	10.7
ppyoloe	ppyoloe_s	[1, 3, 640, 640]	INT8	7.5	20.4	31.9	41.5	11.2	16.4	21.1
	ppyoloe_m	[1, 3, 640, 640]	INT8	4.2	9.3	15.3	17.8	5.2	7.7	9.4
yolo_world	yolo_world_v2s	[1, 3, 640, 640]	INT8	7.5	9.6	22.2	22.1	/	/	/
	clip_text	[1, 20]	FP16	28.33	64.6	92.4	61.7	/	/	/
yolov8_pose	yolov8n-pose	[1, 3, 640, 640]	INT8	23.0	31.6	56.0	67.2	/	/	/
deeplabv3	deeplab-v3-plus-mobilenet-v2	[1, 513, 513, 1]	INT8	10.0	21.6	34.3	39.6	10.1	13.0	4.4
yolov5_seg	yolov5n-seg	[1, 3, 640, 640]	INT8	32.6	40.0	69.5	89.2	28.6	42.2	49.6
	yolov5s-seg	[1, 3, 640, 640]	INT8	15.1	18.3	36.9	41.8	9.6	14.0	22.5
	yolov5m-seg	[1, 3, 640, 640]	INT8	6.8	8.5	16.4	18.0	4.7	6.8	10.8
yolov8_seg	yolov8n-seg	[1, 3, 640, 640]	INT8	28.1	33.7	61.0	71.5	18.6	27.6	32.9
	yolov8s-seg	[1, 3, 640, 640]	INT8	11.8	14.2	29.0	30.9	6.6	9.8	14.6
	yolov8m-seg	[1, 3, 640, 640]	INT8	5.2	6.4	12.6	12.8	3.1	4.6	6.9
ppseg	ppseg_lite_1024x512	[1, 3, 512, 512]	INT8	4.4	12.0	29.9	29.2	18.4	27.1	20.9
mobilesam	mobilesam_encoder_tiny	[1, 3, 448, 448]	FP16	0.9	6.7	9.8	12.0	/	/	/
	mobilesam_decoder	[1, 1, 112, 112]	FP16	23.3	71.3	116.3	108.9	/	/	/
RetinaFace	RetinaFace_mobile320	[1, 3, 320, 320]	INT8	157.0	316.5	230.3	476.6	144.8	212.5	198.5
	RetinaFace_resnet50_320	[1, 3, 320, 320]	INT8	18.8	26.8	49.4	56.8	14.6	20.8	24.6
LPRNet	lprnet	[1, 3, 24, 94]	FP16	154.7	320.0	432.8	492.5	30.6(INT8)	47.6(INT8)	30.1(INT8)
PPOCR-Det	ppocrv4_det	[1, 3, 480, 480]	INT8	22.6	28.9	50.8	64.8	11.0	16.1	14.2
PPOCR-Rec	ppocrv4_rec	[1, 3, 48, 320]	FP16	19.7	54.1	74.3	96.6	1.0	1.6	6.7
lite_transformer	lite-transformer-encoder-16	embedding-256, token-16	FP16	331.6	728.8	878.5	786.4	22.7	35.4	98.3
	lite-transformer-decoder-16	embedding-256, token-16	FP16	142.4	252.2	346.3	270.3	48.0	65.8	109.9
clip	clip_images	[1, 3, 224, 224]	FP16	2.3	3.4	6.5	6.7	/	/	/
	clip_text	[1, 20]	FP16	28.33	64.6	92.5	61.5	/	/	/
wav2vec2	wav2vec2_base_960h_20s	20s audio	FP16	RTF 0.861	RTF 0.333	RTF 0.131	RTF 0.073	/	/	/
whisper	encoder+decoder+NPU-outside process	20s audio	FP16	RTF 1.253	RTF 0.417	RTF 0.219	RTF 0.216	/	/	/
yamnet	yamnet_3s	3s audio	FP16	RTF 0.013	RTF 0.008	RTF 0.004	RTF 0.005	/	/	/

该性能数据基于各平台的最大NPU频率进行测试
该性能数据指模型推理的耗时, 不包含前后处理的耗时
/表示当前版本暂不支持

Demo编译说明

对于 Linux 系统的开发板:

./build-linux.sh -t <target> -a <arch> -d <build_demo_name> [-b <build_type>] [-m]
    -t : target (rk356x/rk3588/rk3576/rv1106/rk1808/rv1126)
    -a : arch (aarch64/armhf)
    -d : demo name
    -b : build_type(Debug/Release)
    -m : enable address sanitizer, build_type need set to Debug
Note: 'rk356x' represents rk3562/rk3566/rk3568, 'rv1106' represents rv1103/rv1106, 'rv1126' represents rv1109/rv1126

# 以编译64位Linux RK3566的yolov5 demo为例:
./build-linux.sh -t rk356x -a aarch64 -d yolov5

对于 Android 系统的开发板:

# 对于 Android 系统的开发板, 首先需要根据实际情况, 设置安卓NDK编译工具的路径
export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b
./build-android.sh -t <target> -a <arch> -d <build_demo_name> [-b <build_type>] [-m]
    -t : target (rk356x/rk3588/rk3576)
    -a : arch (arm64-v8a/armeabi-v7a)
    -d : demo name
    -b : build_type (Debug/Release)
    -m : enable address sanitizer, build_type need set to Debug

# 以编译64位Android RK3566的yolov5 demo为例:
./build-android.sh -t rk356x -a arm64-v8a -d yolov5

版本说明

版本	说明
2.2.0	添加新例程 wav2vec, mobilesam. 更新部分模型的导出说明
2.1.0	新例程添加, 包含 yolov8_pose, yolov8_obb, yolov10, yolo_world, clip, whisper, yamnet 部分模型暂不支持 `RK1808`, `RV1109`, `RV1126` 平台, 将在下个版本添加支持
2.0.0	新增所有示例`RK3576`平台的支持支持`RK1808`, `RV1109`, `RV1126`平台
1.6.0	提供目标检测、图像分割、OCR、车牌识别等多个例程支持`RK3562`, `RK3566`, `RK3568`, `RK3588`平台部分支持`RV1103`, `RV1106`平台
1.5.0	提供Yolo检测模型的demo

环境依赖

RKNN Model Zoo 的例程基于当前最新的 RKNPU SDK 进行验证。若使用低版本的 RKNPU SDK 进行验证, 推理性能、推理结果可能会有差异。

版本	RKNPU2 SDK	RKNPU1 SDK
2.2.0	>=2.2.0	>=1.7.5
2.1.0	>=2.1.0	>=1.7.5
2.0.0	>=2.0.0	>=1.7.5
1.6.0	>=1.6.0	-
1.5.0	>=1.5.0	>=1.7.3

RKNPU相关资料

RKNPU2 SDK: https://github.com/airockchip/rknn-toolkit2
RKNPU1 SDK: https://github.com/airockchip/rknn-toolkit

许可证

Apache License 2.0

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

README_CN.md

README_CN.md

RKNN Model Zoo

简介

依赖库安装

模型支持说明

Model performance benchmark(FPS)

Demo编译说明

版本说明

环境依赖

RKNPU相关资料

许可证

Files

README_CN.md

Latest commit

History

README_CN.md

File metadata and controls

RKNN Model Zoo

简介

依赖库安装

模型支持说明

Model performance benchmark(FPS)

Demo编译说明

版本说明

环境依赖

RKNPU相关资料

许可证