OpenCV Zoo and Benchmark

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

Guidelines:

Install latest opencv-python:

python3 -m pip install opencv-python
# Or upgrade to latest version
python3 -m pip install --upgrade opencv-python

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

Hardware Setup:

Intel Core i7-12700K: 8 Performance-cores (3.60 GHz, turbo up to 4.90 GHz), 4 Efficient-cores (2.70 GHz, turbo up to 3.80 GHz), 20 threads.
Raspberry Pi 4B: Broadcom BCM2711 SoC with a Quad core Cortex-A72 (ARM v8) 64-bit @ 1.5 GHz.
Toybrick RV1126: Rockchip RV1126 SoC with a quard-core ARM Cortex-A7 CPU and a 2.0 TOPs NPU.
Khadas Edge 2: Rockchip RK3588S SoC with a CPU of 2.25 GHz Quad Core ARM Cortex-A76 + 1.8 GHz Quad Core Cortex-A55, and a 6 TOPS NPU.
Horizon Sunrise X3: an SoC from Horizon Robotics with a quad-core ARM Cortex-A53 1.2 GHz CPU and a 5 TOPS BPU (a.k.a NPU).
MAIX-III AXera-Pi: Axera AX620A SoC with a quad-core ARM Cortex-A7 CPU and a 3.6 TOPS @ int8 NPU.
StarFive VisionFive 2: StarFive JH7110 SoC with a RISC-V quad-core CPU, which can turbo up to 1.5GHz, and an GPU of model IMG BXE-4-32 MC1 from Imagination, which has a work freq up to 600MHz.
NVIDIA Jetson Nano B01: a Quad-core ARM A57 @ 1.43 GHz CPU, and a 128-core NVIDIA Maxwell GPU.
Khadas VIM3: Amlogic A311D SoC with a 2.2GHz Quad core ARM Cortex-A73 + 1.8GHz dual core Cortex-A53 ARM CPU, and a 5 TOPS NPU. Benchmarks are done using per-tensor quantized models. Follow this guide to build OpenCV with TIM-VX backend enabled.
Atlas 200 DK: Ascend 310 NPU with 22 TOPS @ INT8. Follow this guide to build OpenCV with CANN backend enabled.
Allwinner Nezha D1: Allwinner D1 SoC with a 1.0 GHz single-core RISC-V Xuantie C906 CPU with RVV 0.7.1 support. YuNet is tested 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 mean 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.