OpenCV Zoo and Benchmark
A zoo for models tuned for OpenCV DNN with benchmarks on different platforms.
- 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
- To run benchmarks on your hardware settings, please refer to benchmark/README.
Models & Benchmark Results
|Model||Input Size||INTEL-CPU (ms)||RPI-CPU (ms)||JETSON-GPU (ms)||KV3-NPU (ms)||D1-CPU (ms)|
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.
KV3-NPU: Khadas VIM3, 5TOPS Performance. Benchmarks are done using quantized models. TIM-VX backend and NPU target support for OpenCV is under reivew. You will need to compile OpenCV with TIM-VX following this guide to run benchmarks.
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.
- 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.