OpenCV Zoo

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

Guidelines:

To clone this repo, please install git-lfs, run git lfs install and use git lfs clone https://github.com/opencv/opencv_zoo.
To run benchmark on your hardware settings, please refer to benchmark/README.
Understand model filename: <topic>_<model_name>_<dataset>_<arch>_<upload_time>
- <topic>: research topics, such as face detection etc.
- <model_name>: exact model names.
- <dataset>: (Optional) the dataset that the model is trained with.
- <arch>: (Optional) the backbone architecture of the model.
- <upload_time>: the time when the model is uploaded, meaning the latest version of this model unless specified.

Models & Benchmarks

Hardware Setup:

CPU x86_64: INTEL CPU i7-5930K @ 3.50GHz, 6 cores, 12 threads.
CPU ARM: Raspberry 4B, BCM2711B0 @ 1.5GHz (Cortex A-72), 4 cores, 4 threads.
GPU CUDA: NVIDIA Jetson Nano B01, 128-core Maxwell, Quad-core ARM A57 @ 1.43 GHz.

Important Notes:

The time data that shown on the following table presents the time elapsed from preprocess (resize is excluded), to a forward pass of a network, and postprocess to get final results.
The time data that shown on the following table is the median of 10 runs. Different metrics may be applied to some specific models.
Batch size is 1 for all benchmark results.
View benchmark/config for more details on benchmarking different models.

Model	Input Size	CPU x86_64 (ms)	CPU ARM (ms)	GPU CUDA (ms)
YuNet	160x120	1.45	6.22	12.18
DB-IC15	640x480	142.91	2835.91	208.41
DB-TD500	640x480	142.91	2841.71	210.51
CRNN	100x32	50.21	234.32	196.15
SFace	112x112	8.65	99.20	24.88
PP-ResNet	224x224	56.05	602.58	98.64
PP-HumanSeg	192x192	19.92	105.32	67.97

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