/opencv_zoo

Model Zoo For OpenCV DNN and Benchmarks.

Primary LanguagePython

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
  • 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)
YuNet 160x120 1.45 6.22 12.18 4.04 86.69
SFace 112x112 8.65 99.20 24.88 46.25 ---
DB-IC15 640x480 142.91 2835.91 208.41 --- ---
DB-TD500 640x480 142.91 2841.71 210.51 --- ---
CRNN-EN 100x32 50.21 234.32 196.15 125.30 ---
CRNN-CN 100x32 73.52 322.16 239.76 166.79 ---
PP-ResNet 224x224 56.05 602.58 98.64 75.45 ---
MobileNet-V1 224x224 9.04 92.25 33.18 145.66 (per-channel) ---
MobileNet-V2 224x224 8.86 74.03 31.92 146.31 (per-channel) ---
PP-HumanSeg 192x192 19.92 105.32 67.97 74.77 ---
WeChatQRCode 100x100 7.04 37.68 --- --- ---
DaSiamRPN 1280x720 36.15 705.48 76.82 --- ---
YoutuReID 128x256 35.81 521.98 90.07 44.61 ---

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.

License

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