OpenCV Zoo and Benchmark

A zoo for models tuned for OpenCV DNN with benchmarks on different platforms.

Guidelines:

Clone this repo to download all models and demo scripts:

# Install git-lfs from https://git-lfs.github.com/
git clone https://github.com/opencv/opencv_zoo && cd opencv_zoo
git lfs install
git lfs pull

Models & Benchmark Results

Model	Input Size	INTEL-CPU	RPI-CPU	JETSON-GPU	D1-CPU
YuNet	160x120	1.45	6.22	12.18	86.69
SFace	112x112	8.65	99.20	24.88	---
DB-IC15	640x480	142.91	2835.91	208.41	---
DB-TD500	640x480	142.91	2841.71	210.51	---
CRNN-EN	100x32	50.21	234.32	196.15	---
CRNN-CN	100x32	73.52	322.16	239.76	---
PP-ResNet	224x224	56.05	602.58	98.64	---
PP-HumanSeg	192x192	19.92	105.32	67.97	---
WeChatQRCode	100x100	7.04	37.68	---	---
DaSiamRPN	1280x720	36.15	705.48	76.82	---
YoutuReID	128x256	35.81	521.98	90.07	---

Hardware Setup:

INTEL-CPU: Intel Core i7-5930K @ 3.50GHz, 6 cores, 12 threads.
RPI-CPU: Raspberry Pi 4B, Broadcom BCM2711, Quad core Cortex-A72 (ARM v8) 64-bit SoC @ 1.5GHz.
JETSON-GPU: NVIDIA Jetson Nano B01, 128-core NVIDIA Maxwell GPU.
D1-CPU: Allwinner D1, Xuantie C906 CPU (RISC-V, RVV 0.7.1) @ 1.0GHz, 1 core. YuNet is supported for now. Visit here for more details.

Important Notes:

The data under each column of hardware setups on the above table represents the elapsed time of an inference (preprocess, forward and postprocess).
The time data is the median of 10 runs after some warmup runs. Different metrics may be applied to some specific models.
Batch size is 1 for all benchmark results.
--- represents the model is not availble to run on the device.
View benchmark/config for more details on benchmarking different models.

OpenCV Zoo is licensed under the Apache 2.0 license. Please refer to licenses of different models.