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

• To run benchmarks on your hardware settings, please refer to benchmark/README.

*: Models are quantized in per-channel mode, which run slower than per-tensor quantized models on NPU.

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.
• KV3-NPU: Khadas VIM3, 5TOPS Performance. Benchmarks are done using quantized models. You will need to compile OpenCV with TIM-VX following this guide to run benchmarks. The test results use the per-tensor quantization model by default.
• 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.

Some examples are listed below. You can find more in the directory of each model!

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