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RKNN Model Zoo

Description

RKNN Model Zoo is developed based on the RKNPU SDK toolchain and provides deployment examples for current mainstream algorithms. Include the process of exporting the RKNN model and using Python API and CAPI to infer the RKNN model.

• Support RK3562, RK3566, RK3568, RK3588 , RK3576 platforms.
• Limited support RV1103, RV1106
• Support RK1808, RV1109, RV1126 platforms.

Dependency library installation

RKNN Model Zoo relies on RKNN-Toolkit2 for model conversion. The Android compilation tool chain is required when compiling the Android demo, and the Linux compilation tool chain is required when compiling the Linux demo. For the installation of these dependencies, please refer to the Quick Start documentation at https://github.com/airockchip/rknn-toolkit2/tree/master/doc.

• Please note that the Android compilation tool chain recommends using version r18 or r19. Using other versions may encounter the problem of Cdemo compilation failure.

Model support

In addition to exporting the model from the corresponding respository, the models file are available on https://console.zbox.filez.com/l/8ufwtG (key: rknn).

Demo	Algorithm Category	Dtype support	Pretrain model
mobilenet	Classification	FP16/INT8	mobilenetv2-12.onnx
resnet	Classification	FP16/INT8	resnet50-v2-7.onnx
yolov5	Object detection	FP16/INT8	yolov5n.onnx yolov5s_relu.onnx yolov5s.onnx yolov5m.onnx
yolov6	Object detection	FP16/INT8	yolov6n.onnx yolov6s.onnx yolov6m.onnx
yolov7	Object detection	FP16/INT8	yolov7-tiny.onnx yolov7.onnx
yolov8	Object detection	FP16/INT8	yolov8n.onnx yolov8s.onnx yolov8m.onnx
yolox	Object detection	FP16/INT8	yolox_s.onnx yolox_m.onnx
ppyoloe	Object detection	FP16/INT8	ppyoloe_s.onnx ppyoloe_m.onnx
deeplabv3	Image segmentation	FP16/INT8	deeplab-v3-plus-mobilenet-v2.pb
yolov5-seg	Image segmentation	FP16/INT8	yolov5n-seg.onnx yolov5s-seg.onnx yolov5m-seg.onnx
yolov8-seg	Image segmentation	FP16/INT8	yolov8n-seg.onnx yolov8s-seg.onnx yolov8m-seg.onnx
ppseg	Image segmentation	FP16	pp_liteseg_cityscapes.onnx
RetinaFace	Face key points	INT8	RetinaFace_mobile320.onnx RetinaFace_resnet50_320.onnx
LPRNet	Car Plate Recognition	FP16/INT8	lprnet.onnx
PPOCR-Det	Text detection	FP16/INT8	ppocrv4_det.onnx
PPOCR-Rec	Text recognition	FP16	ppocrv4_rec.onnx
lite_transformer	Neural Machine Translation	FP16	lite-transformer-encoder-16.onnx lite-transformer-decoder-16.onnx

Model performance benchmark(FPS)

demo	model_name	inputs_shape	dtype	RK3566 RK3568	RK3562	RK3588 @single_core	RK3576 @single_core	RK3576 @single_core @sparse_weight	RV1109	RV1126	RK1808
mobilenet	mobilenetv2-12	[1, 3, 224, 224]	INT8	197.4	266.8	433.0	452.3	483.9	213.5	316.5	168.6
resnet	resnet50-v2-7	[1, 3, 224, 224]	INT8	40.6	54.5	108.6	97.4	129.9	24.5	36.4	37.0
yolov5	yolov5s_relu	[1, 3, 640, 640]	INT8	26.7	31.6	63.3	62.6	82.0	20.3	29.3	36.7
	yolov5n	[1, 3, 640, 640]	INT8	41.6	43.8	68.1	104.4	112.2	36.4	53.5	61.0
	yolov5s	[1, 3, 640, 640]	INT8	19.9	22.7	42.5	54.2	65.5	13.7	20.1	28.1
	yolov5m	[1, 3, 640, 640]	INT8	8.7	10.6	19.3	23.0	31.5	5.8	8.5	13.1
yolov6	yolov6n	[1, 3, 640, 640]	INT8	50.2	51.5	93.8	98.6	136.6	37.7	56.8	66.4
	yolov6s	[1, 3, 640, 640]	INT8	15.2	16.8	34.1	33.1	55.3	10.9	16.4	24.0
	yolov6m	[1, 3, 640, 640]	INT8	7.5	8.0	17.6	17.0	27.8	5.7	8.3	11.4
yolov7	yolov7-tiny	[1, 3, 640, 640]	INT8	29.9	34.9	69.7	70.9	91.8	15.6	22.5	37.2
	yolov7	[1, 3, 640, 640]	INT8	4.7	5.5	10.9	12.5	17.9	3.3	4.9	7.4
yolov8	yolov8n	[1, 3, 640, 640]	INT8	35.7	38.5	59.6	79.5	95.6	24.1	36.0	41.9
	yolov8s	[1, 3, 640, 640]	INT8	15.4	17.1	32.8	38.7	52.4	9.0	13.2	19.1
	yolov8m	[1, 3, 640, 640]	INT8	6.6	7.5	14.8	15.9	23.5	3.9	5.8	9.1
yolox	yolox_s	[1, 3, 640, 640]	INT8	15.5	17.7	32.9	36.4	46.7	10.6	15.7	22.9
	yolox_m	[1, 3, 640, 640]	INT8	6.7	8.1	14.8	16.5	23.2	4.7	6.8	10.5
ppyoloe	ppyoloe_s	[1, 3, 640, 640]	INT8	17.5	19.7	32.9	30.0	34.4	11.3	16.4	21.0
	ppyoloe_m	[1, 3, 640, 640]	INT8	7.9	8.3	16.2	12.9	14.8	5.2	7.7	9.4
deeplabv3	deeplab-v3-plus-mobilenet-v2	[1, 513, 513, 1]	INT8	10.7	20.7	34.4	38.1	42.5	10.3	13.1	4.4
yolov5_seg	yolov5n-seg	[1, 3, 640, 640]	INT8	33.9	36.3	58.0	82.4	92.2	28.7	41.9	49.6
	yolov5s-seg	[1, 3, 640, 640]	INT8	15.3	17.2	32.6	39.5	51.1	9.7	14.0	22.4
	yolov5m-seg	[1, 3, 640, 640]	INT8	6.8	8.1	15.2	17.2	25.1	4.7	6.9	10.7
yolov8_seg	yolov8n-seg	[1, 3, 640, 640]	INT8	29.1	30.7	49.1	64.5	78.0	18.6	27.8	32.7
	yolov8s-seg	[1, 3, 640, 640]	INT8	11.8	11.3	25.4	29.3	39.7	6.7	9.8	14.5
	yolov8m-seg	[1, 3, 640, 640]	INT8	5.2	6.1	11.6	12.1	18.1	3.1	4.6	6.8
ppseg	ppseg_lite_1024x512	[1, 3, 512, 512]	INT8	2.6	4.6	13.0	8.7	35.5	18.4	27.2	14.7
RetinaFace	RetinaFace_mobile320	[1, 3, 320, 320]	INT8	142.5	279.5	234.7	416.0	396.8	146.3	210.1	242.2
	RetinaFace_resnet50_320	[1, 3, 320, 320]	INT8	18.5	26.0	48.8	47.3	70.4	14.7	20.9	24.2
LPRNet	lprnet	[1, 3, 24, 94]	INT8	58.2	119.7	204.4	130.2	130.6	30.6	47.8	30.1
PPOCR-Det	ppocrv4_det	[1, 3, 480, 480]	INT8	24.4	27.5	43.0	46.1	47.0	11.1	16.2	9.1
PPOCR-Rec	ppocrv4_rec	[1, 3, 48, 320]	FP16	20.0	45.1	35.7	55	58.9	1.0	1.6	6.7
lite_transformer	lite-transformer-encoder-16	embedding-256, token-16	FP16	130.8	656.7	261.5	609.1	674.8	22.7	35.6	97.8
	lite-transformer-decoder-16	embedding-256, token-16	FP16	114.3	151.3	164.0	240	341.8	49.0	66.3	114.9

• This performance data are collected based on the maximum NPU frequency of each platform.
• This performance data calculate the time-consuming of model inference. Does not include the time-consuming of pre-processing and post-processing.
• RK3576 with sparse_weight referring to the performance when enabling the sparse weight for models
• Note: Models with sparse weight (via Kernel) should have improved performance, but may have accuracy drops depending on models.

Compile Demo

For Linux develop board:

./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

# Here is an example for compiling yolov5 demo for 64-bit Linux RK3566.
./build-linux.sh -t rk356x -a aarch64 -d yolov5

For Android development board:

# For Android develop boards, it's require to set path for Android NDK compilation tool path according to the user environment
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

# Here is an example for compiling yolov5 demo for 64-bit Android RK3566.
./build-android.sh -t rk356x -a arm64-v8a -d yolov5

Release Notes

Version	Description
2.0.0	Add new support for RK3576 for all demo. Full support for RK1808, RK1109, RK1126 platform.
1.6.0	New demo release, including object detection, image segmentation, OCR, car plate detection&recognition etc. Full support for RK3566, RK3568, RK3588, RK3562 platforms. Limited support for RV1103, RV1106 platforms.
1.5.0	Yolo detection demo release.

Environment dependencies

All demos in RKNN Model Zoo are verified based on the latest RKNPU SDK. If using a lower version for verification, the inference performance and inference results may be wrong.

Version	RKNPU2 SDK	RKNPU1 SDK
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 Resource

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

License

Apache License 2.0